From 6d2d648126b112ca1455c6e1f2d344a4d8d27c1d Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 2 Nov 2020 11:06:36 +0300 Subject: add tests for show trait module --- .../tests/wqflask/show_trait/test_show_trait.py | 267 +++++++++++++++++++++ 1 file changed, 267 insertions(+) create mode 100644 wqflask/tests/wqflask/show_trait/test_show_trait.py diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py new file mode 100644 index 00000000..37eae06e --- /dev/null +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -0,0 +1,267 @@ +"""test for wqflask/show_trait/test_show_trait.py""" + + +import unittest +from unittest import mock +from wqflask import app +from wqflask.show_trait.show_trait import check_if_attr_exists +from wqflask.show_trait.show_trait import requests + +from wqflask.show_trait.show_trait import get_ncbi_summary + +from wqflask.show_trait.show_trait import has_num_cases + +from wqflask.show_trait.show_trait import get_table_widths + +from wqflask.show_trait.show_trait import get_categorical_variables +from wqflask.show_trait.show_trait import get_trait_units + +from wqflask.show_trait.show_trait import get_nearest_marker + +from wqflask.show_trait.show_trait import get_genotype_scales + +class TraitObject: + def __init__(self, obj): + for key, value in obj.items(): + setattr(self, key, value) + + +class TestTraits(unittest.TestCase): + def setUp(self): + self.app_context = app.app_context() + self.app_context.push() + + def tearDown(self): + self.app_context.pop() + + def test_check_if_attr_exists_truthy(self): + """"test if attributes exists with true return""" + trait_obj = TraitObject({"id_type": "id"}) + trait_obj2 = TraitObject({"sample_name": ['samp1']}) + results = check_if_attr_exists(trait_obj, "id_type") + result2 = check_if_attr_exists(trait_obj2, "sample_name") + self.assertIsInstance(trait_obj, TraitObject) + self.assertEqual(results, True) + self.assertEqual(result2, True) + + def test_check_if_attr_exists_empty_attr(self): + """test if attributes exists with empty attributes""" + trait_obj = TraitObject({"sample": ""}) + trait_obj2 = TraitObject({"group": None}) + result = check_if_attr_exists(trait_obj, "sample") + result2 = check_if_attr_exists(trait_obj, "group") + self.assertEqual(result, False) + self.assertEqual(result2, False) + + def test_check_if_attr_exists_falsey(self): + """check if attribute exists with empty attributes""" + trait_obj = TraitObject({}) + results = check_if_attr_exists(trait_obj, "any") + self.assertEqual(results, False) + + @mock.patch("wqflask.show_trait.show_trait.requests.get") + @mock.patch("wqflask.show_trait.show_trait.check_if_attr_exists") + def test_get_ncbi_summary_request_success(self, mock_exists, mock_get): + """test for getting ncbi summary with + successful request""" + trait = TraitObject({"geneid": "id"}) + mock_exists.return_value = True + content_json_string = """{ + "result":{ + "id":{ + "summary":"this is a summary of the geneid" + } + } + } + """ + + get_return_obj = TraitObject({"content": content_json_string}) + + mock_get.return_value = get_return_obj + + results = get_ncbi_summary(trait) + mock_exists.assert_called_once() + mock_get.assert_called_once_with(f"http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=gene&id={trait.geneid}&retmode=json") + + self.assertEqual(results, "this is a summary of the geneid") + + @mock.patch("wqflask.show_trait.show_trait.requests.get") + @mock.patch("wqflask.show_trait.show_trait.check_if_attr_exists") + def test_get_ncbi_summary_request_fail(self, mock_exists, mock_get_fail): + """test for getting ncbi summary with request fail""" + trait = TraitObject({"geneid": "id"}) + mock_exists.return_value = True + mock_get_fail.side_effect = Exception("an error occurred") + content_json_string = """{ + "result":{ + "id":{ + "summary":"this is a summary of the geneid" + } + } + } + """ + + results = get_ncbi_summary(trait) + self.assertEqual(results, None) + + def test_hash_num_cases_is_probeset(self): + """test for hash num_cases with dataset.type set to Probeset""" + create_dataset = TraitObject({"type": "ProbeSet"}) + create_trait = TraitObject({"dataset": create_dataset}) + + self.assertEqual(has_num_cases(create_trait), False) + + def test_hash_num_cases_no_probeset(self): + """test for hash num cases with dataset.type not Probeset""" + create_dataset = TraitObject({"type": "Temp"}) + + construct_data = { + "nm1": TraitObject({"num_cases": False}), + "nm2": TraitObject({"num_cases": True}), + "nm3": TraitObject({"num_cases": False}) + } + construct_data2 = { + "nm1": TraitObject({"num_cases": False}), + "nm2": TraitObject({"num_cases": False}), + "nm3": TraitObject({"num_cases": False}) + + + } + + create_trait = TraitObject( + {"dataset": create_dataset, "data": construct_data}) + create_trait2 = TraitObject( + {"dataset": create_dataset, "data": construct_data2}) + + results = has_num_cases(create_trait) + + self.assertEqual(has_num_cases(create_trait), True) + self.assertEqual(has_num_cases(create_trait2), False) + + def test_get_table_widths(self): + """test for getting table widths""" + sample_groups = [TraitObject({'se_exists': True, "attributes": ["attr1", "attr2", "attr3"]} + ), TraitObject( + {"se_exists": False, "attributes": ["at1", "at2"] + })] + + results_with_numcase = get_table_widths(sample_groups, True) + result_no_numcase = get_table_widths(sample_groups, False) + + results_one_sample = get_table_widths( + [TraitObject({"se_exists": True, "attributes": []})], True) + expected_with_numcase = (450, "750px") + expected_no_numcase = (450, "670px") + expected_one_sample = (250, "540px") + + self.assertEqual(results_with_numcase, expected_with_numcase) + self.assertEqual(result_no_numcase, expected_no_numcase) + self.assertEqual(results_one_sample, + expected_one_sample) + + def test_get_categorical_variables_no_sample_attributes(self): + """test for getting categorical variable names with no samples""" + trait = TraitObject({}) + sample_list = TraitObject({"se_exists": True, "attributes": []}) + + self.assertEqual(get_categorical_variables(trait, sample_list), []) + + def test_get_categorical_variables_with_sample_attributes(self): + pass + + def test_get_trait_units(self): + """test for gettting trait units""" + trait = TraitObject( + {"description_fmt": "[this is a description] another test [N/A]"}) + + trait_no_unit_type = TraitObject({"description_fmt": ""}) + + results = get_trait_units(trait) + results_no_unit = get_trait_units(trait_no_unit_type) + self.assertEqual(results, "this is a descriptionN/A") + self.assertEqual(results_no_unit, "Value") + + @mock.patch("wqflask.show_trait.show_trait.g") + def test_get_nearest_marker(self, mock_db): + """test for getting nearest marker with non-empty db""" + + mock_db.db.execute.return_value.fetchall.return_value = [ + ["Geno1", "Geno2"], ["Geno3"]] + + trait = TraitObject({"locus_chr": "test_chr", "locus_mb": "test_mb"}) + + group_name = TraitObject({"name": "group_name"}) + + this_db = TraitObject({"group": group_name}) + + results_with_item_db = get_nearest_marker(trait, this_db) + + called_with_value = """SELECT Geno.Name + FROM Geno, GenoXRef, GenoFreeze + WHERE Geno.Chr = 'test_chr' AND + GenoXRef.GenoId = Geno.Id AND + GenoFreeze.Id = GenoXRef.GenoFreezeId AND + GenoFreeze.Name = 'group_nameGeno' + ORDER BY ABS( Geno.Mb - test_mb) LIMIT 1""" + + mock_db.db.execute.assert_called_with(called_with_value) + + self.assertEqual(results_with_item_db, "Geno1") + + @mock.patch("wqflask.show_trait.show_trait.g") + def test_get_nearest_marker_empty_db(self, mock_db): + """test for getting nearest marker with empty db""" + mock_db.db.execute.return_value.fetchall.return_value = [] + + trait = TraitObject({"locus_chr": "test_chr", "locus_mb": "test_mb"}) + group_name = TraitObject({"name": "group_name"}) + this_db = TraitObject({"group": group_name}) + + results_empty_db = get_nearest_marker(trait, this_db) + mock_db.db.execute.assert_called_once() + self.assertEqual(results_empty_db, "") + + + @mock.patch("wqflask.show_trait.show_trait.get_scales_from_genofile") + def test_get_genotype_scales_with_genofile_is_list(self,mock_get_scales): + """test for getting genotype scales with genofile as list """ + #where genofile is instance of list + genofiles_list = [{"filename":"file1","location":"~/data/files/f1"},{"filename":"file2","location":"~/data/files/f2"},{"filename":"file3","location":"~/data/files/f3"}] + + mock_get_scales.side_effect = [[["morgan", "cM"]],[["morgan", "cM"]],[["physic", "Mb"]]] + + results = get_genotype_scales(genofiles_list) + + expected_results = { + "~/data/files/f1":[["morgan","cM"]], + "~/data/files/f2":[["morgan","cM"]], + "~/data/files/f3":[["physic","Mb"]] + } + + multiple_calls = [mock.call('~/data/files/f1'),mock.call('~/data/files/f2'), + mock.call('~/data/files/f3')] + + + mock_get_scales.assert_has_calls(multiple_calls) + self.assertEqual(results,expected_results) + + + @mock.patch("wqflask.show_trait.show_trait.get_scales_from_genofile") + def test_genotype_scales_with_genofile_other(self,mock_get_scales): + """test for getting genotype scales with genofile as a strig""" + file_location = "~/another_file_location" + mock_get_scales.return_value = [["physic","Mb"]] + + expected_results = {f"{file_location}":[["physic","Mb"]]} + + + self.assertEqual(get_genotype_scales(file_location),expected_results) + mock_get_scales.assert_called_once_with(file_location) + + + + + + + + -- cgit v1.2.3 From 62996d6b521379576992172fdee99468cb0260c9 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 2 Nov 2020 13:35:59 +0300 Subject: add function docstring --- wqflask/tests/wqflask/show_trait/test_show_trait.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index 37eae06e..1b5d2265 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -45,7 +45,7 @@ class TestTraits(unittest.TestCase): self.assertEqual(result2, True) def test_check_if_attr_exists_empty_attr(self): - """test if attributes exists with empty attributes""" + """test if attributes exists with false return""" trait_obj = TraitObject({"sample": ""}) trait_obj2 = TraitObject({"group": None}) result = check_if_attr_exists(trait_obj, "sample") @@ -248,7 +248,7 @@ class TestTraits(unittest.TestCase): @mock.patch("wqflask.show_trait.show_trait.get_scales_from_genofile") def test_genotype_scales_with_genofile_other(self,mock_get_scales): - """test for getting genotype scales with genofile as a strig""" + """test for getting genotype scales with genofile as a string""" file_location = "~/another_file_location" mock_get_scales.return_value = [["physic","Mb"]] -- cgit v1.2.3 From 2883f17236081bc11faf4c4202393998bde0924d Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 2 Nov 2020 13:50:25 +0300 Subject: add better formatting --- .../tests/wqflask/show_trait/test_show_trait.py | 47 +++++++++------------- 1 file changed, 20 insertions(+), 27 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index 1b5d2265..408797b9 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -20,6 +20,7 @@ from wqflask.show_trait.show_trait import get_nearest_marker from wqflask.show_trait.show_trait import get_genotype_scales + class TraitObject: def __init__(self, obj): for key, value in obj.items(): @@ -141,7 +142,7 @@ class TestTraits(unittest.TestCase): def test_get_table_widths(self): """test for getting table widths""" sample_groups = [TraitObject({'se_exists': True, "attributes": ["attr1", "attr2", "attr3"]} - ), TraitObject( + ), TraitObject( {"se_exists": False, "attributes": ["at1", "at2"] })] @@ -221,47 +222,39 @@ class TestTraits(unittest.TestCase): mock_db.db.execute.assert_called_once() self.assertEqual(results_empty_db, "") - @mock.patch("wqflask.show_trait.show_trait.get_scales_from_genofile") - def test_get_genotype_scales_with_genofile_is_list(self,mock_get_scales): + def test_get_genotype_scales_with_genofile_is_list(self, mock_get_scales): """test for getting genotype scales with genofile as list """ - #where genofile is instance of list - genofiles_list = [{"filename":"file1","location":"~/data/files/f1"},{"filename":"file2","location":"~/data/files/f2"},{"filename":"file3","location":"~/data/files/f3"}] + # where genofile is instance of list + genofiles_list = [{"filename": "file1", "location": "~/data/files/f1"}, + {"filename": "file2", "location": "~/data/files/f2"}, + {"filename": "file3", "location": "~/data/files/f3"}] - mock_get_scales.side_effect = [[["morgan", "cM"]],[["morgan", "cM"]],[["physic", "Mb"]]] + mock_get_scales.side_effect = [[["morgan", "cM"]], + [["morgan", "cM"]], + [["physic", "Mb"]]] results = get_genotype_scales(genofiles_list) expected_results = { - "~/data/files/f1":[["morgan","cM"]], - "~/data/files/f2":[["morgan","cM"]], - "~/data/files/f3":[["physic","Mb"]] + "~/data/files/f1": [["morgan", "cM"]], + "~/data/files/f2": [["morgan", "cM"]], + "~/data/files/f3": [["physic", "Mb"]] } - multiple_calls = [mock.call('~/data/files/f1'),mock.call('~/data/files/f2'), - mock.call('~/data/files/f3')] - + multiple_calls = [mock.call('~/data/files/f1'), mock.call('~/data/files/f2'), + mock.call('~/data/files/f3')] mock_get_scales.assert_has_calls(multiple_calls) - self.assertEqual(results,expected_results) - + self.assertEqual(results, expected_results) @mock.patch("wqflask.show_trait.show_trait.get_scales_from_genofile") - def test_genotype_scales_with_genofile_other(self,mock_get_scales): + def test_genotype_scales_with_genofile_other(self, mock_get_scales): """test for getting genotype scales with genofile as a string""" file_location = "~/another_file_location" - mock_get_scales.return_value = [["physic","Mb"]] + mock_get_scales.return_value = [["physic", "Mb"]] - expected_results = {f"{file_location}":[["physic","Mb"]]} + expected_results = {f"{file_location}": [["physic", "Mb"]]} - - self.assertEqual(get_genotype_scales(file_location),expected_results) + self.assertEqual(get_genotype_scales(file_location), expected_results) mock_get_scales.assert_called_once_with(file_location) - - - - - - - - -- cgit v1.2.3 From 2864bf07f919dacc72b5df590b140881bbe8dc0c Mon Sep 17 00:00:00 2001 From: zsloan Date: Mon, 2 Nov 2020 13:16:59 -0600 Subject: Changed correlation page logic to skip over traits that share fewer than 6 samples + traits that user doesn't have permission to access * wqflask/wqflask/correlation/show_corr_results.py - Moved the num_overlap check so that it never attempts to calculate the correlation if it's too low + checked if trait_object is None in the main loop (since it would be returned as None if the user doesn't have permissions --- wqflask/wqflask/correlation/show_corr_results.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py index e710bacd..4c2b64ba 100644 --- a/wqflask/wqflask/correlation/show_corr_results.py +++ b/wqflask/wqflask/correlation/show_corr_results.py @@ -184,6 +184,8 @@ class CorrelationResults(object): for _trait_counter, trait in enumerate(list(self.correlation_data.keys())[:self.return_number]): trait_object = create_trait(dataset=self.target_dataset, name=trait, get_qtl_info=True, get_sample_info=False) + if not trait_object: + continue if self.target_dataset.type == "ProbeSet" or self.target_dataset.type == "Geno": #ZS: Convert trait chromosome to an int for the location range option @@ -434,15 +436,15 @@ class CorrelationResults(object): self.this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(self.this_trait_vals, target_vals) - #ZS: 2015 could add biweight correlation, see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465711/ - if self.corr_method == 'bicor': - sample_r, sample_p = do_bicor(self.this_trait_vals, target_vals) - elif self.corr_method == 'pearson': - sample_r, sample_p = scipy.stats.pearsonr(self.this_trait_vals, target_vals) - else: - sample_r, sample_p = scipy.stats.spearmanr(self.this_trait_vals, target_vals) - if num_overlap > 5: + #ZS: 2015 could add biweight correlation, see http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465711/ + if self.corr_method == 'bicor': + sample_r, sample_p = do_bicor(self.this_trait_vals, target_vals) + elif self.corr_method == 'pearson': + sample_r, sample_p = scipy.stats.pearsonr(self.this_trait_vals, target_vals) + else: + sample_r, sample_p = scipy.stats.spearmanr(self.this_trait_vals, target_vals) + if numpy.isnan(sample_r): pass else: @@ -635,3 +637,4 @@ def get_header_fields(data_type, corr_method): 'Sample p(r)'] return header_fields + -- cgit v1.2.3 From 04e981ba202a6bd9d969c05855a33040928d15d0 Mon Sep 17 00:00:00 2001 From: zsloan Date: Mon, 2 Nov 2020 13:21:40 -0600 Subject: Created function for encoding the column value as bytes when fetching from the JSON-formatted string pulled from Redis (since this was needed after the Python 3 switchover) * wqflask/utility/redis_tools.py - Created function load_json_from_redis that encodes the key (column_value) when fetching a value from the JSON pulled from Redis --- wqflask/utility/redis_tools.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/wqflask/utility/redis_tools.py b/wqflask/utility/redis_tools.py index d855a7fa..236cc755 100644 --- a/wqflask/utility/redis_tools.py +++ b/wqflask/utility/redis_tools.py @@ -25,6 +25,10 @@ def is_redis_available(): return True +def load_json_from_redis(item_list, column_value): + return json.loads(item_list[str.encode(column_value)]) + + def get_user_id(column_name, column_value): user_list = Redis.hgetall("users") key_list = [] @@ -46,7 +50,7 @@ def get_user_by_unique_column(column_name, column_value): if column_name in user_ob and user_ob[column_name] == column_value: item_details = user_ob else: - item_details = json.loads(user_list[column_value]) + item_details = load_json_from_redis(user_list, column_value) return item_details @@ -70,7 +74,7 @@ def get_users_like_unique_column(column_name, column_value): if column_value in user_ob[column_name]: matched_users.append(user_ob) else: - matched_users.append(json.loads(user_list[column_value])) + matched_users.append(load_json_from_redis(user_list, column_value)) return matched_users @@ -199,7 +203,7 @@ def get_groups_like_unique_column(column_name, column_value): if column_value in group_info[column_name]: matched_groups.append(group_info) else: - matched_groups.append(json.loads(group_list[column_value])) + matched_groups.append(load_json_from_redis(group_list, column_value)) return matched_groups -- cgit v1.2.3 From 251113ee34819afdc3e2abc63d59a01bd6a560cb Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Tue, 3 Nov 2020 10:32:36 +0300 Subject: use assertTrue and assertFalse in while testing boolean --- .../tests/wqflask/show_trait/test_show_trait.py | 38 ++++++++-------------- 1 file changed, 13 insertions(+), 25 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index 408797b9..a2ef71dd 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -1,24 +1,12 @@ """test for wqflask/show_trait/test_show_trait.py""" - import unittest from unittest import mock from wqflask import app -from wqflask.show_trait.show_trait import check_if_attr_exists -from wqflask.show_trait.show_trait import requests - -from wqflask.show_trait.show_trait import get_ncbi_summary - -from wqflask.show_trait.show_trait import has_num_cases - -from wqflask.show_trait.show_trait import get_table_widths - -from wqflask.show_trait.show_trait import get_categorical_variables -from wqflask.show_trait.show_trait import get_trait_units - -from wqflask.show_trait.show_trait import get_nearest_marker - -from wqflask.show_trait.show_trait import get_genotype_scales +from wqflask.show_trait.show_trait import (check_if_attr_exists, get_ncbi_summary, + has_num_cases, get_table_widths, + get_categorical_variables, + get_trait_units, get_nearest_marker, get_genotype_scales, requests) class TraitObject: @@ -42,8 +30,8 @@ class TestTraits(unittest.TestCase): results = check_if_attr_exists(trait_obj, "id_type") result2 = check_if_attr_exists(trait_obj2, "sample_name") self.assertIsInstance(trait_obj, TraitObject) - self.assertEqual(results, True) - self.assertEqual(result2, True) + self.assertTrue(results, True) + self.assertTrue(result2, True) def test_check_if_attr_exists_empty_attr(self): """test if attributes exists with false return""" @@ -51,14 +39,14 @@ class TestTraits(unittest.TestCase): trait_obj2 = TraitObject({"group": None}) result = check_if_attr_exists(trait_obj, "sample") result2 = check_if_attr_exists(trait_obj, "group") - self.assertEqual(result, False) - self.assertEqual(result2, False) + self.assertFalse(result, False) + self.assertFalse(result2, False) def test_check_if_attr_exists_falsey(self): """check if attribute exists with empty attributes""" trait_obj = TraitObject({}) results = check_if_attr_exists(trait_obj, "any") - self.assertEqual(results, False) + self.assertFalse(results, False) @mock.patch("wqflask.show_trait.show_trait.requests.get") @mock.patch("wqflask.show_trait.show_trait.check_if_attr_exists") @@ -110,7 +98,7 @@ class TestTraits(unittest.TestCase): create_dataset = TraitObject({"type": "ProbeSet"}) create_trait = TraitObject({"dataset": create_dataset}) - self.assertEqual(has_num_cases(create_trait), False) + self.assertFalse(has_num_cases(create_trait), False) def test_hash_num_cases_no_probeset(self): """test for hash num cases with dataset.type not Probeset""" @@ -136,8 +124,8 @@ class TestTraits(unittest.TestCase): results = has_num_cases(create_trait) - self.assertEqual(has_num_cases(create_trait), True) - self.assertEqual(has_num_cases(create_trait2), False) + self.assertTrue(has_num_cases(create_trait), True) + self.assertFalse(has_num_cases(create_trait2), False) def test_get_table_widths(self): """test for getting table widths""" @@ -171,7 +159,7 @@ class TestTraits(unittest.TestCase): pass def test_get_trait_units(self): - """test for gettting trait units""" + """test for getting trait units""" trait = TraitObject( {"description_fmt": "[this is a description] another test [N/A]"}) -- cgit v1.2.3 From ef0c9bb30523b32decc163dcb62c125e7ba5a076 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Tue, 3 Nov 2020 16:50:23 +0300 Subject: switch from assertTrue/False to assertIs to make the tests tighter --- wqflask/tests/wqflask/show_trait/test_show_trait.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index a2ef71dd..f666912b 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -1,5 +1,4 @@ """test for wqflask/show_trait/test_show_trait.py""" - import unittest from unittest import mock from wqflask import app @@ -30,8 +29,9 @@ class TestTraits(unittest.TestCase): results = check_if_attr_exists(trait_obj, "id_type") result2 = check_if_attr_exists(trait_obj2, "sample_name") self.assertIsInstance(trait_obj, TraitObject) - self.assertTrue(results, True) - self.assertTrue(result2, True) + self.assertIs(results, True) + self.assertIs(result2, True) + def test_check_if_attr_exists_empty_attr(self): """test if attributes exists with false return""" @@ -39,14 +39,14 @@ class TestTraits(unittest.TestCase): trait_obj2 = TraitObject({"group": None}) result = check_if_attr_exists(trait_obj, "sample") result2 = check_if_attr_exists(trait_obj, "group") - self.assertFalse(result, False) - self.assertFalse(result2, False) + self.assertIs(result, False) + self.assertIs(result2, False) def test_check_if_attr_exists_falsey(self): """check if attribute exists with empty attributes""" trait_obj = TraitObject({}) results = check_if_attr_exists(trait_obj, "any") - self.assertFalse(results, False) + self.assertIs(results, False) @mock.patch("wqflask.show_trait.show_trait.requests.get") @mock.patch("wqflask.show_trait.show_trait.check_if_attr_exists") @@ -124,8 +124,8 @@ class TestTraits(unittest.TestCase): results = has_num_cases(create_trait) - self.assertTrue(has_num_cases(create_trait), True) - self.assertFalse(has_num_cases(create_trait2), False) + self.assertIs(has_num_cases(create_trait), True) + self.assertIs(has_num_cases(create_trait2), False) def test_get_table_widths(self): """test for getting table widths""" -- cgit v1.2.3 From ca26d98ff906163580adbc1e9c00122f9da245b7 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 16:54:45 +0300 Subject: Remove stale file * jquery.qtip.css: Delete it. --- jquery.qtip.css | 623 -------------------------------------------------------- 1 file changed, 623 deletions(-) delete mode 100644 jquery.qtip.css diff --git a/jquery.qtip.css b/jquery.qtip.css deleted file mode 100644 index c2dcb306..00000000 --- a/jquery.qtip.css +++ /dev/null @@ -1,623 +0,0 @@ -/* - * qTip2 - Pretty powerful tooltips - v2.2.0 - * http://qtip2.com - * - * Copyright (c) 2013 Craig Michael Thompson - * Released under the MIT, GPL licenses - * http://jquery.org/license - * - * Date: Thu Nov 21 2013 08:34 GMT+0000 - * Plugins: tips modal viewport svg imagemap ie6 - * Styles: basic css3 - */ -.qtip{ - position: absolute; - left: -28000px; - top: -28000px; - display: none; - - max-width: 280px; - min-width: 50px; - - font-size: 10.5px; - line-height: 12px; - - direction: ltr; - - box-shadow: none; - padding: 0; -} - - .qtip-content{ - position: relative; - padding: 5px 9px; - overflow: hidden; - - text-align: left; - word-wrap: break-word; - } - - .qtip-titlebar{ - position: relative; - padding: 5px 35px 5px 10px; - overflow: hidden; - - border-width: 0 0 1px; - font-weight: bold; - } - - .qtip-titlebar + .qtip-content{ border-top-width: 0 !important; } - - /* Default close button class */ - .qtip-close{ - position: absolute; - right: -9px; top: -9px; - - cursor: pointer; - outline: medium none; - - border-width: 1px; - border-style: solid; - border-color: transparent; - } - - .qtip-titlebar .qtip-close{ - right: 4px; top: 50%; - margin-top: -9px; - } - - * html .qtip-titlebar .qtip-close{ top: 16px; } /* IE fix */ - - .qtip-titlebar .ui-icon, - .qtip-icon .ui-icon{ - display: block; - text-indent: -1000em; - direction: ltr; - } - - .qtip-icon, .qtip-icon .ui-icon{ - -moz-border-radius: 3px; - -webkit-border-radius: 3px; - border-radius: 3px; - text-decoration: none; - } - - .qtip-icon .ui-icon{ - width: 18px; - height: 14px; - - line-height: 14px; - text-align: center; - text-indent: 0; - font: normal bold 10px/13px Tahoma,sans-serif; - - color: inherit; - background: transparent none no-repeat -100em -100em; - } - -/* Applied to 'focused' tooltips e.g. most recently displayed/interacted with */ -.qtip-focus{} - -/* Applied on hover of tooltips i.e. added/removed on mouseenter/mouseleave respectively */ -.qtip-hover{} - -/* Default tooltip style */ -.qtip-default{ - border-width: 1px; - border-style: solid; - border-color: #F1D031; - - background-color: #FFFFA3; - color: #555; -} - - .qtip-default .qtip-titlebar{ - background-color: #FFEF93; - } - - .qtip-default .qtip-icon{ - border-color: #CCC; - background: #F1F1F1; - color: #777; - } - - .qtip-default .qtip-titlebar .qtip-close{ - border-color: #AAA; - color: #111; - } - - - -/*! Light tooltip style */ -.qtip-light{ - background-color: white; - border-color: #E2E2E2; - color: #454545; -} - - .qtip-light .qtip-titlebar{ - background-color: #f1f1f1; - } - - -/*! Dark tooltip style */ -.qtip-dark{ - background-color: #505050; - border-color: #303030; - color: #f3f3f3; -} - - .qtip-dark .qtip-titlebar{ - background-color: #404040; - } - - .qtip-dark .qtip-icon{ - border-color: #444; - } - - .qtip-dark .qtip-titlebar .ui-state-hover{ - border-color: #303030; - } - - -/*! Cream tooltip style */ -.qtip-cream{ - background-color: #FBF7AA; - border-color: #F9E98E; - color: #A27D35; -} - - .qtip-cream .qtip-titlebar{ - background-color: #F0DE7D; - } - - .qtip-cream .qtip-close .qtip-icon{ - background-position: -82px 0; - } - - -/*! Red tooltip style */ -.qtip-red{ - background-color: #F78B83; - border-color: #D95252; - color: #912323; -} - - .qtip-red .qtip-titlebar{ - background-color: #F06D65; - } - - .qtip-red .qtip-close .qtip-icon{ - background-position: -102px 0; - } - - .qtip-red .qtip-icon{ - border-color: #D95252; - } - - .qtip-red .qtip-titlebar .ui-state-hover{ - border-color: #D95252; - } - - -/*! Green tooltip style */ -.qtip-green{ - background-color: #CAED9E; - border-color: #90D93F; - color: #3F6219; -} - - .qtip-green .qtip-titlebar{ - background-color: #B0DE78; - } - - .qtip-green .qtip-close .qtip-icon{ - background-position: -42px 0; - } - - -/*! Blue tooltip style */ -.qtip-blue{ - background-color: #E5F6FE; - border-color: #ADD9ED; - color: #5E99BD; -} - - .qtip-blue .qtip-titlebar{ - background-color: #D0E9F5; - } - - .qtip-blue .qtip-close .qtip-icon{ - background-position: -2px 0; - } - - - -.qtip-shadow{ - -webkit-box-shadow: 1px 1px 3px 1px rgba(0, 0, 0, 0.15); - -moz-box-shadow: 1px 1px 3px 1px rgba(0, 0, 0, 0.15); - box-shadow: 1px 1px 3px 1px rgba(0, 0, 0, 0.15); -} - -/* Add rounded corners to your tooltips in: FF3+, Chrome 2+, Opera 10.6+, IE9+, Safari 2+ */ -.qtip-rounded, -.qtip-tipsy, -.qtip-bootstrap{ - -moz-border-radius: 5px; - -webkit-border-radius: 5px; - border-radius: 5px; -} - -.qtip-rounded .qtip-titlebar{ - -moz-border-radius: 4px 4px 0 0; - -webkit-border-radius: 4px 4px 0 0; - border-radius: 4px 4px 0 0; -} - -/* Youtube tooltip style */ -.qtip-youtube{ - -moz-border-radius: 2px; - -webkit-border-radius: 2px; - border-radius: 2px; - - -webkit-box-shadow: 0 0 3px #333; - -moz-box-shadow: 0 0 3px #333; - box-shadow: 0 0 3px #333; - - color: white; - border-width: 0; - - background: #4A4A4A; - background-image: -webkit-gradient(linear,left top,left bottom,color-stop(0,#4A4A4A),color-stop(100%,black)); - background-image: -webkit-linear-gradient(top,#4A4A4A 0,black 100%); - background-image: -moz-linear-gradient(top,#4A4A4A 0,black 100%); - background-image: -ms-linear-gradient(top,#4A4A4A 0,black 100%); - background-image: -o-linear-gradient(top,#4A4A4A 0,black 100%); -} - - .qtip-youtube .qtip-titlebar{ - background-color: #4A4A4A; - background-color: rgba(0,0,0,0); - } - - .qtip-youtube .qtip-content{ - padding: .75em; - font: 12px arial,sans-serif; - - filter: progid:DXImageTransform.Microsoft.Gradient(GradientType=0,StartColorStr=#4a4a4a,EndColorStr=#000000); - -ms-filter: "progid:DXImageTransform.Microsoft.Gradient(GradientType=0,StartColorStr=#4a4a4a,EndColorStr=#000000);"; - } - - .qtip-youtube .qtip-icon{ - border-color: #222; - } - - .qtip-youtube .qtip-titlebar .ui-state-hover{ - border-color: #303030; - } - - -/* jQuery TOOLS Tooltip style */ -.qtip-jtools{ - background: #232323; - background: rgba(0, 0, 0, 0.7); - background-image: -webkit-gradient(linear, left top, left bottom, from(#717171), to(#232323)); - background-image: -moz-linear-gradient(top, #717171, #232323); - background-image: -webkit-linear-gradient(top, #717171, #232323); - background-image: -ms-linear-gradient(top, #717171, #232323); - background-image: -o-linear-gradient(top, #717171, #232323); - - border: 2px solid #ddd; - border: 2px solid rgba(241,241,241,1); - - -moz-border-radius: 2px; - -webkit-border-radius: 2px; - border-radius: 2px; - - -webkit-box-shadow: 0 0 12px #333; - -moz-box-shadow: 0 0 12px #333; - box-shadow: 0 0 12px #333; -} - - /* IE Specific */ - .qtip-jtools .qtip-titlebar{ - background-color: transparent; - filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=#717171,endColorstr=#4A4A4A); - -ms-filter: "progid:DXImageTransform.Microsoft.gradient(startColorstr=#717171,endColorstr=#4A4A4A)"; - } - .qtip-jtools .qtip-content{ - filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=#4A4A4A,endColorstr=#232323); - -ms-filter: "progid:DXImageTransform.Microsoft.gradient(startColorstr=#4A4A4A,endColorstr=#232323)"; - } - - .qtip-jtools .qtip-titlebar, - .qtip-jtools .qtip-content{ - background: transparent; - color: white; - border: 0 dashed transparent; - } - - .qtip-jtools .qtip-icon{ - border-color: #555; - } - - .qtip-jtools .qtip-titlebar .ui-state-hover{ - border-color: #333; - } - - -/* Cluetip style */ -.qtip-cluetip{ - -webkit-box-shadow: 4px 4px 5px rgba(0, 0, 0, 0.4); - -moz-box-shadow: 4px 4px 5px rgba(0, 0, 0, 0.4); - box-shadow: 4px 4px 5px rgba(0, 0, 0, 0.4); - - background-color: #D9D9C2; - color: #111; - border: 0 dashed transparent; -} - - .qtip-cluetip .qtip-titlebar{ - background-color: #87876A; - color: white; - border: 0 dashed transparent; - } - - .qtip-cluetip .qtip-icon{ - border-color: #808064; - } - - .qtip-cluetip .qtip-titlebar .ui-state-hover{ - border-color: #696952; - color: #696952; - } - - -/* Tipsy style */ -.qtip-tipsy{ - background: black; - background: rgba(0, 0, 0, .87); - - color: white; - border: 0 solid transparent; - - font-size: 11px; - font-family: 'Lucida Grande', sans-serif; - font-weight: bold; - line-height: 16px; - text-shadow: 0 1px black; -} - - .qtip-tipsy .qtip-titlebar{ - padding: 6px 35px 0 10px; - background-color: transparent; - } - - .qtip-tipsy .qtip-content{ - padding: 6px 10px; - } - - .qtip-tipsy .qtip-icon{ - border-color: #222; - text-shadow: none; - } - - .qtip-tipsy .qtip-titlebar .ui-state-hover{ - border-color: #303030; - } - - -/* Tipped style */ -.qtip-tipped{ - border: 3px solid #959FA9; - - -moz-border-radius: 3px; - -webkit-border-radius: 3px; - border-radius: 3px; - - background-color: #F9F9F9; - color: #454545; - - font-weight: normal; - font-family: serif; -} - - .qtip-tipped .qtip-titlebar{ - border-bottom-width: 0; - - color: white; - background: #3A79B8; - background-image: -webkit-gradient(linear, left top, left bottom, from(#3A79B8), to(#2E629D)); - background-image: -webkit-linear-gradient(top, #3A79B8, #2E629D); - background-image: -moz-linear-gradient(top, #3A79B8, #2E629D); - background-image: -ms-linear-gradient(top, #3A79B8, #2E629D); - background-image: -o-linear-gradient(top, #3A79B8, #2E629D); - filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=#3A79B8,endColorstr=#2E629D); - -ms-filter: "progid:DXImageTransform.Microsoft.gradient(startColorstr=#3A79B8,endColorstr=#2E629D)"; - } - - .qtip-tipped .qtip-icon{ - border: 2px solid #285589; - background: #285589; - } - - .qtip-tipped .qtip-icon .ui-icon{ - background-color: #FBFBFB; - color: #555; - } - - -/** - * Twitter Bootstrap style. - * - * Tested with IE 8, IE 9, Chrome 18, Firefox 9, Opera 11. - * Does not work with IE 7. - */ -.qtip-bootstrap{ - /** Taken from Bootstrap body */ - font-size: 14px; - line-height: 20px; - color: #333333; - - /** Taken from Bootstrap .popover */ - padding: 1px; - background-color: #ffffff; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.2); - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; - -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -webkit-background-clip: padding-box; - -moz-background-clip: padding; - background-clip: padding-box; -} - - .qtip-bootstrap .qtip-titlebar{ - /** Taken from Bootstrap .popover-title */ - padding: 8px 14px; - margin: 0; - font-size: 14px; - font-weight: normal; - line-height: 18px; - background-color: #f7f7f7; - border-bottom: 1px solid #ebebeb; - -webkit-border-radius: 5px 5px 0 0; - -moz-border-radius: 5px 5px 0 0; - border-radius: 5px 5px 0 0; - } - - .qtip-bootstrap .qtip-titlebar .qtip-close{ - /** - * Overrides qTip2: - * .qtip-titlebar .qtip-close{ - * [...] - * right: 4px; - * top: 50%; - * [...] - * border-style: solid; - * } - */ - right: 11px; - top: 45%; - border-style: none; - } - - .qtip-bootstrap .qtip-content{ - /** Taken from Bootstrap .popover-content */ - padding: 9px 14px; - } - - .qtip-bootstrap .qtip-icon{ - /** - * Overrides qTip2: - * .qtip-default .qtip-icon { - * border-color: #CCC; - * background: #F1F1F1; - * color: #777; - * } - */ - background: transparent; - } - - .qtip-bootstrap .qtip-icon .ui-icon{ - /** - * Overrides qTip2: - * .qtip-icon .ui-icon{ - * width: 18px; - * height: 14px; - * } - */ - width: auto; - height: auto; - - /* Taken from Bootstrap .close */ - float: right; - font-size: 20px; - font-weight: bold; - line-height: 18px; - color: #000000; - text-shadow: 0 1px 0 #ffffff; - opacity: 0.2; - filter: alpha(opacity=20); - } - - .qtip-bootstrap .qtip-icon .ui-icon:hover{ - /* Taken from Bootstrap .close:hover */ - color: #000000; - text-decoration: none; - cursor: pointer; - opacity: 0.4; - filter: alpha(opacity=40); - } - - -/* IE9 fix - removes all filters */ -.qtip:not(.ie9haxors) div.qtip-content, -.qtip:not(.ie9haxors) div.qtip-titlebar{ - filter: none; - -ms-filter: none; -} - - - -.qtip .qtip-tip{ - margin: 0 auto; - overflow: hidden; - z-index: 10; - -} - - /* Opera bug #357 - Incorrect tip position - https://github.com/Craga89/qTip2/issues/367 */ - x:-o-prefocus, .qtip .qtip-tip{ - visibility: hidden; - } - - .qtip .qtip-tip, - .qtip .qtip-tip .qtip-vml, - .qtip .qtip-tip canvas{ - position: absolute; - - color: #123456; - background: transparent; - border: 0 dashed transparent; - } - - .qtip .qtip-tip canvas{ top: 0; left: 0; } - - .qtip .qtip-tip .qtip-vml{ - behavior: url(#default#VML); - display: inline-block; - visibility: visible; - } - -#qtip-overlay{ - position: fixed; - left: 0; top: 0; - width: 100%; height: 100%; -} - - /* Applied to modals with show.modal.blur set to true */ - #qtip-overlay.blurs{ cursor: pointer; } - - /* Change opacity of overlay here */ - #qtip-overlay div{ - position: absolute; - left: 0; top: 0; - width: 100%; height: 100%; - - background-color: black; - - opacity: 0.7; - filter:alpha(opacity=70); - -ms-filter:"progid:DXImageTransform.Microsoft.Alpha(Opacity=70)"; - } - - - -.qtipmodal-ie6fix{ - position: absolute !important; -} \ No newline at end of file -- cgit v1.2.3 From 7da9a5206800d305f97b025e180300ca451ac92d Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Tue, 3 Nov 2020 17:08:05 +0300 Subject: switch from assertFalse to assertIs --- wqflask/tests/wqflask/show_trait/test_show_trait.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index f666912b..ec45d558 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -31,7 +31,6 @@ class TestTraits(unittest.TestCase): self.assertIsInstance(trait_obj, TraitObject) self.assertIs(results, True) self.assertIs(result2, True) - def test_check_if_attr_exists_empty_attr(self): """test if attributes exists with false return""" @@ -98,7 +97,7 @@ class TestTraits(unittest.TestCase): create_dataset = TraitObject({"type": "ProbeSet"}) create_trait = TraitObject({"dataset": create_dataset}) - self.assertFalse(has_num_cases(create_trait), False) + self.assertIs(has_num_cases(create_trait), False) def test_hash_num_cases_no_probeset(self): """test for hash num cases with dataset.type not Probeset""" -- cgit v1.2.3 From d6c18c29ed9ccfadc1798c8a7c460faa428d1d5b Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 17:27:15 +0300 Subject: Apply PEP-8 to file * wqflask/wqflask/__init__.py: Remove unused import and variables. Also add module docstring and pylint errors to ignore. (before_request): Use correct spacing. --- wqflask/wqflask/__init__.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/wqflask/wqflask/__init__.py b/wqflask/wqflask/__init__.py index 274c3d82..eeece241 100644 --- a/wqflask/wqflask/__init__.py +++ b/wqflask/wqflask/__init__.py @@ -1,4 +1,5 @@ -import sys +"""Entry point for flask app""" +# pylint: disable=C0413,E0611 import time import jinja2 @@ -6,10 +7,6 @@ from flask import g from flask import Flask from utility import formatting -import logging -logger = logging.getLogger(__name__ ) -logging.basicConfig(level=logging.INFO) - app = Flask(__name__) # See http://flask.pocoo.org/docs/config/#configuring-from-files @@ -22,8 +19,8 @@ app.jinja_env.globals.update( @app.before_request def before_request(): - g.request_start_time = time.time() - g.request_time = lambda: "%.5fs" % (time.time() - g.request_start_time) + g.request_start_time = time.time() + g.request_time = lambda: "%.5fs" % (time.time() - g.request_start_time) from wqflask.api import router -- cgit v1.2.3 From 52b2b9b8a68572207d9165e57b50fd90c63b1d1e Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 17:29:04 +0300 Subject: Add and register glossary blueprint --- wqflask/wqflask/__init__.py | 3 +++ wqflask/wqflask/glossary.py | 9 +++++++++ 2 files changed, 12 insertions(+) create mode 100644 wqflask/wqflask/glossary.py diff --git a/wqflask/wqflask/__init__.py b/wqflask/wqflask/__init__.py index eeece241..a3870ce6 100644 --- a/wqflask/wqflask/__init__.py +++ b/wqflask/wqflask/__init__.py @@ -6,6 +6,7 @@ import jinja2 from flask import g from flask import Flask from utility import formatting +from wqflask.glossary import glossary_blueprint app = Flask(__name__) @@ -16,6 +17,8 @@ app.jinja_env.globals.update( undefined=jinja2.StrictUndefined, numify=formatting.numify) +# Registering blueprints +app.register_blueprint(glossary_blueprint, url_prefix="/glossary") @app.before_request def before_request(): diff --git a/wqflask/wqflask/glossary.py b/wqflask/wqflask/glossary.py new file mode 100644 index 00000000..0918744a --- /dev/null +++ b/wqflask/wqflask/glossary.py @@ -0,0 +1,9 @@ +from flask import Blueprint + + +glossary_blueprint = Blueprint('glossary_blueprint', __name__) + + +@glossary_blueprint.route('/') +def glossary(): + return "This is a test", 200 -- cgit v1.2.3 From 682763de47dbf15048faf302bb7f8ed524ecb27b Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 17:33:58 +0300 Subject: Add glossary template and use it --- wqflask/wqflask/glossary.py | 4 ++-- wqflask/wqflask/templates/glossary.html | 7 +++++++ 2 files changed, 9 insertions(+), 2 deletions(-) create mode 100644 wqflask/wqflask/templates/glossary.html diff --git a/wqflask/wqflask/glossary.py b/wqflask/wqflask/glossary.py index 0918744a..a44e7c45 100644 --- a/wqflask/wqflask/glossary.py +++ b/wqflask/wqflask/glossary.py @@ -1,9 +1,9 @@ from flask import Blueprint - +from flask import render_template glossary_blueprint = Blueprint('glossary_blueprint', __name__) @glossary_blueprint.route('/') def glossary(): - return "This is a test", 200 + return render_template("glossary.html"), 200 diff --git a/wqflask/wqflask/templates/glossary.html b/wqflask/wqflask/templates/glossary.html new file mode 100644 index 00000000..988297d3 --- /dev/null +++ b/wqflask/wqflask/templates/glossary.html @@ -0,0 +1,7 @@ +{% extends "base.html" %} + +{% block title %}Glossary{% endblock %} + +{% block content %} +Test +{% endblock %} -- cgit v1.2.3 From 5d9b1f5d6380beaf8a2d713b5c33baa5a163b2bc Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 18:11:06 +0300 Subject: Add test for "/glossary" route --- wqflask/tests/integration/__init__.py | 0 wqflask/tests/integration/test_glossary.py | 28 ++++++++++++++++++++++++++++ 2 files changed, 28 insertions(+) create mode 100644 wqflask/tests/integration/__init__.py create mode 100644 wqflask/tests/integration/test_glossary.py diff --git a/wqflask/tests/integration/__init__.py b/wqflask/tests/integration/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/wqflask/tests/integration/test_glossary.py b/wqflask/tests/integration/test_glossary.py new file mode 100644 index 00000000..c9f1e62a --- /dev/null +++ b/wqflask/tests/integration/test_glossary.py @@ -0,0 +1,28 @@ +"Integration tests for glossary" +import unittest + +from bs4 import BeautifulSoup + +from wqflask import app + + +class TestGenMenu(unittest.TestCase): + """Tests for glossary""" + + def setUp(self): + self.app = app.test_client() + + def tearDown(self): + pass + + def test_glossary_page(self): + """Test that the glossary page is rendered properly""" + response = self.app.get('/glossary', follow_redirects=True) + html_content = BeautifulSoup(response.data, "lxml") + self.assertEqual(html_content.find("title").get_text(), + "Glossary GeneNetwork 2") + self.assertEqual( + html_content.find( + 'p', + attrs={'id': 'mytest'}).get_text(), + "Test") -- cgit v1.2.3 From f4a3652ee5b8087f551553df9498d5f00e169a86 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 18:13:57 +0300 Subject: Separate unittests from integration tests --- wqflask/tests/base/__init__.py | 0 wqflask/tests/base/data.py | 110 ------ wqflask/tests/base/test_data_set.py | 181 --------- wqflask/tests/base/test_general_object.py | 40 -- wqflask/tests/base/test_trait.py | 241 ------------ wqflask/tests/base/test_webqtl_case_data.py | 39 -- wqflask/tests/unit/__init__.py | 0 wqflask/tests/unit/base/__init__.py | 0 wqflask/tests/unit/base/data.py | 110 ++++++ wqflask/tests/unit/base/test_data_set.py | 181 +++++++++ wqflask/tests/unit/base/test_general_object.py | 40 ++ wqflask/tests/unit/base/test_trait.py | 241 ++++++++++++ wqflask/tests/unit/base/test_webqtl_case_data.py | 39 ++ wqflask/tests/unit/utility/__init__.py | 0 .../unit/utility/test_authentication_tools.py | 189 ++++++++++ wqflask/tests/unit/utility/test_chunks.py | 19 + wqflask/tests/unit/utility/test_corestats.py | 55 +++ .../tests/unit/utility/test_corr_result_helpers.py | 32 ++ wqflask/tests/unit/utility/test_formatting.py | 33 ++ wqflask/tests/unit/utility/test_hmac.py | 52 +++ wqflask/tests/unit/wqflask/__init__.py | 0 wqflask/tests/unit/wqflask/api/__init__.py | 0 wqflask/tests/unit/wqflask/api/test_gen_menu.py | 413 +++++++++++++++++++++ .../unit/wqflask/marker_regression/__init__.py | 0 .../test_display_mapping_results.py | 156 ++++++++ wqflask/tests/unit/wqflask/show_trait/__init__.py | 0 .../wqflask/show_trait/test_export_trait_data.py | 212 +++++++++++ wqflask/tests/unit/wqflask/test_collect.py | 73 ++++ wqflask/tests/unit/wqflask/test_pbkdf2.py | 61 +++ wqflask/tests/unit/wqflask/test_user_login.py | 21 ++ wqflask/tests/unit/wqflask/test_user_session.py | 15 + wqflask/tests/utility/__init__.py | 0 wqflask/tests/utility/test_authentication_tools.py | 189 ---------- wqflask/tests/utility/test_chunks.py | 19 - wqflask/tests/utility/test_corestats.py | 55 --- wqflask/tests/utility/test_corr_result_helpers.py | 32 -- wqflask/tests/utility/test_formatting.py | 33 -- wqflask/tests/utility/test_hmac.py | 52 --- wqflask/tests/wqflask/__init__.py | 0 wqflask/tests/wqflask/api/__init__.py | 0 wqflask/tests/wqflask/api/test_gen_menu.py | 413 --------------------- .../tests/wqflask/marker_regression/__init__.py | 0 .../test_display_mapping_results.py | 156 -------- wqflask/tests/wqflask/show_trait/__init__.py | 0 .../wqflask/show_trait/test_export_trait_data.py | 212 ----------- wqflask/tests/wqflask/test_collect.py | 73 ---- wqflask/tests/wqflask/test_pbkdf2.py | 61 --- wqflask/tests/wqflask/test_user_login.py | 21 -- wqflask/tests/wqflask/test_user_session.py | 15 - wqflask/wqflask/templates/glossary.html | 2 +- 50 files changed, 1943 insertions(+), 1943 deletions(-) delete mode 100644 wqflask/tests/base/__init__.py delete mode 100644 wqflask/tests/base/data.py delete mode 100644 wqflask/tests/base/test_data_set.py delete mode 100644 wqflask/tests/base/test_general_object.py delete mode 100644 wqflask/tests/base/test_trait.py delete mode 100644 wqflask/tests/base/test_webqtl_case_data.py create mode 100644 wqflask/tests/unit/__init__.py create mode 100644 wqflask/tests/unit/base/__init__.py create mode 100644 wqflask/tests/unit/base/data.py create mode 100644 wqflask/tests/unit/base/test_data_set.py create mode 100644 wqflask/tests/unit/base/test_general_object.py create mode 100644 wqflask/tests/unit/base/test_trait.py create mode 100644 wqflask/tests/unit/base/test_webqtl_case_data.py create mode 100644 wqflask/tests/unit/utility/__init__.py create mode 100644 wqflask/tests/unit/utility/test_authentication_tools.py create mode 100644 wqflask/tests/unit/utility/test_chunks.py create mode 100644 wqflask/tests/unit/utility/test_corestats.py create mode 100644 wqflask/tests/unit/utility/test_corr_result_helpers.py create mode 100644 wqflask/tests/unit/utility/test_formatting.py create mode 100644 wqflask/tests/unit/utility/test_hmac.py create mode 100644 wqflask/tests/unit/wqflask/__init__.py create mode 100644 wqflask/tests/unit/wqflask/api/__init__.py create mode 100644 wqflask/tests/unit/wqflask/api/test_gen_menu.py create mode 100644 wqflask/tests/unit/wqflask/marker_regression/__init__.py create mode 100644 wqflask/tests/unit/wqflask/marker_regression/test_display_mapping_results.py create mode 100644 wqflask/tests/unit/wqflask/show_trait/__init__.py create mode 100644 wqflask/tests/unit/wqflask/show_trait/test_export_trait_data.py create mode 100644 wqflask/tests/unit/wqflask/test_collect.py create mode 100644 wqflask/tests/unit/wqflask/test_pbkdf2.py create mode 100644 wqflask/tests/unit/wqflask/test_user_login.py create mode 100644 wqflask/tests/unit/wqflask/test_user_session.py delete mode 100644 wqflask/tests/utility/__init__.py delete mode 100644 wqflask/tests/utility/test_authentication_tools.py delete mode 100644 wqflask/tests/utility/test_chunks.py delete mode 100644 wqflask/tests/utility/test_corestats.py delete mode 100644 wqflask/tests/utility/test_corr_result_helpers.py delete mode 100644 wqflask/tests/utility/test_formatting.py delete mode 100644 wqflask/tests/utility/test_hmac.py delete mode 100644 wqflask/tests/wqflask/__init__.py delete mode 100644 wqflask/tests/wqflask/api/__init__.py delete mode 100644 wqflask/tests/wqflask/api/test_gen_menu.py delete mode 100644 wqflask/tests/wqflask/marker_regression/__init__.py delete mode 100644 wqflask/tests/wqflask/marker_regression/test_display_mapping_results.py delete mode 100644 wqflask/tests/wqflask/show_trait/__init__.py delete mode 100644 wqflask/tests/wqflask/show_trait/test_export_trait_data.py delete mode 100644 wqflask/tests/wqflask/test_collect.py delete mode 100644 wqflask/tests/wqflask/test_pbkdf2.py delete mode 100644 wqflask/tests/wqflask/test_user_login.py delete mode 100644 wqflask/tests/wqflask/test_user_session.py diff --git a/wqflask/tests/base/__init__.py b/wqflask/tests/base/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/wqflask/tests/base/data.py b/wqflask/tests/base/data.py deleted file mode 100644 index 06a5a989..00000000 --- a/wqflask/tests/base/data.py +++ /dev/null @@ -1,110 +0,0 @@ -gen_menu_json = """ -{ - "datasets": { - "human": { - "HLC": { - "Liver mRNA": [ - [ - "320", - "HLC_0311", - "GSE9588 Human Liver Normal (Mar11) Both Sexes" - ] - ], - "Phenotypes": [ - [ - "635", - "HLCPublish", - "HLC Published Phenotypes" - ] - ] - } - }, - "mouse": { - "BXD": { - "Genotypes": [ - [ - "600", - "BXDGeno", - "BXD Genotypes" - ] - ], - "Hippocampus mRNA": [ - [ - "112", - "HC_M2_0606_P", - "Hippocampus Consortium M430v2 (Jun06) PDNN" - ] - ], - "Phenotypes": [ - [ - "602", - "BXDPublish", - "BXD Published Phenotypes" - ] - ] - } - } - }, - "groups": { - "human": [ - [ - "HLC", - "Liver: Normal Gene Expression with Genotypes (Merck)", - "Family:None" - ] - ], - "mouse": [ - [ - "BXD", - "BXD", - "Family:None" - ] - ] - }, - "species": [ - [ - "human", - "Human" - ], - [ - "mouse", - "Mouse" - ] - ], - "types": { - "human": { - "HLC": [ - [ - "Phenotypes", - "Traits and Cofactors", - "Phenotypes" - ], - [ - "Liver mRNA", - "Liver mRNA", - "Molecular Trait Datasets" - ] - ] - }, - "mouse": { - "BXD": [ - [ - "Phenotypes", - "Traits and Cofactors", - "Phenotypes" - ], - [ - "Genotypes", - "DNA Markers and SNPs", - "Genotypes" - ], - [ - "Hippocampus mRNA", - "Hippocampus mRNA", - "Molecular Trait Datasets" - ] - ] - } - } -} -""" diff --git a/wqflask/tests/base/test_data_set.py b/wqflask/tests/base/test_data_set.py deleted file mode 100644 index 96563a16..00000000 --- a/wqflask/tests/base/test_data_set.py +++ /dev/null @@ -1,181 +0,0 @@ -"""Tests for wqflask/base/data_set.py""" - -import unittest -from unittest import mock - -from wqflask import app -from .data import gen_menu_json -from base.data_set import DatasetType - - -class TestDataSetTypes(unittest.TestCase): - """Tests for the DataSetType class""" - - def setUp(self): - self.test_dataset = """ - { - "AD-cases-controls-MyersGeno": "Geno", - "AD-cases-controls-MyersPublish": "Publish", - "AKXDGeno": "Geno", - "AXBXAGeno": "Geno", - "AXBXAPublish": "Publish", - "Aging-Brain-UCIPublish": "Publish", - "All Phenotypes": "Publish", - "B139_K_1206_M": "ProbeSet", - "B139_K_1206_R": "ProbeSet" - } - """ - self.app_context = app.app_context() - self.app_context.push() - - def tearDown(self): - self.app_context.pop() - - @mock.patch('base.data_set.g') - def test_data_set_type(self, db_mock): - """Test that DatasetType returns correctly if the Redis Instance is not empty - and the name variable exists in the dictionary - - """ - with app.app_context(): - db_mock.get = mock.Mock() - redis_mock = mock.Mock() - redis_mock.get.return_value = self.test_dataset - self.assertEqual(DatasetType(redis_mock) - ("All Phenotypes"), "Publish") - redis_mock.get.assert_called_once_with("dataset_structure") - - @mock.patch('base.data_set.requests.get') - def test_data_set_type_with_empty_redis(self, request_mock): - """Test that DatasetType returns correctly if the Redis Instance is empty and - the name variable exists in the dictionary - - """ - with app.app_context(): - request_mock.return_value.content = gen_menu_json - redis_mock = mock.Mock() - redis_mock.get.return_value = None - data_set = DatasetType(redis_mock) - self.assertEqual(data_set("BXDGeno"), "Geno") - self.assertEqual(data_set("BXDPublish"), "Publish") - self.assertEqual(data_set("HLC_0311"), "ProbeSet") - - redis_mock.set.assert_called_once_with( - "dataset_structure", - ('{"HLC_0311": "ProbeSet", ' - '"HLCPublish": "Publish", ' - '"BXDGeno": "Geno", ' - '"HC_M2_0606_P": "ProbeSet", ' - '"BXDPublish": "Publish"}')) - - @mock.patch('base.data_set.g') - def test_set_dataset_key_mrna(self, db_mock): - with app.app_context(): - db_mock.db.execute.return_value.fetchone.return_value = [1, 2, 3] - redis_mock = mock.Mock() - redis_mock.get.return_value = self.test_dataset - data_set = DatasetType(redis_mock) - data_set.set_dataset_key("mrna_expr", "Test") - self.assertEqual(data_set("Test"), "ProbeSet") - redis_mock.set.assert_called_once_with( - "dataset_structure", - ('{"AD-cases-controls-MyersGeno": "Geno", ' - '"AD-cases-controls-MyersPublish": "Publish", ' - '"AKXDGeno": "Geno", ' - '"AXBXAGeno": "Geno", ' - '"AXBXAPublish": "Publish", ' - '"Aging-Brain-UCIPublish": "Publish", ' - '"All Phenotypes": "Publish", ' - '"B139_K_1206_M": "ProbeSet", ' - '"B139_K_1206_R": "ProbeSet", ' - '"Test": "ProbeSet"}')) - - db_mock.db.execute.assert_called_with( - ("SELECT ProbeSetFreeze.Id FROM ProbeSetFreeze " + - "WHERE ProbeSetFreeze.Name = \"Test\" ") - ) - - @mock.patch('base.data_set.g') - def test_set_dataset_key_pheno(self, db_mock): - with app.app_context(): - db_mock.db.execute.return_value.fetchone.return_value = [1, 2, 3] - redis_mock = mock.Mock() - redis_mock.get.return_value = self.test_dataset - data_set = DatasetType(redis_mock) - data_set.set_dataset_key("pheno", "Test") - self.assertEqual(data_set("Test"), "Publish") - redis_mock.set.assert_called_once_with( - "dataset_structure", - ('{"AD-cases-controls-MyersGeno": "Geno", ' - '"AD-cases-controls-MyersPublish": "Publish", ' - '"AKXDGeno": "Geno", ' - '"AXBXAGeno": "Geno", ' - '"AXBXAPublish": "Publish", ' - '"Aging-Brain-UCIPublish": "Publish", ' - '"All Phenotypes": "Publish", ' - '"B139_K_1206_M": "ProbeSet", ' - '"B139_K_1206_R": "ProbeSet", ' - '"Test": "Publish"}')) - db_mock.db.execute.assert_called_with( - ("SELECT InfoFiles.GN_AccesionId " - "FROM InfoFiles, PublishFreeze, InbredSet " - "WHERE InbredSet.Name = 'Test' AND " - "PublishFreeze.InbredSetId = InbredSet.Id AND " - "InfoFiles.InfoPageName = PublishFreeze.Name") - ) - - @mock.patch('base.data_set.g') - def test_set_dataset_other_pheno(self, db_mock): - with app.app_context(): - db_mock.db.execute.return_value.fetchone.return_value = [1, 2, 3] - redis_mock = mock.Mock() - redis_mock.get.return_value = self.test_dataset - data_set = DatasetType(redis_mock) - data_set.set_dataset_key("other_pheno", "Test") - self.assertEqual(data_set("Test"), "Publish") - - redis_mock.set.assert_called_once_with( - "dataset_structure", - ('{"AD-cases-controls-MyersGeno": "Geno", ' - '"AD-cases-controls-MyersPublish": "Publish", ' - '"AKXDGeno": "Geno", ' - '"AXBXAGeno": "Geno", ' - '"AXBXAPublish": "Publish", ' - '"Aging-Brain-UCIPublish": "Publish", ' - '"All Phenotypes": "Publish", ' - '"B139_K_1206_M": "ProbeSet", ' - '"B139_K_1206_R": "ProbeSet", ' - '"Test": "Publish"}')) - - db_mock.db.execute.assert_called_with( - ("SELECT PublishFreeze.Name " + - "FROM PublishFreeze, InbredSet " + - "WHERE InbredSet.Name = 'Test' AND " - "PublishFreeze.InbredSetId = InbredSet.Id") - ) - - @mock.patch('base.data_set.g') - def test_set_dataset_geno(self, db_mock): - with app.app_context(): - db_mock.db.execute.return_value.fetchone.return_value = [1, 2, 3] - redis_mock = mock.Mock() - redis_mock.get.return_value = self.test_dataset - data_set = DatasetType(redis_mock) - data_set.set_dataset_key("geno", "Test") - self.assertEqual(data_set("Test"), "Geno") - redis_mock.set.assert_called_once_with( - "dataset_structure", - ('{"AD-cases-controls-MyersGeno": "Geno", ' - '"AD-cases-controls-MyersPublish": "Publish", ' - '"AKXDGeno": "Geno", ' - '"AXBXAGeno": "Geno", ' - '"AXBXAPublish": "Publish", ' - '"Aging-Brain-UCIPublish": "Publish", ' - '"All Phenotypes": "Publish", ' - '"B139_K_1206_M": "ProbeSet", ' - '"B139_K_1206_R": "ProbeSet", ' - '"Test": "Geno"}')) - - db_mock.db.execute.assert_called_with( - ("SELECT GenoFreeze.Id FROM " - "GenoFreeze WHERE GenoFreeze.Name = \"Test\" ")) diff --git a/wqflask/tests/base/test_general_object.py b/wqflask/tests/base/test_general_object.py deleted file mode 100644 index 00fd3c72..00000000 --- a/wqflask/tests/base/test_general_object.py +++ /dev/null @@ -1,40 +0,0 @@ -import unittest - -from base.GeneralObject import GeneralObject - - -class TestGeneralObjectTests(unittest.TestCase): - """ - Test the GeneralObject base class - """ - - def test_object_contents(self): - """Test whether base contents are stored properly""" - test_obj = GeneralObject("a", "b", "c") - self.assertEqual("abc", ''.join(test_obj.contents)) - self.assertEqual(len(test_obj), 0) - - def test_object_dict(self): - """Test whether the base class is printed properly""" - test_obj = GeneralObject("a", name="test", value=1) - self.assertEqual(str(test_obj), "name = test\nvalue = 1\n") - self.assertEqual( - repr(test_obj), "contents = ['a']\nname = test\nvalue = 1\n") - self.assertEqual(len(test_obj), 2) - self.assertEqual(test_obj["value"], 1) - test_obj["test"] = 1 - self.assertEqual(test_obj["test"], 1) - - def test_get_attribute(self): - "Test that getattr works" - test_obj = GeneralObject("a", name="test", value=1) - self.assertEqual(getattr(test_obj, "value", None), 1) - self.assertEqual(getattr(test_obj, "non-existent", None), None) - - def test_object_comparisons(self): - "Test that 2 objects of the same length are equal" - test_obj1 = GeneralObject("a", name="test", value=1) - test_obj2 = GeneralObject("b", name="test2", value=2) - test_obj3 = GeneralObject("a", name="test", x=1, y=2) - self.assertTrue(test_obj1 == test_obj2) - self.assertFalse(test_obj1 == test_obj3) diff --git a/wqflask/tests/base/test_trait.py b/wqflask/tests/base/test_trait.py deleted file mode 100644 index 826ccefd..00000000 --- a/wqflask/tests/base/test_trait.py +++ /dev/null @@ -1,241 +0,0 @@ -# -*- coding: utf-8 -*- -"""Tests wqflask/base/trait.py""" -import unittest -from unittest import mock - -from wqflask import app -from base.trait import GeneralTrait -from base.trait import retrieve_trait_info - - -class TestResponse: - """Mock Test Response after a request""" - @property - def content(self): - """Mock the content from Requests.get(params).content""" - return "[1, 2, 3, 4]" - - -class TestNilResponse: - """Mock Test Response after a request""" - @property - def content(self): - """Mock the content from Requests.get(params).content""" - return "{}" - - -class MockTrait(GeneralTrait): - @property - def wikidata_alias_fmt(self): - return "Mock alias" - - -class TestRetrieveTraitInfo(unittest.TestCase): - """Tests for 'retrieve_trait_info'""" - - def setUp(self): - self.app_context = app.app_context() - self.app_context.push() - - def tearDown(self): - self.app_context.pop() - - def test_retrieve_trait_info_with_empty_dataset(self): - """Test that an exception is raised when dataset is empty""" - with self.assertRaises(AssertionError): - retrieve_trait_info(trait=mock.MagicMock(), - dataset={}) - - @mock.patch('base.trait.requests.get') - @mock.patch('base.trait.g', mock.Mock()) - def test_retrieve_trait_info_with_empty_trait_info(self, - requests_mock): - """Empty trait info""" - requests_mock.return_value = TestNilResponse() - with self.assertRaises(KeyError): - retrieve_trait_info(trait=mock.MagicMock(), - dataset=mock.MagicMock()) - - @mock.patch('base.trait.requests.get') - @mock.patch('base.trait.g', mock.Mock()) - def test_retrieve_trait_info_with_non_empty_trait_info(self, - requests_mock): - """Test that attributes are set""" - mock_dataset = mock.MagicMock() - requests_mock.return_value = TestResponse() - type(mock_dataset).display_fields = mock.PropertyMock( - return_value=["a", "b", "c", "d"]) - test_trait = retrieve_trait_info(trait=MockTrait(dataset=mock_dataset), - dataset=mock_dataset) - self.assertEqual(test_trait.a, 1) - self.assertEqual(test_trait.b, 2) - self.assertEqual(test_trait.c, 3) - self.assertEqual(test_trait.d, 4) - - @mock.patch('base.trait.requests.get') - @mock.patch('base.trait.g', mock.Mock()) - def test_retrieve_trait_info_utf8_parsing(self, - requests_mock): - """Test that utf-8 strings are parsed correctly""" - utf_8_string = "test_string" - mock_dataset = mock.MagicMock() - requests_mock.return_value = TestResponse() - type(mock_dataset).display_fields = mock.PropertyMock( - return_value=["a", "b", "c", "d"]) - type(mock_dataset).type = 'Publish' - - mock_trait = MockTrait( - dataset=mock_dataset, - pre_publication_description=utf_8_string - ) - trait_attrs = { - "group_code": "test_code", - "pre_publication_description": "test_pre_pub", - "pre_publication_abbreviation": "ファイルを画面毎に見て行くには、次のコマンドを使います。", - "post_publication_description": None, - "pubmed_id": None, - 'year': "2020", - "authors": "Jane Doe かいと", - } - for key, val in list(trait_attrs.items()): - setattr(mock_trait, key, val) - test_trait = retrieve_trait_info(trait=mock_trait, - dataset=mock_dataset) - self.assertEqual(test_trait.abbreviation, - "ファイルを画面毎に見て行くには、次のコマンドを使います。") - self.assertEqual(test_trait.authors, - "Jane Doe かいと") - - @mock.patch('base.trait.requests.get') - @mock.patch('base.trait.g') - @mock.patch('base.trait.get_resource_id') - def test_retrieve_trait_info_with_non_empty_lrs(self, - resource_id_mock, - g_mock, - requests_mock): - """Test retrieve trait info when lrs has a value""" - resource_id_mock.return_value = 1 - g_mock.db.execute.return_value.fetchone = mock.Mock() - g_mock.db.execute.return_value.fetchone.side_effect = [ - [1, 2, 3, 4], # trait_info = g.db.execute(query).fetchone() - [1, 2.37, 3, 4, 5], # trait_qtl = g.db.execute(query).fetchone() - [2.7333, 2.1204] # trait_info = g.db.execute(query).fetchone() - ] - requests_mock.return_value = None - - mock_dataset = mock.MagicMock() - type(mock_dataset).display_fields = mock.PropertyMock( - return_value=["a", "b", "c", "d"]) - type(mock_dataset).type = "ProbeSet" - type(mock_dataset).name = "RandomName" - - mock_trait = MockTrait( - dataset=mock_dataset, - pre_publication_description="test_string" - ) - trait_attrs = { - "description": "some description", - "probe_target_description": "some description", - "cellid": False, - "chr": 2.733, - "mb": 2.1204 - } - - for key, val in list(trait_attrs.items()): - setattr(mock_trait, key, val) - test_trait = retrieve_trait_info(trait=mock_trait, - dataset=mock_dataset, - get_qtl_info=True) - self.assertEqual(test_trait.LRS_score_repr, - "2.4") - - @mock.patch('base.trait.requests.get') - @mock.patch('base.trait.g') - @mock.patch('base.trait.get_resource_id') - def test_retrieve_trait_info_with_empty_lrs_field(self, - resource_id_mock, - g_mock, - requests_mock): - """Test retrieve trait info with empty lrs field""" - resource_id_mock.return_value = 1 - g_mock.db.execute.return_value.fetchone = mock.Mock() - g_mock.db.execute.return_value.fetchone.side_effect = [ - [1, 2, 3, 4], # trait_info = g.db.execute(query).fetchone() - [1, None, 3, 4, 5], # trait_qtl = g.db.execute(query).fetchone() - [2, 3] # trait_info = g.db.execute(query).fetchone() - ] - requests_mock.return_value = None - - mock_dataset = mock.MagicMock() - type(mock_dataset).display_fields = mock.PropertyMock( - return_value=["a", "b", "c", "d"]) - type(mock_dataset).type = "ProbeSet" - type(mock_dataset).name = "RandomName" - - mock_trait = MockTrait( - dataset=mock_dataset, - pre_publication_description="test_string" - ) - trait_attrs = { - "description": "some description", - "probe_target_description": "some description", - "cellid": False, - "chr": 2.733, - "mb": 2.1204 - } - - for key, val in list(trait_attrs.items()): - setattr(mock_trait, key, val) - test_trait = retrieve_trait_info(trait=mock_trait, - dataset=mock_dataset, - get_qtl_info=True) - self.assertEqual(test_trait.LRS_score_repr, - "N/A") - self.assertEqual(test_trait.LRS_location_repr, - "Chr2: 3.000000") - - @mock.patch('base.trait.requests.get') - @mock.patch('base.trait.g') - @mock.patch('base.trait.get_resource_id') - def test_retrieve_trait_info_with_empty_chr_field(self, - resource_id_mock, - g_mock, - requests_mock): - """Test retrieve trait info with empty chr field""" - resource_id_mock.return_value = 1 - g_mock.db.execute.return_value.fetchone = mock.Mock() - g_mock.db.execute.return_value.fetchone.side_effect = [ - [1, 2, 3, 4], # trait_info = g.db.execute(query).fetchone() - [1, 2, 3, 4, 5], # trait_qtl = g.db.execute(query).fetchone() - [None, 3] # trait_info = g.db.execute(query).fetchone() - ] - - requests_mock.return_value = None - - mock_dataset = mock.MagicMock() - type(mock_dataset).display_fields = mock.PropertyMock( - return_value=["a", "b", "c", "d"]) - type(mock_dataset).type = "ProbeSet" - type(mock_dataset).name = "RandomName" - - mock_trait = MockTrait( - dataset=mock_dataset, - pre_publication_description="test_string" - ) - trait_attrs = { - "description": "some description", - "probe_target_description": "some description", - "cellid": False, - "chr": 2.733, - "mb": 2.1204 - } - - for key, val in list(trait_attrs.items()): - setattr(mock_trait, key, val) - test_trait = retrieve_trait_info(trait=mock_trait, - dataset=mock_dataset, - get_qtl_info=True) - self.assertEqual(test_trait.LRS_score_repr, - "N/A") - self.assertEqual(test_trait.LRS_location_repr, - "N/A") diff --git a/wqflask/tests/base/test_webqtl_case_data.py b/wqflask/tests/base/test_webqtl_case_data.py deleted file mode 100644 index 8e8ba482..00000000 --- a/wqflask/tests/base/test_webqtl_case_data.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Tests for wqflask/base/webqtlCaseData.py""" -import unittest - -from wqflask import app # Required because of utility.tools in webqtlCaseData.py -from base.webqtlCaseData import webqtlCaseData - -class TestWebqtlCaseData(unittest.TestCase): - """Tests for WebqtlCaseData class""" - - def setUp(self): - self.w = webqtlCaseData(name="Test", - value=0, - variance=0.0, - num_cases=10, - name2="Test2") - - def test_webqtl_case_data_repr(self): - self.assertEqual( - repr(self.w), - " value=0.000 variance=0.000 ndata=10 name=Test name2=Test2" - ) - - def test_class_outlier(self): - self.assertEqual(self.w.class_outlier, "") - - def test_display_value(self): - self.assertEqual(self.w.display_value, "0.000") - self.w.value = None - self.assertEqual(self.w.display_value, "x") - - def test_display_variance(self): - self.assertEqual(self.w.display_variance, "0.000") - self.w.variance = None - self.assertEqual(self.w.display_variance, "x") - - def test_display_num_cases(self): - self.assertEqual(self.w.display_num_cases, "10") - self.w.num_cases = None - self.assertEqual(self.w.display_num_cases, "x") diff --git a/wqflask/tests/unit/__init__.py b/wqflask/tests/unit/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/wqflask/tests/unit/base/__init__.py b/wqflask/tests/unit/base/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/wqflask/tests/unit/base/data.py b/wqflask/tests/unit/base/data.py new file mode 100644 index 00000000..06a5a989 --- /dev/null +++ b/wqflask/tests/unit/base/data.py @@ -0,0 +1,110 @@ +gen_menu_json = """ +{ + "datasets": { + "human": { + "HLC": { + "Liver mRNA": [ + [ + "320", + "HLC_0311", + "GSE9588 Human Liver Normal (Mar11) Both Sexes" + ] + ], + "Phenotypes": [ + [ + "635", + "HLCPublish", + "HLC Published Phenotypes" + ] + ] + } + }, + "mouse": { + "BXD": { + "Genotypes": [ + [ + "600", + "BXDGeno", + "BXD Genotypes" + ] + ], + "Hippocampus mRNA": [ + [ + "112", + "HC_M2_0606_P", + "Hippocampus Consortium M430v2 (Jun06) PDNN" + ] + ], + "Phenotypes": [ + [ + "602", + "BXDPublish", + "BXD Published Phenotypes" + ] + ] + } + } + }, + "groups": { + "human": [ + [ + "HLC", + "Liver: Normal Gene Expression with Genotypes (Merck)", + "Family:None" + ] + ], + "mouse": [ + [ + "BXD", + "BXD", + "Family:None" + ] + ] + }, + "species": [ + [ + "human", + "Human" + ], + [ + "mouse", + "Mouse" + ] + ], + "types": { + "human": { + "HLC": [ + [ + "Phenotypes", + "Traits and Cofactors", + "Phenotypes" + ], + [ + "Liver mRNA", + "Liver mRNA", + "Molecular Trait Datasets" + ] + ] + }, + "mouse": { + "BXD": [ + [ + "Phenotypes", + "Traits and Cofactors", + "Phenotypes" + ], + [ + "Genotypes", + "DNA Markers and SNPs", + "Genotypes" + ], + [ + "Hippocampus mRNA", + "Hippocampus mRNA", + "Molecular Trait Datasets" + ] + ] + } + } +} +""" diff --git a/wqflask/tests/unit/base/test_data_set.py b/wqflask/tests/unit/base/test_data_set.py new file mode 100644 index 00000000..96563a16 --- /dev/null +++ b/wqflask/tests/unit/base/test_data_set.py @@ -0,0 +1,181 @@ +"""Tests for wqflask/base/data_set.py""" + +import unittest +from unittest import mock + +from wqflask import app +from .data import gen_menu_json +from base.data_set import DatasetType + + +class TestDataSetTypes(unittest.TestCase): + """Tests for the DataSetType class""" + + def setUp(self): + self.test_dataset = """ + { + "AD-cases-controls-MyersGeno": "Geno", + "AD-cases-controls-MyersPublish": "Publish", + "AKXDGeno": "Geno", + "AXBXAGeno": "Geno", + "AXBXAPublish": "Publish", + "Aging-Brain-UCIPublish": "Publish", + "All Phenotypes": "Publish", + "B139_K_1206_M": "ProbeSet", + "B139_K_1206_R": "ProbeSet" + } + """ + self.app_context = app.app_context() + self.app_context.push() + + def tearDown(self): + self.app_context.pop() + + @mock.patch('base.data_set.g') + def test_data_set_type(self, db_mock): + """Test that DatasetType returns correctly if the Redis Instance is not empty + and the name variable exists in the dictionary + + """ + with app.app_context(): + db_mock.get = mock.Mock() + redis_mock = mock.Mock() + redis_mock.get.return_value = self.test_dataset + self.assertEqual(DatasetType(redis_mock) + ("All Phenotypes"), "Publish") + redis_mock.get.assert_called_once_with("dataset_structure") + + @mock.patch('base.data_set.requests.get') + def test_data_set_type_with_empty_redis(self, request_mock): + """Test that DatasetType returns correctly if the Redis Instance is empty and + the name variable exists in the dictionary + + """ + with app.app_context(): + request_mock.return_value.content = gen_menu_json + redis_mock = mock.Mock() + redis_mock.get.return_value = None + data_set = DatasetType(redis_mock) + self.assertEqual(data_set("BXDGeno"), "Geno") + self.assertEqual(data_set("BXDPublish"), "Publish") + self.assertEqual(data_set("HLC_0311"), "ProbeSet") + + redis_mock.set.assert_called_once_with( + "dataset_structure", + ('{"HLC_0311": "ProbeSet", ' + '"HLCPublish": "Publish", ' + '"BXDGeno": "Geno", ' + '"HC_M2_0606_P": "ProbeSet", ' + '"BXDPublish": "Publish"}')) + + @mock.patch('base.data_set.g') + def test_set_dataset_key_mrna(self, db_mock): + with app.app_context(): + db_mock.db.execute.return_value.fetchone.return_value = [1, 2, 3] + redis_mock = mock.Mock() + redis_mock.get.return_value = self.test_dataset + data_set = DatasetType(redis_mock) + data_set.set_dataset_key("mrna_expr", "Test") + self.assertEqual(data_set("Test"), "ProbeSet") + redis_mock.set.assert_called_once_with( + "dataset_structure", + ('{"AD-cases-controls-MyersGeno": "Geno", ' + '"AD-cases-controls-MyersPublish": "Publish", ' + '"AKXDGeno": "Geno", ' + '"AXBXAGeno": "Geno", ' + '"AXBXAPublish": "Publish", ' + '"Aging-Brain-UCIPublish": "Publish", ' + '"All Phenotypes": "Publish", ' + '"B139_K_1206_M": "ProbeSet", ' + '"B139_K_1206_R": "ProbeSet", ' + '"Test": "ProbeSet"}')) + + db_mock.db.execute.assert_called_with( + ("SELECT ProbeSetFreeze.Id FROM ProbeSetFreeze " + + "WHERE ProbeSetFreeze.Name = \"Test\" ") + ) + + @mock.patch('base.data_set.g') + def test_set_dataset_key_pheno(self, db_mock): + with app.app_context(): + db_mock.db.execute.return_value.fetchone.return_value = [1, 2, 3] + redis_mock = mock.Mock() + redis_mock.get.return_value = self.test_dataset + data_set = DatasetType(redis_mock) + data_set.set_dataset_key("pheno", "Test") + self.assertEqual(data_set("Test"), "Publish") + redis_mock.set.assert_called_once_with( + "dataset_structure", + ('{"AD-cases-controls-MyersGeno": "Geno", ' + '"AD-cases-controls-MyersPublish": "Publish", ' + '"AKXDGeno": "Geno", ' + '"AXBXAGeno": "Geno", ' + '"AXBXAPublish": "Publish", ' + '"Aging-Brain-UCIPublish": "Publish", ' + '"All Phenotypes": "Publish", ' + '"B139_K_1206_M": "ProbeSet", ' + '"B139_K_1206_R": "ProbeSet", ' + '"Test": "Publish"}')) + db_mock.db.execute.assert_called_with( + ("SELECT InfoFiles.GN_AccesionId " + "FROM InfoFiles, PublishFreeze, InbredSet " + "WHERE InbredSet.Name = 'Test' AND " + "PublishFreeze.InbredSetId = InbredSet.Id AND " + "InfoFiles.InfoPageName = PublishFreeze.Name") + ) + + @mock.patch('base.data_set.g') + def test_set_dataset_other_pheno(self, db_mock): + with app.app_context(): + db_mock.db.execute.return_value.fetchone.return_value = [1, 2, 3] + redis_mock = mock.Mock() + redis_mock.get.return_value = self.test_dataset + data_set = DatasetType(redis_mock) + data_set.set_dataset_key("other_pheno", "Test") + self.assertEqual(data_set("Test"), "Publish") + + redis_mock.set.assert_called_once_with( + "dataset_structure", + ('{"AD-cases-controls-MyersGeno": "Geno", ' + '"AD-cases-controls-MyersPublish": "Publish", ' + '"AKXDGeno": "Geno", ' + '"AXBXAGeno": "Geno", ' + '"AXBXAPublish": "Publish", ' + '"Aging-Brain-UCIPublish": "Publish", ' + '"All Phenotypes": "Publish", ' + '"B139_K_1206_M": "ProbeSet", ' + '"B139_K_1206_R": "ProbeSet", ' + '"Test": "Publish"}')) + + db_mock.db.execute.assert_called_with( + ("SELECT PublishFreeze.Name " + + "FROM PublishFreeze, InbredSet " + + "WHERE InbredSet.Name = 'Test' AND " + "PublishFreeze.InbredSetId = InbredSet.Id") + ) + + @mock.patch('base.data_set.g') + def test_set_dataset_geno(self, db_mock): + with app.app_context(): + db_mock.db.execute.return_value.fetchone.return_value = [1, 2, 3] + redis_mock = mock.Mock() + redis_mock.get.return_value = self.test_dataset + data_set = DatasetType(redis_mock) + data_set.set_dataset_key("geno", "Test") + self.assertEqual(data_set("Test"), "Geno") + redis_mock.set.assert_called_once_with( + "dataset_structure", + ('{"AD-cases-controls-MyersGeno": "Geno", ' + '"AD-cases-controls-MyersPublish": "Publish", ' + '"AKXDGeno": "Geno", ' + '"AXBXAGeno": "Geno", ' + '"AXBXAPublish": "Publish", ' + '"Aging-Brain-UCIPublish": "Publish", ' + '"All Phenotypes": "Publish", ' + '"B139_K_1206_M": "ProbeSet", ' + '"B139_K_1206_R": "ProbeSet", ' + '"Test": "Geno"}')) + + db_mock.db.execute.assert_called_with( + ("SELECT GenoFreeze.Id FROM " + "GenoFreeze WHERE GenoFreeze.Name = \"Test\" ")) diff --git a/wqflask/tests/unit/base/test_general_object.py b/wqflask/tests/unit/base/test_general_object.py new file mode 100644 index 00000000..00fd3c72 --- /dev/null +++ b/wqflask/tests/unit/base/test_general_object.py @@ -0,0 +1,40 @@ +import unittest + +from base.GeneralObject import GeneralObject + + +class TestGeneralObjectTests(unittest.TestCase): + """ + Test the GeneralObject base class + """ + + def test_object_contents(self): + """Test whether base contents are stored properly""" + test_obj = GeneralObject("a", "b", "c") + self.assertEqual("abc", ''.join(test_obj.contents)) + self.assertEqual(len(test_obj), 0) + + def test_object_dict(self): + """Test whether the base class is printed properly""" + test_obj = GeneralObject("a", name="test", value=1) + self.assertEqual(str(test_obj), "name = test\nvalue = 1\n") + self.assertEqual( + repr(test_obj), "contents = ['a']\nname = test\nvalue = 1\n") + self.assertEqual(len(test_obj), 2) + self.assertEqual(test_obj["value"], 1) + test_obj["test"] = 1 + self.assertEqual(test_obj["test"], 1) + + def test_get_attribute(self): + "Test that getattr works" + test_obj = GeneralObject("a", name="test", value=1) + self.assertEqual(getattr(test_obj, "value", None), 1) + self.assertEqual(getattr(test_obj, "non-existent", None), None) + + def test_object_comparisons(self): + "Test that 2 objects of the same length are equal" + test_obj1 = GeneralObject("a", name="test", value=1) + test_obj2 = GeneralObject("b", name="test2", value=2) + test_obj3 = GeneralObject("a", name="test", x=1, y=2) + self.assertTrue(test_obj1 == test_obj2) + self.assertFalse(test_obj1 == test_obj3) diff --git a/wqflask/tests/unit/base/test_trait.py b/wqflask/tests/unit/base/test_trait.py new file mode 100644 index 00000000..826ccefd --- /dev/null +++ b/wqflask/tests/unit/base/test_trait.py @@ -0,0 +1,241 @@ +# -*- coding: utf-8 -*- +"""Tests wqflask/base/trait.py""" +import unittest +from unittest import mock + +from wqflask import app +from base.trait import GeneralTrait +from base.trait import retrieve_trait_info + + +class TestResponse: + """Mock Test Response after a request""" + @property + def content(self): + """Mock the content from Requests.get(params).content""" + return "[1, 2, 3, 4]" + + +class TestNilResponse: + """Mock Test Response after a request""" + @property + def content(self): + """Mock the content from Requests.get(params).content""" + return "{}" + + +class MockTrait(GeneralTrait): + @property + def wikidata_alias_fmt(self): + return "Mock alias" + + +class TestRetrieveTraitInfo(unittest.TestCase): + """Tests for 'retrieve_trait_info'""" + + def setUp(self): + self.app_context = app.app_context() + self.app_context.push() + + def tearDown(self): + self.app_context.pop() + + def test_retrieve_trait_info_with_empty_dataset(self): + """Test that an exception is raised when dataset is empty""" + with self.assertRaises(AssertionError): + retrieve_trait_info(trait=mock.MagicMock(), + dataset={}) + + @mock.patch('base.trait.requests.get') + @mock.patch('base.trait.g', mock.Mock()) + def test_retrieve_trait_info_with_empty_trait_info(self, + requests_mock): + """Empty trait info""" + requests_mock.return_value = TestNilResponse() + with self.assertRaises(KeyError): + retrieve_trait_info(trait=mock.MagicMock(), + dataset=mock.MagicMock()) + + @mock.patch('base.trait.requests.get') + @mock.patch('base.trait.g', mock.Mock()) + def test_retrieve_trait_info_with_non_empty_trait_info(self, + requests_mock): + """Test that attributes are set""" + mock_dataset = mock.MagicMock() + requests_mock.return_value = TestResponse() + type(mock_dataset).display_fields = mock.PropertyMock( + return_value=["a", "b", "c", "d"]) + test_trait = retrieve_trait_info(trait=MockTrait(dataset=mock_dataset), + dataset=mock_dataset) + self.assertEqual(test_trait.a, 1) + self.assertEqual(test_trait.b, 2) + self.assertEqual(test_trait.c, 3) + self.assertEqual(test_trait.d, 4) + + @mock.patch('base.trait.requests.get') + @mock.patch('base.trait.g', mock.Mock()) + def test_retrieve_trait_info_utf8_parsing(self, + requests_mock): + """Test that utf-8 strings are parsed correctly""" + utf_8_string = "test_string" + mock_dataset = mock.MagicMock() + requests_mock.return_value = TestResponse() + type(mock_dataset).display_fields = mock.PropertyMock( + return_value=["a", "b", "c", "d"]) + type(mock_dataset).type = 'Publish' + + mock_trait = MockTrait( + dataset=mock_dataset, + pre_publication_description=utf_8_string + ) + trait_attrs = { + "group_code": "test_code", + "pre_publication_description": "test_pre_pub", + "pre_publication_abbreviation": "ファイルを画面毎に見て行くには、次のコマンドを使います。", + "post_publication_description": None, + "pubmed_id": None, + 'year': "2020", + "authors": "Jane Doe かいと", + } + for key, val in list(trait_attrs.items()): + setattr(mock_trait, key, val) + test_trait = retrieve_trait_info(trait=mock_trait, + dataset=mock_dataset) + self.assertEqual(test_trait.abbreviation, + "ファイルを画面毎に見て行くには、次のコマンドを使います。") + self.assertEqual(test_trait.authors, + "Jane Doe かいと") + + @mock.patch('base.trait.requests.get') + @mock.patch('base.trait.g') + @mock.patch('base.trait.get_resource_id') + def test_retrieve_trait_info_with_non_empty_lrs(self, + resource_id_mock, + g_mock, + requests_mock): + """Test retrieve trait info when lrs has a value""" + resource_id_mock.return_value = 1 + g_mock.db.execute.return_value.fetchone = mock.Mock() + g_mock.db.execute.return_value.fetchone.side_effect = [ + [1, 2, 3, 4], # trait_info = g.db.execute(query).fetchone() + [1, 2.37, 3, 4, 5], # trait_qtl = g.db.execute(query).fetchone() + [2.7333, 2.1204] # trait_info = g.db.execute(query).fetchone() + ] + requests_mock.return_value = None + + mock_dataset = mock.MagicMock() + type(mock_dataset).display_fields = mock.PropertyMock( + return_value=["a", "b", "c", "d"]) + type(mock_dataset).type = "ProbeSet" + type(mock_dataset).name = "RandomName" + + mock_trait = MockTrait( + dataset=mock_dataset, + pre_publication_description="test_string" + ) + trait_attrs = { + "description": "some description", + "probe_target_description": "some description", + "cellid": False, + "chr": 2.733, + "mb": 2.1204 + } + + for key, val in list(trait_attrs.items()): + setattr(mock_trait, key, val) + test_trait = retrieve_trait_info(trait=mock_trait, + dataset=mock_dataset, + get_qtl_info=True) + self.assertEqual(test_trait.LRS_score_repr, + "2.4") + + @mock.patch('base.trait.requests.get') + @mock.patch('base.trait.g') + @mock.patch('base.trait.get_resource_id') + def test_retrieve_trait_info_with_empty_lrs_field(self, + resource_id_mock, + g_mock, + requests_mock): + """Test retrieve trait info with empty lrs field""" + resource_id_mock.return_value = 1 + g_mock.db.execute.return_value.fetchone = mock.Mock() + g_mock.db.execute.return_value.fetchone.side_effect = [ + [1, 2, 3, 4], # trait_info = g.db.execute(query).fetchone() + [1, None, 3, 4, 5], # trait_qtl = g.db.execute(query).fetchone() + [2, 3] # trait_info = g.db.execute(query).fetchone() + ] + requests_mock.return_value = None + + mock_dataset = mock.MagicMock() + type(mock_dataset).display_fields = mock.PropertyMock( + return_value=["a", "b", "c", "d"]) + type(mock_dataset).type = "ProbeSet" + type(mock_dataset).name = "RandomName" + + mock_trait = MockTrait( + dataset=mock_dataset, + pre_publication_description="test_string" + ) + trait_attrs = { + "description": "some description", + "probe_target_description": "some description", + "cellid": False, + "chr": 2.733, + "mb": 2.1204 + } + + for key, val in list(trait_attrs.items()): + setattr(mock_trait, key, val) + test_trait = retrieve_trait_info(trait=mock_trait, + dataset=mock_dataset, + get_qtl_info=True) + self.assertEqual(test_trait.LRS_score_repr, + "N/A") + self.assertEqual(test_trait.LRS_location_repr, + "Chr2: 3.000000") + + @mock.patch('base.trait.requests.get') + @mock.patch('base.trait.g') + @mock.patch('base.trait.get_resource_id') + def test_retrieve_trait_info_with_empty_chr_field(self, + resource_id_mock, + g_mock, + requests_mock): + """Test retrieve trait info with empty chr field""" + resource_id_mock.return_value = 1 + g_mock.db.execute.return_value.fetchone = mock.Mock() + g_mock.db.execute.return_value.fetchone.side_effect = [ + [1, 2, 3, 4], # trait_info = g.db.execute(query).fetchone() + [1, 2, 3, 4, 5], # trait_qtl = g.db.execute(query).fetchone() + [None, 3] # trait_info = g.db.execute(query).fetchone() + ] + + requests_mock.return_value = None + + mock_dataset = mock.MagicMock() + type(mock_dataset).display_fields = mock.PropertyMock( + return_value=["a", "b", "c", "d"]) + type(mock_dataset).type = "ProbeSet" + type(mock_dataset).name = "RandomName" + + mock_trait = MockTrait( + dataset=mock_dataset, + pre_publication_description="test_string" + ) + trait_attrs = { + "description": "some description", + "probe_target_description": "some description", + "cellid": False, + "chr": 2.733, + "mb": 2.1204 + } + + for key, val in list(trait_attrs.items()): + setattr(mock_trait, key, val) + test_trait = retrieve_trait_info(trait=mock_trait, + dataset=mock_dataset, + get_qtl_info=True) + self.assertEqual(test_trait.LRS_score_repr, + "N/A") + self.assertEqual(test_trait.LRS_location_repr, + "N/A") diff --git a/wqflask/tests/unit/base/test_webqtl_case_data.py b/wqflask/tests/unit/base/test_webqtl_case_data.py new file mode 100644 index 00000000..8e8ba482 --- /dev/null +++ b/wqflask/tests/unit/base/test_webqtl_case_data.py @@ -0,0 +1,39 @@ +"""Tests for wqflask/base/webqtlCaseData.py""" +import unittest + +from wqflask import app # Required because of utility.tools in webqtlCaseData.py +from base.webqtlCaseData import webqtlCaseData + +class TestWebqtlCaseData(unittest.TestCase): + """Tests for WebqtlCaseData class""" + + def setUp(self): + self.w = webqtlCaseData(name="Test", + value=0, + variance=0.0, + num_cases=10, + name2="Test2") + + def test_webqtl_case_data_repr(self): + self.assertEqual( + repr(self.w), + " value=0.000 variance=0.000 ndata=10 name=Test name2=Test2" + ) + + def test_class_outlier(self): + self.assertEqual(self.w.class_outlier, "") + + def test_display_value(self): + self.assertEqual(self.w.display_value, "0.000") + self.w.value = None + self.assertEqual(self.w.display_value, "x") + + def test_display_variance(self): + self.assertEqual(self.w.display_variance, "0.000") + self.w.variance = None + self.assertEqual(self.w.display_variance, "x") + + def test_display_num_cases(self): + self.assertEqual(self.w.display_num_cases, "10") + self.w.num_cases = None + self.assertEqual(self.w.display_num_cases, "x") diff --git a/wqflask/tests/unit/utility/__init__.py b/wqflask/tests/unit/utility/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/wqflask/tests/unit/utility/test_authentication_tools.py b/wqflask/tests/unit/utility/test_authentication_tools.py new file mode 100644 index 00000000..5c391be5 --- /dev/null +++ b/wqflask/tests/unit/utility/test_authentication_tools.py @@ -0,0 +1,189 @@ +"""Tests for authentication tools""" +import unittest +from unittest import mock + +from utility.authentication_tools import check_resource_availability +from utility.authentication_tools import add_new_resource + + +class TestResponse: + """Mock Test Response after a request""" + @property + def content(self): + """Mock the content from Requests.get(params).content""" + return '["foo"]' + + +class TestUser: + """Mock user""" + @property + def user_id(self): + """Mockes user id. Used in Flask.g.user_session.user_id""" + return "Jane" + + +class TestUserSession: + """Mock user session""" + @property + def user_session(self): + """Mock user session. Mocks Flask.g.user_session object""" + return TestUser() + + +def mock_add_resource(resource_ob, update=False): + return resource_ob + + +class TestCheckResourceAvailability(unittest.TestCase): + """Test methods related to checking the resource availability""" + @mock.patch('utility.authentication_tools.add_new_resource') + @mock.patch('utility.authentication_tools.Redis') + @mock.patch('utility.authentication_tools.g', mock.Mock()) + @mock.patch('utility.authentication_tools.get_resource_id') + def test_check_resource_availability_default_mask( + self, + resource_id_mock, + redis_mock, + add_new_resource_mock): + """Test the resource availability with default mask""" + resource_id_mock.return_value = 1 + redis_mock.smembers.return_value = [] + test_dataset = mock.MagicMock() + type(test_dataset).type = mock.PropertyMock(return_value="Test") + add_new_resource_mock.return_value = {"default_mask": 2} + self.assertEqual(check_resource_availability(test_dataset), 2) + + @mock.patch('utility.authentication_tools.requests.get') + @mock.patch('utility.authentication_tools.add_new_resource') + @mock.patch('utility.authentication_tools.Redis') + @mock.patch('utility.authentication_tools.g', TestUserSession()) + @mock.patch('utility.authentication_tools.get_resource_id') + def test_check_resource_availability_non_default_mask( + self, + resource_id_mock, + redis_mock, + add_new_resource_mock, + requests_mock): + """Test the resource availability with default mask""" + resource_id_mock.return_value = 1 + redis_mock.smembers.return_value = [] + add_new_resource_mock.return_value = {"default_mask": 2} + requests_mock.return_value = TestResponse() + test_dataset = mock.MagicMock() + type(test_dataset).type = mock.PropertyMock(return_value="Test") + self.assertEqual(check_resource_availability(test_dataset), + ['foo']) + + @mock.patch('utility.authentication_tools.webqtlConfig.SUPER_PRIVILEGES', + "SUPERUSER") + @mock.patch('utility.authentication_tools.requests.get') + @mock.patch('utility.authentication_tools.add_new_resource') + @mock.patch('utility.authentication_tools.Redis') + @mock.patch('utility.authentication_tools.g', TestUserSession()) + @mock.patch('utility.authentication_tools.get_resource_id') + def test_check_resource_availability_of_super_user( + self, + resource_id_mock, + redis_mock, + add_new_resource_mock, + requests_mock): + """Test the resource availability if the user is the super user""" + resource_id_mock.return_value = 1 + redis_mock.smembers.return_value = ["Jane"] + add_new_resource_mock.return_value = {"default_mask": 2} + requests_mock.return_value = TestResponse() + test_dataset = mock.MagicMock() + type(test_dataset).type = mock.PropertyMock(return_value="Test") + self.assertEqual(check_resource_availability(test_dataset), + "SUPERUSER") + + @mock.patch('utility.authentication_tools.webqtlConfig.DEFAULT_PRIVILEGES', + "John Doe") + def test_check_resource_availability_string_dataset(self): + """Test the resource availability if the dataset is a string""" + self.assertEqual(check_resource_availability("Test"), + "John Doe") + + @mock.patch('utility.authentication_tools.webqtlConfig.DEFAULT_PRIVILEGES', + "John Doe") + def test_check_resource_availability_temp(self): + """Test the resource availability if the dataset is a string""" + test_dataset = mock.MagicMock() + type(test_dataset).type = mock.PropertyMock(return_value="Temp") + self.assertEqual(check_resource_availability(test_dataset), + "John Doe") + + +class TestAddNewResource(unittest.TestCase): + """Test cases for add_new_resource method""" + @mock.patch('utility.authentication_tools.webqtlConfig.DEFAULT_PRIVILEGES', + "John Doe") + @mock.patch('utility.authentication_tools.add_resource', mock_add_resource) + @mock.patch('utility.authentication_tools.get_group_code') + def test_add_new_resource_if_publish_datatype(self, group_code_mock): + """Test add_new_resource if dataset type is 'publish'""" + group_code_mock.return_value = "Test" + test_dataset = mock.MagicMock() + type(test_dataset).type = mock.PropertyMock(return_value="Publish") + type(test_dataset).id = mock.PropertyMock(return_value=10) + expected_value = { + "owner_id": "none", + "default_mask": "John Doe", + "group_masks": {}, + "name": "Test_None", + "data": { + "dataset": 10, + "trait": None + }, + "type": "dataset-publish" + } + self.assertEqual(add_new_resource(test_dataset), + expected_value) + + @mock.patch('utility.authentication_tools.webqtlConfig.DEFAULT_PRIVILEGES', + "John Doe") + @mock.patch('utility.authentication_tools.add_resource', mock_add_resource) + @mock.patch('utility.authentication_tools.get_group_code') + def test_add_new_resource_if_geno_datatype(self, group_code_mock): + """Test add_new_resource if dataset type is 'geno'""" + group_code_mock.return_value = "Test" + test_dataset = mock.MagicMock() + type(test_dataset).name = mock.PropertyMock(return_value="Geno") + type(test_dataset).type = mock.PropertyMock(return_value="Geno") + type(test_dataset).id = mock.PropertyMock(return_value=20) + expected_value = { + "owner_id": "none", + "default_mask": "John Doe", + "group_masks": {}, + "name": "Geno", + "data": { + "dataset": 20, + }, + "type": "dataset-geno" + } + self.assertEqual(add_new_resource(test_dataset), + expected_value) + + @mock.patch('utility.authentication_tools.webqtlConfig.DEFAULT_PRIVILEGES', + "John Doe") + @mock.patch('utility.authentication_tools.add_resource', mock_add_resource) + @mock.patch('utility.authentication_tools.get_group_code') + def test_add_new_resource_if_other_datatype(self, group_code_mock): + """Test add_new_resource if dataset type is not 'geno' or 'publish'""" + group_code_mock.return_value = "Test" + test_dataset = mock.MagicMock() + type(test_dataset).name = mock.PropertyMock(return_value="Geno") + type(test_dataset).type = mock.PropertyMock(return_value="other") + type(test_dataset).id = mock.PropertyMock(return_value=20) + expected_value = { + "owner_id": "none", + "default_mask": "John Doe", + "group_masks": {}, + "name": "Geno", + "data": { + "dataset": 20, + }, + "type": "dataset-probeset" + } + self.assertEqual(add_new_resource(test_dataset), + expected_value) diff --git a/wqflask/tests/unit/utility/test_chunks.py b/wqflask/tests/unit/utility/test_chunks.py new file mode 100644 index 00000000..8d90a1ec --- /dev/null +++ b/wqflask/tests/unit/utility/test_chunks.py @@ -0,0 +1,19 @@ +"""Test chunking""" + +import unittest + +from utility.chunks import divide_into_chunks + + +class TestChunks(unittest.TestCase): + "Test Utility method for chunking" + def test_divide_into_chunks(self): + "Check that a list is chunked correctly" + self.assertEqual(divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 3), + [[1, 2, 7], [3, 22, 8], [5, 22, 333]]) + self.assertEqual(divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 4), + [[1, 2, 7], [3, 22, 8], [5, 22, 333]]) + self.assertEqual(divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 5), + [[1, 2], [7, 3], [22, 8], [5, 22], [333]]) + self.assertEqual(divide_into_chunks([], 5), + [[]]) diff --git a/wqflask/tests/unit/utility/test_corestats.py b/wqflask/tests/unit/utility/test_corestats.py new file mode 100644 index 00000000..cf91a248 --- /dev/null +++ b/wqflask/tests/unit/utility/test_corestats.py @@ -0,0 +1,55 @@ +"""Test Core Stats""" + +import unittest + +from utility.corestats import Stats + + +class TestChunks(unittest.TestCase): + "Test Utility method for chunking" + + def setUp(self): + self.stat_test = Stats((x for x in range(1, 11))) + + def test_stats_sum(self): + """ Test sequence sum """ + self.assertEqual(self.stat_test.sum(), 55) + self.stat_test = Stats([]) + self.assertEqual(self.stat_test.sum(), None) + + def test_stats_count(self): + """ Test sequence count """ + self.assertEqual(self.stat_test.count(), 10) + self.stat_test = Stats([]) + self.assertEqual(self.stat_test.count(), 0) + + def test_stats_min(self): + """ Test min value in sequence""" + self.assertEqual(self.stat_test.min(), 1) + self.stat_test = Stats([]) + self.assertEqual(self.stat_test.min(), None) + + def test_stats_max(self): + """ Test max value in sequence """ + self.assertEqual(self.stat_test.max(), 10) + self.stat_test = Stats([]) + self.assertEqual(self.stat_test.max(), None) + + def test_stats_avg(self): + """ Test avg of sequence """ + self.assertEqual(self.stat_test.avg(), 5.5) + self.stat_test = Stats([]) + self.assertEqual(self.stat_test.avg(), None) + + def test_stats_stdev(self): + """ Test standard deviation of sequence """ + self.assertEqual(self.stat_test.stdev(), 3.0276503540974917) + self.stat_test = Stats([]) + self.assertEqual(self.stat_test.stdev(), None) + + def test_stats_percentile(self): + """ Test percentile of sequence """ + self.assertEqual(self.stat_test.percentile(20), 3.0) + self.assertEqual(self.stat_test.percentile(101), None) + self.stat_test = Stats([]) + self.assertEqual(self.stat_test.percentile(20), None) diff --git a/wqflask/tests/unit/utility/test_corr_result_helpers.py b/wqflask/tests/unit/utility/test_corr_result_helpers.py new file mode 100644 index 00000000..e196fbdf --- /dev/null +++ b/wqflask/tests/unit/utility/test_corr_result_helpers.py @@ -0,0 +1,32 @@ +""" Test correlation helper methods """ + +import unittest +from utility.corr_result_helpers import normalize_values, common_keys, normalize_values_with_samples + + +class TestCorrelationHelpers(unittest.TestCase): + """Test methods for normalising lists""" + + def test_normalize_values(self): + """Test that a list is normalised correctly""" + self.assertEqual( + normalize_values([2.3, None, None, 3.2, 4.1, 5], [ + 3.4, 7.2, 1.3, None, 6.2, 4.1]), + ([2.3, 4.1, 5], [3.4, 6.2, 4.1], 3) + ) + + def test_common_keys(self): + """Test that common keys are returned as a list""" + a = dict(BXD1=9.113, BXD2=9.825, BXD14=8.985, BXD15=9.300) + b = dict(BXD1=9.723, BXD3=9.825, BXD14=9.124, BXD16=9.300) + self.assertEqual(sorted(common_keys(a, b)), ['BXD1', 'BXD14']) + + def test_normalize_values_with_samples(self): + """Test that a sample(dict) is normalised correctly""" + self.assertEqual( + normalize_values_with_samples( + dict(BXD1=9.113, BXD2=9.825, BXD14=8.985, + BXD15=9.300, BXD20=9.300), + dict(BXD1=9.723, BXD3=9.825, BXD14=9.124, BXD16=9.300)), + (({'BXD1': 9.113, 'BXD14': 8.985}, {'BXD1': 9.723, 'BXD14': 9.124}, 2)) + ) diff --git a/wqflask/tests/unit/utility/test_formatting.py b/wqflask/tests/unit/utility/test_formatting.py new file mode 100644 index 00000000..9d3033d1 --- /dev/null +++ b/wqflask/tests/unit/utility/test_formatting.py @@ -0,0 +1,33 @@ +import unittest +from utility.formatting import numify, commify + + +class TestFormatting(unittest.TestCase): + """Test formatting numbers by numifying or commifying""" + + def test_numify(self): + "Test that a number is correctly converted to a English readable string" + self.assertEqual(numify(1, 'item', 'items'), + 'one item') + self.assertEqual(numify(2, 'book'), 'two') + self.assertEqual(numify(2, 'book', 'books'), 'two books') + self.assertEqual(numify(0, 'book', 'books'), 'zero books') + self.assertEqual(numify(0), 'zero') + self.assertEqual(numify(5), 'five') + self.assertEqual(numify(14, 'book', 'books'), '14 books') + self.assertEqual(numify(999, 'book', 'books'), '999 books') + self.assertEqual(numify(1000000, 'book', 'books'), '1,000,000 books') + self.assertEqual(numify(1956), '1956') + + def test_commify(self): + "Test that commas are added correctly" + self.assertEqual(commify(1), '1') + self.assertEqual(commify(123), '123') + self.assertEqual(commify(1234), '1234') + self.assertEqual(commify(12345), '12,345') + self.assertEqual(commify(1234567890), '1,234,567,890') + self.assertEqual(commify(123.0), '123.0') + self.assertEqual(commify(1234.5), '1234.5') + self.assertEqual(commify(1234.56789), '1234.56789') + self.assertEqual(commify(123456.789), '123,456.789') + self.assertEqual(commify(None), None) diff --git a/wqflask/tests/unit/utility/test_hmac.py b/wqflask/tests/unit/utility/test_hmac.py new file mode 100644 index 00000000..4e3652f8 --- /dev/null +++ b/wqflask/tests/unit/utility/test_hmac.py @@ -0,0 +1,52 @@ +# -*- coding: utf-8 -*- +"""Test hmac utility functions""" + +import unittest +from unittest import mock + +from utility.hmac import data_hmac +from utility.hmac import url_for_hmac +from utility.hmac import hmac_creation + + +class TestHmacUtil(unittest.TestCase): + """Test Utility method for hmac creation""" + + @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) + def test_hmac_creation(self): + """Test hmac creation with a utf-8 string""" + self.assertEqual(hmac_creation("ファイ"), "7410466338cfe109e946") + + @mock.patch("utility.hmac.app.config", + {'SECRET_HMAC_CODE': ('\x08\xdf\xfa\x93N\x80' + '\xd9\\H@\\\x9f`\x98d^' + '\xb4a;\xc6OM\x946a\xbc' + '\xfc\x80:*\xebc')}) + def test_hmac_creation_with_cookie(self): + """Test hmac creation with a cookie""" + cookie = "3f4c1dbf-5b56-4260-87d6-f35445bda37e:af4fcf5eace9e7c864ce" + uuid_, _, signature = cookie.partition(":") + self.assertEqual( + hmac_creation(uuid_), + "af4fcf5eace9e7c864ce") + + @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) + def test_data_hmac(self): + """Test data_hmac fn with a utf-8 string""" + self.assertEqual(data_hmac("ファイ"), "ファイ:7410466338cfe109e946") + + @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) + @mock.patch("utility.hmac.url_for") + def test_url_for_hmac_with_plain_url(self, mock_url): + """Test url_for_hmac without params""" + mock_url.return_value = "https://mock_url.com/ファイ/" + self.assertEqual(url_for_hmac("ファイ"), + "https://mock_url.com/ファイ/?hm=05bc39e659b1948f41e7") + + @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) + @mock.patch("utility.hmac.url_for") + def test_url_for_hmac_with_param_in_url(self, mock_url): + """Test url_for_hmac with params""" + mock_url.return_value = "https://mock_url.com/?ファイ=1" + self.assertEqual(url_for_hmac("ファイ"), + "https://mock_url.com/?ファイ=1&hm=4709c1708270644aed79") diff --git a/wqflask/tests/unit/wqflask/__init__.py b/wqflask/tests/unit/wqflask/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/wqflask/tests/unit/wqflask/api/__init__.py b/wqflask/tests/unit/wqflask/api/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/wqflask/tests/unit/wqflask/api/test_gen_menu.py b/wqflask/tests/unit/wqflask/api/test_gen_menu.py new file mode 100644 index 00000000..84898bd1 --- /dev/null +++ b/wqflask/tests/unit/wqflask/api/test_gen_menu.py @@ -0,0 +1,413 @@ +"""Test cases for wqflask.api.gen_menu""" +import unittest +from unittest import mock + +from wqflask import app +from wqflask.api.gen_menu import gen_dropdown_json +from wqflask.api.gen_menu import get_species +from wqflask.api.gen_menu import get_groups +from wqflask.api.gen_menu import get_types +from wqflask.api.gen_menu import get_datasets +from wqflask.api.gen_menu import phenotypes_exist +from wqflask.api.gen_menu import genotypes_exist +from wqflask.api.gen_menu import build_datasets +from wqflask.api.gen_menu import build_types + + +class TestGenMenu(unittest.TestCase): + """Tests for the gen_menu module""" + + def setUp(self): + self.app_context = app.app_context() + self.app_context.push() + self.test_group = { + 'mouse': [ + ['H_T1', + 'H_T', + 'Family:DescriptionA' + ], + ['H_T2', "H_T'", 'Family:None'] + ], + 'human': [ + ['BXD', 'BXD', 'Family:None'], + ['HLC', 'Liver: Normal Gene Expression with Genotypes (Merck)', + 'Family:Test'] + ] + } + + self.test_type = { + 'mouse': { + 'H_T2': [('Phenotypes', + 'Traits and Cofactors', + 'Phenotypes'), + ('Genotypes', + 'DNA Markers and SNPs', + 'Genotypes'), + ['M', 'M', 'Molecular Trait Datasets']], + 'H_T1': [('Phenotypes', + 'Traits and Cofactors', + 'Phenotypes'), + ('Genotypes', + 'DNA Markers and SNPs', + 'Genotypes'), + ['M', 'M', 'Molecular Trait Datasets']] + }, + 'human': { + 'HLC': [('Phenotypes', + 'Traits and Cofactors', + 'Phenotypes'), + ('Genotypes', + 'DNA Markers and SNPs', + 'Genotypes'), + ['M', 'M', 'Molecular Trait Datasets']], + 'BXD': [('Phenotypes', + 'Traits and Cofactors', + 'Phenotypes'), + ('Genotypes', + 'DNA Markers and SNPs', + 'Genotypes'), + ['M', 'M', 'Molecular Trait Datasets']] + } + } + + def tearDown(self): + self.app_context.pop() + + @mock.patch('wqflask.api.gen_menu.g') + def test_get_species(self, db_mock): + """Test that assertion is raised when dataset and dataset_name + are defined""" + db_mock.db.execute.return_value.fetchall.return_value = ( + ('human', 'Human'), + ('mouse', 'Mouse')) + self.assertEqual(get_species(), + [['human', 'Human'], ['mouse', 'Mouse']]) + db_mock.db.execute.assert_called_once_with( + "SELECT Name, MenuName FROM Species ORDER BY OrderId" + ) + + @mock.patch('wqflask.api.gen_menu.g') + def test_get_groups(self, db_mock): + """Test that species groups are grouped correctly""" + db_mock.db.execute.return_value.fetchall.side_effect = [ + # Mouse + (('BXD', 'BXD', None), + ('HLC', 'Liver: Normal Gene Expression with Genotypes (Merck)', + 'Test')), + # Human + (('H_T1', "H_T", "DescriptionA"), + ('H_T2', "H_T'", None)) + ] + + self.assertEqual(get_groups([["human", "Human"], ["mouse", "Mouse"]]), + self.test_group) + + for name in ["mouse", "human"]: + db_mock.db.execute.assert_any_call( + ("SELECT InbredSet.Name, InbredSet.FullName, " + + "IFNULL(InbredSet.Family, 'None') " + + "FROM InbredSet, Species WHERE Species.Name " + + "= '{}' AND InbredSet.SpeciesId = Species.Id GROUP by " + + "InbredSet.Name ORDER BY IFNULL(InbredSet.FamilyOrder, " + + "InbredSet.FullName) ASC, IFNULL(InbredSet.Family, " + + "InbredSet.FullName) ASC, InbredSet.FullName ASC, " + + "InbredSet.MenuOrderId ASC").format(name) + ) + + @mock.patch('wqflask.api.gen_menu.g') + def test_phenotypes_exist_called_with_correct_query(self, db_mock): + """Test that phenotypes_exist is called with the correct query""" + db_mock.db.execute.return_value.fetchone.return_value = None + phenotypes_exist("test") + db_mock.db.execute.assert_called_with( + "SELECT Name FROM PublishFreeze " + "WHERE PublishFreeze.Name = 'testPublish'" + ) + + @mock.patch('wqflask.api.gen_menu.g') + def test_phenotypes_exist_with_falsy_values(self, db_mock): + """Test that phenotype check returns correctly when given + a None value""" + for x in [None, False, (), [], ""]: + db_mock.db.execute.return_value.fetchone.return_value = x + self.assertFalse(phenotypes_exist("test")) + + @mock.patch('wqflask.api.gen_menu.g') + def test_phenotypes_exist_with_truthy_value(self, db_mock): + """Test that phenotype check returns correctly when given Truthy """ + for x in ["x", ("result"), ["result"], [1]]: + db_mock.db.execute.return_value.fetchone.return_value = (x) + self.assertTrue(phenotypes_exist("test")) + + @mock.patch('wqflask.api.gen_menu.g') + def test_genotypes_exist_called_with_correct_query(self, db_mock): + """Test that genotypes_exist is called with the correct query""" + db_mock.db.execute.return_value.fetchone.return_value = None + genotypes_exist("test") + db_mock.db.execute.assert_called_with( + "SELECT Name FROM GenoFreeze WHERE GenoFreeze.Name = 'testGeno'" + ) + + @mock.patch('wqflask.api.gen_menu.g') + def test_genotypes_exist_with_falsy_values(self, db_mock): + """Test that genotype check returns correctly when given + a None value""" + for x in [None, False, (), [], ""]: + db_mock.db.execute.return_value.fetchone.return_value = x + self.assertFalse(genotypes_exist("test")) + + @mock.patch('wqflask.api.gen_menu.g') + def test_genotypes_exist_with_truthy_value(self, db_mock): + """Test that genotype check returns correctly when given Truthy """ + for x in ["x", ("result"), ["result"], [1]]: + db_mock.db.execute.return_value.fetchone.return_value = (x) + self.assertTrue(phenotypes_exist("test")) + + @mock.patch('wqflask.api.gen_menu.g') + def test_build_datasets_with_type_phenotypes(self, db_mock): + """Test that correct dataset is returned for a phenotype type""" + db_mock.db.execute.return_value.fetchall.return_value = ( + (602, "BXDPublish", "BXD Published Phenotypes"), + ) + self.assertEqual(build_datasets("Mouse", "BXD", "Phenotypes"), + [['602', "BXDPublish", "BXD Published Phenotypes"]]) + db_mock.db.execute.assert_called_with( + "SELECT InfoFiles.GN_AccesionId, PublishFreeze.Name, " + + "PublishFreeze.FullName FROM InfoFiles, PublishFreeze, " + + "InbredSet WHERE InbredSet.Name = 'BXD' AND " + + "PublishFreeze.InbredSetId = InbredSet.Id AND " + + "InfoFiles.InfoPageName = PublishFreeze.Name " + + "ORDER BY PublishFreeze.CreateTime ASC" + ) + self.assertEqual(build_datasets("Mouse", "MDP", "Phenotypes"), + [['602', "BXDPublish", "Mouse Phenome Database"]]) + + db_mock.db.execute.return_value.fetchall.return_value = () + db_mock.db.execute.return_value.fetchone.return_value = ( + "BXDPublish", "Mouse Phenome Database" + ) + self.assertEqual(build_datasets("Mouse", "MDP", "Phenotypes"), + [["None", "BXDPublish", "Mouse Phenome Database"]]) + + @mock.patch('wqflask.api.gen_menu.g') + def test_build_datasets_with_type_phenotypes_and_no_results(self, db_mock): + """Test that correct dataset is returned for a phenotype type with no + results + + """ + db_mock.db.execute.return_value.fetchall.return_value = None + db_mock.db.execute.return_value.fetchone.return_value = (121, + "text value") + self.assertEqual(build_datasets("Mouse", "BXD", "Phenotypes"), + [["None", "121", "text value"]]) + db_mock.db.execute.assert_called_with( + "SELECT PublishFreeze.Name, PublishFreeze.FullName " + "FROM PublishFreeze, InbredSet " + "WHERE InbredSet.Name = 'BXD' AND " + "PublishFreeze.InbredSetId = InbredSet.Id " + "ORDER BY PublishFreeze.CreateTime ASC" + ) + + @mock.patch('wqflask.api.gen_menu.g') + def test_build_datasets_with_type_genotypes(self, db_mock): + """Test that correct dataset is returned for a phenotype type""" + db_mock.db.execute.return_value.fetchone.return_value = ( + 635, "HLCPublish", "HLC Published Genotypes" + ) + + self.assertEqual(build_datasets("Mouse", "HLC", "Genotypes"), + [["635", "HLCGeno", "HLC Genotypes"]]) + db_mock.db.execute.assert_called_with( + "SELECT InfoFiles.GN_AccesionId FROM InfoFiles, " + "GenoFreeze, InbredSet WHERE InbredSet.Name = 'HLC' AND " + "GenoFreeze.InbredSetId = InbredSet.Id AND " + "InfoFiles.InfoPageName = GenoFreeze.ShortName " + + "ORDER BY GenoFreeze.CreateTime DESC" + ) + db_mock.db.execute.return_value.fetchone.return_value = () + self.assertEqual(build_datasets("Mouse", "HLC", "Genotypes"), + [["None", "HLCGeno", "HLC Genotypes"]]) + + @mock.patch('wqflask.api.gen_menu.g') + def test_build_datasets_with_type_mrna(self, db_mock): + """Test that correct dataset is returned for a mRNA + expression/ Probeset""" + db_mock.db.execute.return_value.fetchall.return_value = ( + (112, "HC_M2_0606_P", + "Hippocampus Consortium M430v2 (Jun06) PDNN"), ) + self.assertEqual(build_datasets("Mouse", "HLC", "mRNA"), [[ + "112", 'HC_M2_0606_P', "Hippocampus Consortium M430v2 (Jun06) PDNN" + ]]) + db_mock.db.execute.assert_called_once_with( + "SELECT ProbeSetFreeze.Id, ProbeSetFreeze.Name, " + + "ProbeSetFreeze.FullName FROM ProbeSetFreeze, " + + "ProbeFreeze, InbredSet, Tissue, Species WHERE " + + "Species.Name = 'Mouse' AND Species.Id = " + + "InbredSet.SpeciesId AND InbredSet.Name = 'HLC' AND " + + "ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and " + + "Tissue.Name = 'mRNA' AND ProbeFreeze.TissueId = " + + "Tissue.Id and ProbeFreeze.InbredSetId = InbredSet.Id " + + "ORDER BY ProbeSetFreeze.CreateTime DESC") + + @mock.patch('wqflask.api.gen_menu.build_datasets') + @mock.patch('wqflask.api.gen_menu.g') + def test_build_types(self, db_mock, datasets_mock): + """Test that correct tissue metadata is returned""" + datasets_mock.return_value = [ + ["112", 'HC_M2_0606_P', + "Hippocampus Consortium M430v2 (Jun06) PDNN"] + ] + db_mock.db.execute.return_value.fetchall.return_value = ( + ('Mouse Tissue'), ('Human Tissue'), ('Rat Tissue') + ) + self.assertEqual(build_types('mouse', 'random group'), + [['M', 'M', 'Molecular Traits'], + ['H', 'H', 'Molecular Traits'], + ['R', 'R', 'Molecular Traits']]) + db_mock.db.execute.assert_called_once_with( + "SELECT DISTINCT Tissue.Name " + + "FROM ProbeFreeze, ProbeSetFreeze, InbredSet, " + + "Tissue, Species WHERE Species.Name = 'mouse' " + + "AND Species.Id = InbredSet.SpeciesId AND " + + "InbredSet.Name = 'random group' AND " + + "ProbeFreeze.TissueId = Tissue.Id AND " + + "ProbeFreeze.InbredSetId = InbredSet.Id AND " + + "ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id " + + "ORDER BY Tissue.Name" + ) + + @mock.patch('wqflask.api.gen_menu.build_types') + @mock.patch('wqflask.api.gen_menu.genotypes_exist') + @mock.patch('wqflask.api.gen_menu.phenotypes_exist') + def test_get_types_with_existing_genotype_and_phenotypes( + self, + phenotypes_exist_mock, + genotypes_exist_mock, + build_types_mock): + """Test that build types are constructed correctly if phenotypes and genotypes + exist + + """ + phenotypes_exist_mock.return_value = True + genotypes_exist_mock.return_value = True + + expected_result = self.test_type + + build_types_mock.return_value = [ + ['M', 'M', 'Molecular Trait Datasets'] + ] + self.assertEqual(get_types(self.test_group), expected_result) + + @mock.patch('wqflask.api.gen_menu.build_types') + @mock.patch('wqflask.api.gen_menu.genotypes_exist') + @mock.patch('wqflask.api.gen_menu.phenotypes_exist') + def test_get_types_with_buildtype_and_non_existent_genotype_and_phenotypes( + self, + phenotypes_exist_mock, + genotypes_exist_mock, + build_types_mock): + """Test that build types are constructed correctly if phenotypes_exist and + genotypes_exist are false but build_type is falsy + + """ + phenotypes_exist_mock.return_value = False + genotypes_exist_mock.return_value = False + + build_types_mock.return_value = [] + self.assertEqual(get_types(self.test_group), { + 'mouse': {}, + 'human': {} + }) + + @mock.patch('wqflask.api.gen_menu.build_types') + @mock.patch('wqflask.api.gen_menu.genotypes_exist') + @mock.patch('wqflask.api.gen_menu.phenotypes_exist') + def test_get_types_with_non_existent_genotype_phenotypes_and_buildtype( + self, + phenotypes_exist_mock, + genotypes_exist_mock, + build_types_mock): + """Test that build types are constructed correctly if phenotypes_exist, + genotypes_exist and build_types are truthy + + """ + phenotypes_exist_mock.return_value = False + genotypes_exist_mock.return_value = False + + build_types_mock.return_value = [ + ['M', 'M', 'Molecular Trait Datasets'] + ] + expected_result = { + 'mouse': { + 'H_T2': [['M', 'M', 'Molecular Trait Datasets']], + 'H_T1': [['M', 'M', 'Molecular Trait Datasets']]}, + 'human': { + 'HLC': [['M', 'M', 'Molecular Trait Datasets']], + 'BXD': [['M', 'M', 'Molecular Trait Datasets']]}} + self.assertEqual(get_types(self.test_group), + expected_result) + + @mock.patch('wqflask.api.gen_menu.build_datasets') + def test_get_datasets_with_existent_datasets(self, + build_datasets_mock): + """Test correct dataset is returned with existent build_datasets""" + build_datasets_mock.return_value = "Test" + expected_result = { + 'mouse': { + 'H_T2': {'Genotypes': 'Test', + 'M': 'Test', + 'Phenotypes': 'Test'}, + 'H_T1': {'Genotypes': 'Test', + 'M': 'Test', + 'Phenotypes': 'Test'}}, + 'human': {'HLC': {'Genotypes': 'Test', + 'M': 'Test', + 'Phenotypes': 'Test'}, + 'BXD': {'Genotypes': 'Test', + 'M': 'Test', + 'Phenotypes': 'Test'}}} + self.assertEqual(get_datasets(self.test_type), + expected_result) + + @mock.patch('wqflask.api.gen_menu.build_datasets') + def test_get_datasets_with_non_existent_datasets(self, + build_datasets_mock): + """Test correct dataset is returned with non-existent build_datasets""" + build_datasets_mock.return_value = None + expected_result = { + 'mouse': { + 'H_T2': {}, + 'H_T1': {}}, + 'human': {'HLC': {}, + 'BXD': {}}} + self.assertEqual(get_datasets(self.test_type), + expected_result) + + @mock.patch('wqflask.api.gen_menu.get_datasets') + @mock.patch('wqflask.api.gen_menu.get_types') + @mock.patch('wqflask.api.gen_menu.get_groups') + @mock.patch('wqflask.api.gen_menu.get_species') + def test_gen_dropdown_json(self, + species_mock, + groups_mock, + types_mock, + datasets_mock): + "Test that the correct dictionary is constructed properly" + species_mock.return_value = ("speciesA speciesB speciesC speciesD" + .split(" ")) + datasets_mock.return_value = ("datasetA datasetB datasetC datasetD" + .split(" ")) + groups_mock.return_value = ("groupA groupB groupC groupD" + .split(" ")) + types_mock.return_value = ("typeA typeB typeC typeD" + .split(" ")) + datasets_mock.return_value = ("datasetA datasetB datasetC datasetD" + .split(" ")) + + expected_result = { + 'datasets': ['datasetA', 'datasetB', 'datasetC', 'datasetD'], + 'types': ['typeA', 'typeB', 'typeC', 'typeD'], + 'groups': ['groupA', 'groupB', 'groupC', 'groupD'], + 'species': ['speciesA', 'speciesB', 'speciesC', 'speciesD']} + + self.assertEqual(gen_dropdown_json(), expected_result) diff --git a/wqflask/tests/unit/wqflask/marker_regression/__init__.py b/wqflask/tests/unit/wqflask/marker_regression/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_display_mapping_results.py b/wqflask/tests/unit/wqflask/marker_regression/test_display_mapping_results.py new file mode 100644 index 00000000..8ae0f09f --- /dev/null +++ b/wqflask/tests/unit/wqflask/marker_regression/test_display_mapping_results.py @@ -0,0 +1,156 @@ +import unittest + +import htmlgen as HT +from wqflask.marker_regression.display_mapping_results import ( + DisplayMappingResults, + HtmlGenWrapper +) + + +class TestDisplayMappingResults(unittest.TestCase): + """Basic Methods to test Mapping Results""" + def test_pil_colors(self): + """Test that colors use PILLOW color format""" + self.assertEqual(DisplayMappingResults.CLICKABLE_WEBQTL_REGION_COLOR, + (245, 211, 211)) + + +class TestHtmlGenWrapper(unittest.TestCase): + """Test Wrapper around HTMLGen""" + def test_create_image(self): + """Test HT.Image method""" + self.assertEqual( + str(HtmlGenWrapper.create_image_tag(src="test.png", + alt="random", + border="0", + width="10", + height="13", + usemap="#webqtlmap")), + ("""random""") + ) + + def test_create_form(self): + """Test HT.Form method""" + test_form = HtmlGenWrapper.create_form_tag( + cgi="/testing/", + enctype='multipart/form-data', + name="formName", + submit=HtmlGenWrapper.create_input_tag(type_='hidden', name='Default_Name') + ) + test_image = HtmlGenWrapper.create_image_tag( + src="test.png", + alt="random", + border="0", + width="10", + height="13", + usemap="#webqtlmap" + ) + self.assertEqual( + str(test_form).replace("\n", ""), + ("""
""")) + hddn = { + 'FormID': 'showDatabase', + 'ProbeSetID': '_', + 'database': "TestGeno", + 'CellID': '_', + 'RISet': "Test", + 'incparentsf1': 'ON' + } + for key in hddn.keys(): + test_form.append( + HtmlGenWrapper.create_input_tag( + name=key, + value=hddn[key], + type_='hidden')) + test_form.append(test_image) + + self.assertEqual(str(test_form).replace("\n", ""), ( + """
""" + """""" + """""" + """""" + """""" + """""" + """""" + """""" + """random""" + """
""")) + + def test_create_paragraph(self): + """Test HT.Paragraph method""" + test_p_element = HtmlGenWrapper.create_p_tag(id="smallSize") + par_text = ( + "Mapping using genotype data as " + "a trait will result in infinity LRS at one locus. " + "In order to display the result properly, all LRSs " + "higher than 100 are capped at 100." + ) + self.assertEqual( + str(test_p_element), + """

""" + ) + test_p_element.append(HtmlGenWrapper.create_br_tag()) + test_p_element.append(par_text) + self.assertEqual( + str(test_p_element), + """


{}

""".format(par_text) + ) + + def test_create_br_tag(self): + """Test HT.BR() method""" + self.assertEqual(str(HtmlGenWrapper.create_br_tag()), + "
") + + def test_create_input_tag(self): + """Test HT.Input method""" + self.assertEqual( + str(HtmlGenWrapper.create_input_tag( + type_="hidden", + name="name", + value="key", + Class="trait trait_")).replace("\n", ""), + ("""""")) + + def test_create_map_tag(self): + """Test HT.Map method""" + self.assertEqual(str(HtmlGenWrapper.create_map_tag( + name="WebqTLImageMap")).replace("\n", ""), + """""") + gifmap = HtmlGenWrapper.create_map_tag(name="test") + gifmap.append(HtmlGenWrapper.create_area_tag(shape="rect", + coords='1 2 3', href='#area1')) + gifmap.append(HtmlGenWrapper.create_area_tag(shape="rect", + coords='1 2 3', href='#area2')) + self.assertEqual( + str(gifmap).replace("\n", ""), + ("""""" + """""" + """""" + """""")) + + def test_create_area_tag(self): + """Test HT.Area method""" + self.assertEqual( + str(HtmlGenWrapper.create_area_tag( + shape="rect", + coords="1 2", + href="http://test.com", + title="Some Title")).replace("\n", ""), + ("""""")) + + def test_create_link_tag(self): + """Test HT.HREF method""" + self.assertEqual( + str(HtmlGenWrapper.create_link_tag( + "www.test.com", "test", target="_blank")).replace("\n", ""), + """test""") diff --git a/wqflask/tests/unit/wqflask/show_trait/__init__.py b/wqflask/tests/unit/wqflask/show_trait/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/wqflask/tests/unit/wqflask/show_trait/test_export_trait_data.py b/wqflask/tests/unit/wqflask/show_trait/test_export_trait_data.py new file mode 100644 index 00000000..41761944 --- /dev/null +++ b/wqflask/tests/unit/wqflask/show_trait/test_export_trait_data.py @@ -0,0 +1,212 @@ +import unittest +from unittest import mock +from wqflask.show_trait.export_trait_data import dict_to_sorted_list +from wqflask.show_trait.export_trait_data import cmp_samples +from wqflask.show_trait.export_trait_data import export_sample_table +from wqflask.show_trait.export_trait_data import get_export_metadata + + +class AttributesSetter: + def __init__(self, obj): + for key, value in obj.items(): + setattr(self, key, value) + + +class TestExportTraits(unittest.TestCase): + """Test methods related to converting dict to sortedlist""" + @mock.patch("wqflask.show_trait.export_trait_data.create_trait") + @mock.patch("wqflask.show_trait.export_trait_data.data_set") + def test_get_export_metadata_no_publish(self, mock_dataset, mock_trait): + """test for exporting metadata with no publish""" + mock_dataset_attributes = AttributesSetter( + {"type": "no_publish", "dataset_name": "Temp", "name": "Temp"}) + + mock_nested_attributes = AttributesSetter({"name": "name"}) + mock_dataset_attributes.group = mock_nested_attributes + mock_dataset.create_dataset.return_value = mock_dataset_attributes + mock_trait.return_value = AttributesSetter({"symbol": "", "description_display": "Description", + "title": "research1", "journal": "", "authors": ""}) + + results = get_export_metadata("random_id", "Temp") + expected = [["Record ID: random_id"], + ["Trait URL: http://genenetwork.org/show_trait?trait_id=random_id&dataset=Temp"], + ["Dataset: Temp"], + ["Group: name"], []] + + mock_dataset.create_dataset.assert_called_with("Temp") + mock_trait.assert_called_with( + dataset=mock_dataset_attributes, name="random_id", cellid=None, get_qtl_info=False) + self.assertEqual(results, expected) + + @mock.patch("wqflask.show_trait.export_trait_data.create_trait") + @mock.patch("wqflask.show_trait.export_trait_data.data_set") + def test_get_export_metadata_with_publish(self, data_mock, trait_mock): + """test for exporting metadata with dataset.type=Publish""" + mock_dataset_attributes = AttributesSetter({"type": "Publish", "dataset_name": "Temp", + "name": "Temp", "description_display": "Description goes here"}) + + mock_nested_attributes = AttributesSetter({"name": "name"}) + mock_dataset_attributes.group = mock_nested_attributes + data_mock.create_dataset.return_value = mock_dataset_attributes + trait_instance = AttributesSetter({"symbol": "", "description_display": "Description", + "title": "research1", "journal": "", "authors": ""}) + trait_mock.return_value = trait_instance + + results = get_export_metadata( + "29ae0615-0d77-4814-97c7-c9e91f6bfd7b", "Temp") + + expected = [['Phenotype ID: 29ae0615-0d77-4814-97c7-c9e91f6bfd7b'], + ['Phenotype URL: http://genenetwork.org/show_trait?trait_id=29ae0615-0d77-4814-97c7-c9e91f6bfd7b&dataset=Temp'], [ + 'Group: name'], ['Phenotype: Description'], + ['Authors: N/A'], ['Title: research1'], + ['Journal: N/A'], ['Dataset Link: http://gn1.genenetwork.org/webqtl/main.py?FormID=sharinginfo&InfoPageName=Temp'], []] + + self.assertEqual(results, expected) + + @mock.patch("wqflask.show_trait.export_trait_data.dict_to_sorted_list") + @mock.patch("wqflask.show_trait.export_trait_data.get_export_metadata") + def test_export_sample_table(self, exp_metadata, dict_list): + """test for exporting sample table""" + targs_obj = { + "export_data": """{ + "primary_samples": [ + { + "other": "germanotta", + "name": "Sauroniops", + "se":{ + "name":"S2" + }, + "num_cases":{ + "k1":"value" + + } + } + ], + "other_samples": [ + { + "se": 1, + "num_cases": 4, + "value": 6, + "name": 3 + } + ] + }""", + "trait_display_name": "Hair_color", + "trait_id": "23177fdc-312e-4084-ad0c-f3eae785fff5", + "dataset": { + } + } + exp_metadata.return_value = [ + ["Phenotype ID:0a2be192-57f5-400b-bbbd-0cf50135995f"], ['Group:gp1'], + ["Phenotype:p1"], [ + "Authors:N/A"], + ["Title:research1"], + ["Journal:N/A"], + ["Dataset Link: http://gn1.genenetwork.org/webqtl/main.py?FormID=sharinginfo&InfoPageName=name1"], []] + expected = ('Hair_color', + [['Phenotype ID:0a2be192-57f5-400b-bbbd-0cf50135995f'], + ['Group:gp1'], + ['Phenotype:p1'], + ['Authors:N/A'], + ['Title:research1'], + ['Journal:N/A'], + ['Dataset Link: ' + 'http://gn1.genenetwork.org/webqtl/main.py?FormID=sharinginfo&InfoPageName=name1'], + [], + ['Name', 'Value', 'SE', 'N'], + ['Sauroniops', 'germanotta'], + [3, 6, 1, 4]]) + + dict_list.side_effect = [['Sauroniops', 'germanotta'], [3, 6, 1, 4]] + + self.assertEqual(export_sample_table(targs_obj), expected) + exp_metadata.assert_called_with( + "23177fdc-312e-4084-ad0c-f3eae785fff5", {}) + self.assertEqual(dict_list.call_count, 2) + + def test_dict_to_sortedlist(self): + """test for conversion of dict to sorted list""" + sample1 = { + "other": "exp1", + "name": "exp2" + } + sample2 = { + "se": 1, + "num_cases": 4, + "value": 6, + "name": 3 + + } + rever = { + "name": 3, + "value": 6, + "num_cases": 4, + "se": 1 + } + oneItem = { + "item1": "one" + } + + self.assertEqual(["exp2", "exp1"], dict_to_sorted_list(sample1)) + self.assertEqual([3, 6, 1, 4], dict_to_sorted_list(sample2)) + self.assertEqual([3, 6, 1, 4], dict_to_sorted_list(rever)) + self.assertEqual(["one"], dict_to_sorted_list(oneItem)) + """test that the func returns the values not the keys""" + self.assertFalse(["other", "name"] == dict_to_sorted_list(sample1)) + + def test_cmp_samples(self): + """test for comparing samples function""" + sampleA = [ + [ + ("value", "other"), + ("name", "test_name") + ] + ] + sampleB = [ + [ + ("value", "other"), + ("unknown", "test_name") + ] + ] + sampleC = [ + [("other", "value"), + ("name", "value") + ], + [ + ("name", "value"), + ("value", "name") + ], + [ + ("other", "value"), + ("name", "value" + )], + [ + ("name", "name1"), + ("se", "valuex") + ], + [( + "value", "name1"), + ("se", "valuex") + ], + [( + "other", "name1"), + ("se", "valuex" + ) + ], + [( + "name", "name_val"), + ("num_cases", "num_val") + ], + [( + "other_a", "val_a"), + ("other_b", "val" + ) + ] + ] + results = [cmp_samples(val[0], val[1]) for val in sampleA] + resultB = [cmp_samples(val[0], val[1]) for val in sampleB] + resultC = [cmp_samples(val[0], val[1]) for val in sampleC] + + self.assertEqual(1, *results) + self.assertEqual(-1, *resultB) + self.assertEqual([1, -1, 1, -1, -1, 1, -1, -1], resultC) diff --git a/wqflask/tests/unit/wqflask/test_collect.py b/wqflask/tests/unit/wqflask/test_collect.py new file mode 100644 index 00000000..9a36132d --- /dev/null +++ b/wqflask/tests/unit/wqflask/test_collect.py @@ -0,0 +1,73 @@ +"""Test cases for some methods in collect.py""" + +import unittest +from unittest import mock + +from flask import Flask +from wqflask.collect import process_traits + +app = Flask(__name__) + + +class MockSession: + """Helper class for mocking wqflask.collect.g.user_session.logged_in""" + def __init__(self, is_logged_in=False): + self.is_logged_in = is_logged_in + + @property + def logged_in(self): + return self.is_logged_in + + +class MockFlaskG: + """Helper class for mocking wqflask.collect.g.user_session""" + def __init__(self, is_logged_in=False): + self.is_logged_in = is_logged_in + + @property + def user_session(self): + if self.is_logged_in: + return MockSession(is_logged_in=True) + return MockSession() + + +class TestCollect(unittest.TestCase): + + def setUp(self): + self.app_context = app.app_context() + self.app_context.push() + + def tearDown(self): + self.app_context.pop() + + @mock.patch("wqflask.collect.g", MockFlaskG()) + def test_process_traits_with_bytestring(self): + """ + Test that the correct traits are returned when the user is logged + out and bytes are used. + """ + self.assertEqual(process_traits( + b'1452452_at:HC_M2_0606_P:163d04f7db7c9e110de6,' + b'1452447_at:HC_M2_0606_P:eeece8fceb67072debea,' + b'1451401_a_at:HC_M2_0606_P:a043d23b3b3906d8318e,' + b'1429252_at:HC_M2_0606_P:6fa378b349bc9180e8f5'), + set(['1429252_at:HC_M2_0606_P', + '1451401_a_at:HC_M2_0606_P', + '1452447_at:HC_M2_0606_P', + '1452452_at:HC_M2_0606_P'])) + + @mock.patch("wqflask.collect.g", MockFlaskG()) + def test_process_traits_with_normal_string(self): + """ + Test that the correct traits are returned when the user is logged + out and a normal string is used. + """ + self.assertEqual(process_traits( + '1452452_at:HC_M2_0606_P:163d04f7db7c9e110de6,' + '1452447_at:HC_M2_0606_P:eeece8fceb67072debea,' + '1451401_a_at:HC_M2_0606_P:a043d23b3b3906d8318e,' + '1429252_at:HC_M2_0606_P:6fa378b349bc9180e8f5'), + set(['1429252_at:HC_M2_0606_P', + '1451401_a_at:HC_M2_0606_P', + '1452447_at:HC_M2_0606_P', + '1452452_at:HC_M2_0606_P'])) diff --git a/wqflask/tests/unit/wqflask/test_pbkdf2.py b/wqflask/tests/unit/wqflask/test_pbkdf2.py new file mode 100644 index 00000000..a33fbd4f --- /dev/null +++ b/wqflask/tests/unit/wqflask/test_pbkdf2.py @@ -0,0 +1,61 @@ +"""Test cases pbkdf2""" + +import unittest +from wqflask.pbkdf2 import pbkdf2_hex + + +class TestPbkdf2(unittest.TestCase): + def test_pbkdf2_hex(self): + """ + Test pbkdf2_hex function + """ + + for password, salt, iterations, keylen, expected_value in [ + ('password', 'salt', 1, 20, + '0c60c80f961f0e71f3a9b524af6012062fe037a6'), + ('password', 'salt', 2, 20, + 'ea6c014dc72d6f8ccd1ed92ace1d41f0d8de8957'), + ('password', 'salt', 4096, 20, + '4b007901b765489abead49d926f721d065a429c1'), + ('passwordPASSWORDpassword', + 'saltSALTsaltSALTsaltSALTsaltSALTsalt', + 4096, 25, + '3d2eec4fe41c849b80c8d83662c0e44a8b291a964cf2f07038'), + ('pass\x00word', 'sa\x00lt', 4096, 16, + '56fa6aa75548099dcc37d7f03425e0c3'), + ('password', 'ATHENA.MIT.EDUraeburn', 1, 16, + 'cdedb5281bb2f801565a1122b2563515'), + ('password', 'ATHENA.MIT.EDUraeburn', 1, 32, + ('cdedb5281bb2f80' + '1565a1122b256351' + '50ad1f7a04bb9f3a33' + '3ecc0e2e1f70837')), + ('password', 'ATHENA.MIT.EDUraeburn', 2, 16, + '01dbee7f4a9e243e988b62c73cda935d'), + ('password', 'ATHENA.MIT.EDUraeburn', 2, 32, + ('01dbee7f4a9e243e9' + '88b62c73cda935da05' + '378b93244ec8f48a99' + 'e61ad799d86')), + ('password', 'ATHENA.MIT.EDUraeburn', 1200, 32, + ('5c08eb61fdf71e' + '4e4ec3cf6ba1f55' + '12ba7e52ddbc5e51' + '42f708a31e2e62b1e13')), + ('X' * 64, 'pass phrase equals block size', 1200, 32, + ('139c30c0966bc32ba' + '55fdbf212530ac9c5' + 'ec59f1a452f5cc9ad' + '940fea0598ed1')), + ('X' * 65, 'pass phrase exceeds block size', 1200, 32, + ('9ccad6d468770cd' + '51b10e6a68721be6' + '11a8b4d282601db3' + 'b36be9246915ec82a')) + ]: + self.assertEqual( + pbkdf2_hex(data=password, + salt=salt, + iterations=iterations, + keylen=keylen), + expected_value) diff --git a/wqflask/tests/unit/wqflask/test_user_login.py b/wqflask/tests/unit/wqflask/test_user_login.py new file mode 100644 index 00000000..61cd9ab9 --- /dev/null +++ b/wqflask/tests/unit/wqflask/test_user_login.py @@ -0,0 +1,21 @@ +"""Test cases for some methods in login.py""" + +import unittest +from wqflask.user_login import encode_password + + +class TestUserLogin(unittest.TestCase): + def test_encode_password(self): + """ + Test encode password + """ + pass_gen_fields = { + "salt": "salt", + "hashfunc": "sha1", + "iterations": 4096, + "keylength": 20, + } + self.assertEqual( + encode_password(pass_gen_fields, + "password").get("password"), + '4b007901b765489abead49d926f721d065a429c1') diff --git a/wqflask/tests/unit/wqflask/test_user_session.py b/wqflask/tests/unit/wqflask/test_user_session.py new file mode 100644 index 00000000..ebb0334a --- /dev/null +++ b/wqflask/tests/unit/wqflask/test_user_session.py @@ -0,0 +1,15 @@ +"""Test cases for some methods in user_session.py""" + +import unittest +from wqflask.user_session import verify_cookie + + +class TestUserSession(unittest.TestCase): + def test_verify_cookie(self): + """ + Test cookie verification + """ + self.assertEqual( + "3f4c1dbf-5b56-4260-87d6-f35445bda37e", + verify_cookie(("3f4c1dbf-5b56-4260-87d6-" + "f35445bda37e:af4fcf5eace9e7c864ce"))) diff --git a/wqflask/tests/utility/__init__.py b/wqflask/tests/utility/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/wqflask/tests/utility/test_authentication_tools.py b/wqflask/tests/utility/test_authentication_tools.py deleted file mode 100644 index 5c391be5..00000000 --- a/wqflask/tests/utility/test_authentication_tools.py +++ /dev/null @@ -1,189 +0,0 @@ -"""Tests for authentication tools""" -import unittest -from unittest import mock - -from utility.authentication_tools import check_resource_availability -from utility.authentication_tools import add_new_resource - - -class TestResponse: - """Mock Test Response after a request""" - @property - def content(self): - """Mock the content from Requests.get(params).content""" - return '["foo"]' - - -class TestUser: - """Mock user""" - @property - def user_id(self): - """Mockes user id. Used in Flask.g.user_session.user_id""" - return "Jane" - - -class TestUserSession: - """Mock user session""" - @property - def user_session(self): - """Mock user session. Mocks Flask.g.user_session object""" - return TestUser() - - -def mock_add_resource(resource_ob, update=False): - return resource_ob - - -class TestCheckResourceAvailability(unittest.TestCase): - """Test methods related to checking the resource availability""" - @mock.patch('utility.authentication_tools.add_new_resource') - @mock.patch('utility.authentication_tools.Redis') - @mock.patch('utility.authentication_tools.g', mock.Mock()) - @mock.patch('utility.authentication_tools.get_resource_id') - def test_check_resource_availability_default_mask( - self, - resource_id_mock, - redis_mock, - add_new_resource_mock): - """Test the resource availability with default mask""" - resource_id_mock.return_value = 1 - redis_mock.smembers.return_value = [] - test_dataset = mock.MagicMock() - type(test_dataset).type = mock.PropertyMock(return_value="Test") - add_new_resource_mock.return_value = {"default_mask": 2} - self.assertEqual(check_resource_availability(test_dataset), 2) - - @mock.patch('utility.authentication_tools.requests.get') - @mock.patch('utility.authentication_tools.add_new_resource') - @mock.patch('utility.authentication_tools.Redis') - @mock.patch('utility.authentication_tools.g', TestUserSession()) - @mock.patch('utility.authentication_tools.get_resource_id') - def test_check_resource_availability_non_default_mask( - self, - resource_id_mock, - redis_mock, - add_new_resource_mock, - requests_mock): - """Test the resource availability with default mask""" - resource_id_mock.return_value = 1 - redis_mock.smembers.return_value = [] - add_new_resource_mock.return_value = {"default_mask": 2} - requests_mock.return_value = TestResponse() - test_dataset = mock.MagicMock() - type(test_dataset).type = mock.PropertyMock(return_value="Test") - self.assertEqual(check_resource_availability(test_dataset), - ['foo']) - - @mock.patch('utility.authentication_tools.webqtlConfig.SUPER_PRIVILEGES', - "SUPERUSER") - @mock.patch('utility.authentication_tools.requests.get') - @mock.patch('utility.authentication_tools.add_new_resource') - @mock.patch('utility.authentication_tools.Redis') - @mock.patch('utility.authentication_tools.g', TestUserSession()) - @mock.patch('utility.authentication_tools.get_resource_id') - def test_check_resource_availability_of_super_user( - self, - resource_id_mock, - redis_mock, - add_new_resource_mock, - requests_mock): - """Test the resource availability if the user is the super user""" - resource_id_mock.return_value = 1 - redis_mock.smembers.return_value = ["Jane"] - add_new_resource_mock.return_value = {"default_mask": 2} - requests_mock.return_value = TestResponse() - test_dataset = mock.MagicMock() - type(test_dataset).type = mock.PropertyMock(return_value="Test") - self.assertEqual(check_resource_availability(test_dataset), - "SUPERUSER") - - @mock.patch('utility.authentication_tools.webqtlConfig.DEFAULT_PRIVILEGES', - "John Doe") - def test_check_resource_availability_string_dataset(self): - """Test the resource availability if the dataset is a string""" - self.assertEqual(check_resource_availability("Test"), - "John Doe") - - @mock.patch('utility.authentication_tools.webqtlConfig.DEFAULT_PRIVILEGES', - "John Doe") - def test_check_resource_availability_temp(self): - """Test the resource availability if the dataset is a string""" - test_dataset = mock.MagicMock() - type(test_dataset).type = mock.PropertyMock(return_value="Temp") - self.assertEqual(check_resource_availability(test_dataset), - "John Doe") - - -class TestAddNewResource(unittest.TestCase): - """Test cases for add_new_resource method""" - @mock.patch('utility.authentication_tools.webqtlConfig.DEFAULT_PRIVILEGES', - "John Doe") - @mock.patch('utility.authentication_tools.add_resource', mock_add_resource) - @mock.patch('utility.authentication_tools.get_group_code') - def test_add_new_resource_if_publish_datatype(self, group_code_mock): - """Test add_new_resource if dataset type is 'publish'""" - group_code_mock.return_value = "Test" - test_dataset = mock.MagicMock() - type(test_dataset).type = mock.PropertyMock(return_value="Publish") - type(test_dataset).id = mock.PropertyMock(return_value=10) - expected_value = { - "owner_id": "none", - "default_mask": "John Doe", - "group_masks": {}, - "name": "Test_None", - "data": { - "dataset": 10, - "trait": None - }, - "type": "dataset-publish" - } - self.assertEqual(add_new_resource(test_dataset), - expected_value) - - @mock.patch('utility.authentication_tools.webqtlConfig.DEFAULT_PRIVILEGES', - "John Doe") - @mock.patch('utility.authentication_tools.add_resource', mock_add_resource) - @mock.patch('utility.authentication_tools.get_group_code') - def test_add_new_resource_if_geno_datatype(self, group_code_mock): - """Test add_new_resource if dataset type is 'geno'""" - group_code_mock.return_value = "Test" - test_dataset = mock.MagicMock() - type(test_dataset).name = mock.PropertyMock(return_value="Geno") - type(test_dataset).type = mock.PropertyMock(return_value="Geno") - type(test_dataset).id = mock.PropertyMock(return_value=20) - expected_value = { - "owner_id": "none", - "default_mask": "John Doe", - "group_masks": {}, - "name": "Geno", - "data": { - "dataset": 20, - }, - "type": "dataset-geno" - } - self.assertEqual(add_new_resource(test_dataset), - expected_value) - - @mock.patch('utility.authentication_tools.webqtlConfig.DEFAULT_PRIVILEGES', - "John Doe") - @mock.patch('utility.authentication_tools.add_resource', mock_add_resource) - @mock.patch('utility.authentication_tools.get_group_code') - def test_add_new_resource_if_other_datatype(self, group_code_mock): - """Test add_new_resource if dataset type is not 'geno' or 'publish'""" - group_code_mock.return_value = "Test" - test_dataset = mock.MagicMock() - type(test_dataset).name = mock.PropertyMock(return_value="Geno") - type(test_dataset).type = mock.PropertyMock(return_value="other") - type(test_dataset).id = mock.PropertyMock(return_value=20) - expected_value = { - "owner_id": "none", - "default_mask": "John Doe", - "group_masks": {}, - "name": "Geno", - "data": { - "dataset": 20, - }, - "type": "dataset-probeset" - } - self.assertEqual(add_new_resource(test_dataset), - expected_value) diff --git a/wqflask/tests/utility/test_chunks.py b/wqflask/tests/utility/test_chunks.py deleted file mode 100644 index 8d90a1ec..00000000 --- a/wqflask/tests/utility/test_chunks.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Test chunking""" - -import unittest - -from utility.chunks import divide_into_chunks - - -class TestChunks(unittest.TestCase): - "Test Utility method for chunking" - def test_divide_into_chunks(self): - "Check that a list is chunked correctly" - self.assertEqual(divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 3), - [[1, 2, 7], [3, 22, 8], [5, 22, 333]]) - self.assertEqual(divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 4), - [[1, 2, 7], [3, 22, 8], [5, 22, 333]]) - self.assertEqual(divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 5), - [[1, 2], [7, 3], [22, 8], [5, 22], [333]]) - self.assertEqual(divide_into_chunks([], 5), - [[]]) diff --git a/wqflask/tests/utility/test_corestats.py b/wqflask/tests/utility/test_corestats.py deleted file mode 100644 index cf91a248..00000000 --- a/wqflask/tests/utility/test_corestats.py +++ /dev/null @@ -1,55 +0,0 @@ -"""Test Core Stats""" - -import unittest - -from utility.corestats import Stats - - -class TestChunks(unittest.TestCase): - "Test Utility method for chunking" - - def setUp(self): - self.stat_test = Stats((x for x in range(1, 11))) - - def test_stats_sum(self): - """ Test sequence sum """ - self.assertEqual(self.stat_test.sum(), 55) - self.stat_test = Stats([]) - self.assertEqual(self.stat_test.sum(), None) - - def test_stats_count(self): - """ Test sequence count """ - self.assertEqual(self.stat_test.count(), 10) - self.stat_test = Stats([]) - self.assertEqual(self.stat_test.count(), 0) - - def test_stats_min(self): - """ Test min value in sequence""" - self.assertEqual(self.stat_test.min(), 1) - self.stat_test = Stats([]) - self.assertEqual(self.stat_test.min(), None) - - def test_stats_max(self): - """ Test max value in sequence """ - self.assertEqual(self.stat_test.max(), 10) - self.stat_test = Stats([]) - self.assertEqual(self.stat_test.max(), None) - - def test_stats_avg(self): - """ Test avg of sequence """ - self.assertEqual(self.stat_test.avg(), 5.5) - self.stat_test = Stats([]) - self.assertEqual(self.stat_test.avg(), None) - - def test_stats_stdev(self): - """ Test standard deviation of sequence """ - self.assertEqual(self.stat_test.stdev(), 3.0276503540974917) - self.stat_test = Stats([]) - self.assertEqual(self.stat_test.stdev(), None) - - def test_stats_percentile(self): - """ Test percentile of sequence """ - self.assertEqual(self.stat_test.percentile(20), 3.0) - self.assertEqual(self.stat_test.percentile(101), None) - self.stat_test = Stats([]) - self.assertEqual(self.stat_test.percentile(20), None) diff --git a/wqflask/tests/utility/test_corr_result_helpers.py b/wqflask/tests/utility/test_corr_result_helpers.py deleted file mode 100644 index e196fbdf..00000000 --- a/wqflask/tests/utility/test_corr_result_helpers.py +++ /dev/null @@ -1,32 +0,0 @@ -""" Test correlation helper methods """ - -import unittest -from utility.corr_result_helpers import normalize_values, common_keys, normalize_values_with_samples - - -class TestCorrelationHelpers(unittest.TestCase): - """Test methods for normalising lists""" - - def test_normalize_values(self): - """Test that a list is normalised correctly""" - self.assertEqual( - normalize_values([2.3, None, None, 3.2, 4.1, 5], [ - 3.4, 7.2, 1.3, None, 6.2, 4.1]), - ([2.3, 4.1, 5], [3.4, 6.2, 4.1], 3) - ) - - def test_common_keys(self): - """Test that common keys are returned as a list""" - a = dict(BXD1=9.113, BXD2=9.825, BXD14=8.985, BXD15=9.300) - b = dict(BXD1=9.723, BXD3=9.825, BXD14=9.124, BXD16=9.300) - self.assertEqual(sorted(common_keys(a, b)), ['BXD1', 'BXD14']) - - def test_normalize_values_with_samples(self): - """Test that a sample(dict) is normalised correctly""" - self.assertEqual( - normalize_values_with_samples( - dict(BXD1=9.113, BXD2=9.825, BXD14=8.985, - BXD15=9.300, BXD20=9.300), - dict(BXD1=9.723, BXD3=9.825, BXD14=9.124, BXD16=9.300)), - (({'BXD1': 9.113, 'BXD14': 8.985}, {'BXD1': 9.723, 'BXD14': 9.124}, 2)) - ) diff --git a/wqflask/tests/utility/test_formatting.py b/wqflask/tests/utility/test_formatting.py deleted file mode 100644 index 9d3033d1..00000000 --- a/wqflask/tests/utility/test_formatting.py +++ /dev/null @@ -1,33 +0,0 @@ -import unittest -from utility.formatting import numify, commify - - -class TestFormatting(unittest.TestCase): - """Test formatting numbers by numifying or commifying""" - - def test_numify(self): - "Test that a number is correctly converted to a English readable string" - self.assertEqual(numify(1, 'item', 'items'), - 'one item') - self.assertEqual(numify(2, 'book'), 'two') - self.assertEqual(numify(2, 'book', 'books'), 'two books') - self.assertEqual(numify(0, 'book', 'books'), 'zero books') - self.assertEqual(numify(0), 'zero') - self.assertEqual(numify(5), 'five') - self.assertEqual(numify(14, 'book', 'books'), '14 books') - self.assertEqual(numify(999, 'book', 'books'), '999 books') - self.assertEqual(numify(1000000, 'book', 'books'), '1,000,000 books') - self.assertEqual(numify(1956), '1956') - - def test_commify(self): - "Test that commas are added correctly" - self.assertEqual(commify(1), '1') - self.assertEqual(commify(123), '123') - self.assertEqual(commify(1234), '1234') - self.assertEqual(commify(12345), '12,345') - self.assertEqual(commify(1234567890), '1,234,567,890') - self.assertEqual(commify(123.0), '123.0') - self.assertEqual(commify(1234.5), '1234.5') - self.assertEqual(commify(1234.56789), '1234.56789') - self.assertEqual(commify(123456.789), '123,456.789') - self.assertEqual(commify(None), None) diff --git a/wqflask/tests/utility/test_hmac.py b/wqflask/tests/utility/test_hmac.py deleted file mode 100644 index 4e3652f8..00000000 --- a/wqflask/tests/utility/test_hmac.py +++ /dev/null @@ -1,52 +0,0 @@ -# -*- coding: utf-8 -*- -"""Test hmac utility functions""" - -import unittest -from unittest import mock - -from utility.hmac import data_hmac -from utility.hmac import url_for_hmac -from utility.hmac import hmac_creation - - -class TestHmacUtil(unittest.TestCase): - """Test Utility method for hmac creation""" - - @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) - def test_hmac_creation(self): - """Test hmac creation with a utf-8 string""" - self.assertEqual(hmac_creation("ファイ"), "7410466338cfe109e946") - - @mock.patch("utility.hmac.app.config", - {'SECRET_HMAC_CODE': ('\x08\xdf\xfa\x93N\x80' - '\xd9\\H@\\\x9f`\x98d^' - '\xb4a;\xc6OM\x946a\xbc' - '\xfc\x80:*\xebc')}) - def test_hmac_creation_with_cookie(self): - """Test hmac creation with a cookie""" - cookie = "3f4c1dbf-5b56-4260-87d6-f35445bda37e:af4fcf5eace9e7c864ce" - uuid_, _, signature = cookie.partition(":") - self.assertEqual( - hmac_creation(uuid_), - "af4fcf5eace9e7c864ce") - - @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) - def test_data_hmac(self): - """Test data_hmac fn with a utf-8 string""" - self.assertEqual(data_hmac("ファイ"), "ファイ:7410466338cfe109e946") - - @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) - @mock.patch("utility.hmac.url_for") - def test_url_for_hmac_with_plain_url(self, mock_url): - """Test url_for_hmac without params""" - mock_url.return_value = "https://mock_url.com/ファイ/" - self.assertEqual(url_for_hmac("ファイ"), - "https://mock_url.com/ファイ/?hm=05bc39e659b1948f41e7") - - @mock.patch("utility.hmac.app.config", {'SECRET_HMAC_CODE': "secret"}) - @mock.patch("utility.hmac.url_for") - def test_url_for_hmac_with_param_in_url(self, mock_url): - """Test url_for_hmac with params""" - mock_url.return_value = "https://mock_url.com/?ファイ=1" - self.assertEqual(url_for_hmac("ファイ"), - "https://mock_url.com/?ファイ=1&hm=4709c1708270644aed79") diff --git a/wqflask/tests/wqflask/__init__.py b/wqflask/tests/wqflask/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/wqflask/tests/wqflask/api/__init__.py b/wqflask/tests/wqflask/api/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/wqflask/tests/wqflask/api/test_gen_menu.py b/wqflask/tests/wqflask/api/test_gen_menu.py deleted file mode 100644 index 84898bd1..00000000 --- a/wqflask/tests/wqflask/api/test_gen_menu.py +++ /dev/null @@ -1,413 +0,0 @@ -"""Test cases for wqflask.api.gen_menu""" -import unittest -from unittest import mock - -from wqflask import app -from wqflask.api.gen_menu import gen_dropdown_json -from wqflask.api.gen_menu import get_species -from wqflask.api.gen_menu import get_groups -from wqflask.api.gen_menu import get_types -from wqflask.api.gen_menu import get_datasets -from wqflask.api.gen_menu import phenotypes_exist -from wqflask.api.gen_menu import genotypes_exist -from wqflask.api.gen_menu import build_datasets -from wqflask.api.gen_menu import build_types - - -class TestGenMenu(unittest.TestCase): - """Tests for the gen_menu module""" - - def setUp(self): - self.app_context = app.app_context() - self.app_context.push() - self.test_group = { - 'mouse': [ - ['H_T1', - 'H_T', - 'Family:DescriptionA' - ], - ['H_T2', "H_T'", 'Family:None'] - ], - 'human': [ - ['BXD', 'BXD', 'Family:None'], - ['HLC', 'Liver: Normal Gene Expression with Genotypes (Merck)', - 'Family:Test'] - ] - } - - self.test_type = { - 'mouse': { - 'H_T2': [('Phenotypes', - 'Traits and Cofactors', - 'Phenotypes'), - ('Genotypes', - 'DNA Markers and SNPs', - 'Genotypes'), - ['M', 'M', 'Molecular Trait Datasets']], - 'H_T1': [('Phenotypes', - 'Traits and Cofactors', - 'Phenotypes'), - ('Genotypes', - 'DNA Markers and SNPs', - 'Genotypes'), - ['M', 'M', 'Molecular Trait Datasets']] - }, - 'human': { - 'HLC': [('Phenotypes', - 'Traits and Cofactors', - 'Phenotypes'), - ('Genotypes', - 'DNA Markers and SNPs', - 'Genotypes'), - ['M', 'M', 'Molecular Trait Datasets']], - 'BXD': [('Phenotypes', - 'Traits and Cofactors', - 'Phenotypes'), - ('Genotypes', - 'DNA Markers and SNPs', - 'Genotypes'), - ['M', 'M', 'Molecular Trait Datasets']] - } - } - - def tearDown(self): - self.app_context.pop() - - @mock.patch('wqflask.api.gen_menu.g') - def test_get_species(self, db_mock): - """Test that assertion is raised when dataset and dataset_name - are defined""" - db_mock.db.execute.return_value.fetchall.return_value = ( - ('human', 'Human'), - ('mouse', 'Mouse')) - self.assertEqual(get_species(), - [['human', 'Human'], ['mouse', 'Mouse']]) - db_mock.db.execute.assert_called_once_with( - "SELECT Name, MenuName FROM Species ORDER BY OrderId" - ) - - @mock.patch('wqflask.api.gen_menu.g') - def test_get_groups(self, db_mock): - """Test that species groups are grouped correctly""" - db_mock.db.execute.return_value.fetchall.side_effect = [ - # Mouse - (('BXD', 'BXD', None), - ('HLC', 'Liver: Normal Gene Expression with Genotypes (Merck)', - 'Test')), - # Human - (('H_T1', "H_T", "DescriptionA"), - ('H_T2', "H_T'", None)) - ] - - self.assertEqual(get_groups([["human", "Human"], ["mouse", "Mouse"]]), - self.test_group) - - for name in ["mouse", "human"]: - db_mock.db.execute.assert_any_call( - ("SELECT InbredSet.Name, InbredSet.FullName, " + - "IFNULL(InbredSet.Family, 'None') " + - "FROM InbredSet, Species WHERE Species.Name " + - "= '{}' AND InbredSet.SpeciesId = Species.Id GROUP by " + - "InbredSet.Name ORDER BY IFNULL(InbredSet.FamilyOrder, " + - "InbredSet.FullName) ASC, IFNULL(InbredSet.Family, " + - "InbredSet.FullName) ASC, InbredSet.FullName ASC, " + - "InbredSet.MenuOrderId ASC").format(name) - ) - - @mock.patch('wqflask.api.gen_menu.g') - def test_phenotypes_exist_called_with_correct_query(self, db_mock): - """Test that phenotypes_exist is called with the correct query""" - db_mock.db.execute.return_value.fetchone.return_value = None - phenotypes_exist("test") - db_mock.db.execute.assert_called_with( - "SELECT Name FROM PublishFreeze " - "WHERE PublishFreeze.Name = 'testPublish'" - ) - - @mock.patch('wqflask.api.gen_menu.g') - def test_phenotypes_exist_with_falsy_values(self, db_mock): - """Test that phenotype check returns correctly when given - a None value""" - for x in [None, False, (), [], ""]: - db_mock.db.execute.return_value.fetchone.return_value = x - self.assertFalse(phenotypes_exist("test")) - - @mock.patch('wqflask.api.gen_menu.g') - def test_phenotypes_exist_with_truthy_value(self, db_mock): - """Test that phenotype check returns correctly when given Truthy """ - for x in ["x", ("result"), ["result"], [1]]: - db_mock.db.execute.return_value.fetchone.return_value = (x) - self.assertTrue(phenotypes_exist("test")) - - @mock.patch('wqflask.api.gen_menu.g') - def test_genotypes_exist_called_with_correct_query(self, db_mock): - """Test that genotypes_exist is called with the correct query""" - db_mock.db.execute.return_value.fetchone.return_value = None - genotypes_exist("test") - db_mock.db.execute.assert_called_with( - "SELECT Name FROM GenoFreeze WHERE GenoFreeze.Name = 'testGeno'" - ) - - @mock.patch('wqflask.api.gen_menu.g') - def test_genotypes_exist_with_falsy_values(self, db_mock): - """Test that genotype check returns correctly when given - a None value""" - for x in [None, False, (), [], ""]: - db_mock.db.execute.return_value.fetchone.return_value = x - self.assertFalse(genotypes_exist("test")) - - @mock.patch('wqflask.api.gen_menu.g') - def test_genotypes_exist_with_truthy_value(self, db_mock): - """Test that genotype check returns correctly when given Truthy """ - for x in ["x", ("result"), ["result"], [1]]: - db_mock.db.execute.return_value.fetchone.return_value = (x) - self.assertTrue(phenotypes_exist("test")) - - @mock.patch('wqflask.api.gen_menu.g') - def test_build_datasets_with_type_phenotypes(self, db_mock): - """Test that correct dataset is returned for a phenotype type""" - db_mock.db.execute.return_value.fetchall.return_value = ( - (602, "BXDPublish", "BXD Published Phenotypes"), - ) - self.assertEqual(build_datasets("Mouse", "BXD", "Phenotypes"), - [['602', "BXDPublish", "BXD Published Phenotypes"]]) - db_mock.db.execute.assert_called_with( - "SELECT InfoFiles.GN_AccesionId, PublishFreeze.Name, " + - "PublishFreeze.FullName FROM InfoFiles, PublishFreeze, " + - "InbredSet WHERE InbredSet.Name = 'BXD' AND " + - "PublishFreeze.InbredSetId = InbredSet.Id AND " + - "InfoFiles.InfoPageName = PublishFreeze.Name " + - "ORDER BY PublishFreeze.CreateTime ASC" - ) - self.assertEqual(build_datasets("Mouse", "MDP", "Phenotypes"), - [['602', "BXDPublish", "Mouse Phenome Database"]]) - - db_mock.db.execute.return_value.fetchall.return_value = () - db_mock.db.execute.return_value.fetchone.return_value = ( - "BXDPublish", "Mouse Phenome Database" - ) - self.assertEqual(build_datasets("Mouse", "MDP", "Phenotypes"), - [["None", "BXDPublish", "Mouse Phenome Database"]]) - - @mock.patch('wqflask.api.gen_menu.g') - def test_build_datasets_with_type_phenotypes_and_no_results(self, db_mock): - """Test that correct dataset is returned for a phenotype type with no - results - - """ - db_mock.db.execute.return_value.fetchall.return_value = None - db_mock.db.execute.return_value.fetchone.return_value = (121, - "text value") - self.assertEqual(build_datasets("Mouse", "BXD", "Phenotypes"), - [["None", "121", "text value"]]) - db_mock.db.execute.assert_called_with( - "SELECT PublishFreeze.Name, PublishFreeze.FullName " - "FROM PublishFreeze, InbredSet " - "WHERE InbredSet.Name = 'BXD' AND " - "PublishFreeze.InbredSetId = InbredSet.Id " - "ORDER BY PublishFreeze.CreateTime ASC" - ) - - @mock.patch('wqflask.api.gen_menu.g') - def test_build_datasets_with_type_genotypes(self, db_mock): - """Test that correct dataset is returned for a phenotype type""" - db_mock.db.execute.return_value.fetchone.return_value = ( - 635, "HLCPublish", "HLC Published Genotypes" - ) - - self.assertEqual(build_datasets("Mouse", "HLC", "Genotypes"), - [["635", "HLCGeno", "HLC Genotypes"]]) - db_mock.db.execute.assert_called_with( - "SELECT InfoFiles.GN_AccesionId FROM InfoFiles, " - "GenoFreeze, InbredSet WHERE InbredSet.Name = 'HLC' AND " - "GenoFreeze.InbredSetId = InbredSet.Id AND " - "InfoFiles.InfoPageName = GenoFreeze.ShortName " + - "ORDER BY GenoFreeze.CreateTime DESC" - ) - db_mock.db.execute.return_value.fetchone.return_value = () - self.assertEqual(build_datasets("Mouse", "HLC", "Genotypes"), - [["None", "HLCGeno", "HLC Genotypes"]]) - - @mock.patch('wqflask.api.gen_menu.g') - def test_build_datasets_with_type_mrna(self, db_mock): - """Test that correct dataset is returned for a mRNA - expression/ Probeset""" - db_mock.db.execute.return_value.fetchall.return_value = ( - (112, "HC_M2_0606_P", - "Hippocampus Consortium M430v2 (Jun06) PDNN"), ) - self.assertEqual(build_datasets("Mouse", "HLC", "mRNA"), [[ - "112", 'HC_M2_0606_P', "Hippocampus Consortium M430v2 (Jun06) PDNN" - ]]) - db_mock.db.execute.assert_called_once_with( - "SELECT ProbeSetFreeze.Id, ProbeSetFreeze.Name, " + - "ProbeSetFreeze.FullName FROM ProbeSetFreeze, " + - "ProbeFreeze, InbredSet, Tissue, Species WHERE " + - "Species.Name = 'Mouse' AND Species.Id = " + - "InbredSet.SpeciesId AND InbredSet.Name = 'HLC' AND " + - "ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and " + - "Tissue.Name = 'mRNA' AND ProbeFreeze.TissueId = " + - "Tissue.Id and ProbeFreeze.InbredSetId = InbredSet.Id " + - "ORDER BY ProbeSetFreeze.CreateTime DESC") - - @mock.patch('wqflask.api.gen_menu.build_datasets') - @mock.patch('wqflask.api.gen_menu.g') - def test_build_types(self, db_mock, datasets_mock): - """Test that correct tissue metadata is returned""" - datasets_mock.return_value = [ - ["112", 'HC_M2_0606_P', - "Hippocampus Consortium M430v2 (Jun06) PDNN"] - ] - db_mock.db.execute.return_value.fetchall.return_value = ( - ('Mouse Tissue'), ('Human Tissue'), ('Rat Tissue') - ) - self.assertEqual(build_types('mouse', 'random group'), - [['M', 'M', 'Molecular Traits'], - ['H', 'H', 'Molecular Traits'], - ['R', 'R', 'Molecular Traits']]) - db_mock.db.execute.assert_called_once_with( - "SELECT DISTINCT Tissue.Name " + - "FROM ProbeFreeze, ProbeSetFreeze, InbredSet, " + - "Tissue, Species WHERE Species.Name = 'mouse' " + - "AND Species.Id = InbredSet.SpeciesId AND " + - "InbredSet.Name = 'random group' AND " + - "ProbeFreeze.TissueId = Tissue.Id AND " + - "ProbeFreeze.InbredSetId = InbredSet.Id AND " + - "ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id " + - "ORDER BY Tissue.Name" - ) - - @mock.patch('wqflask.api.gen_menu.build_types') - @mock.patch('wqflask.api.gen_menu.genotypes_exist') - @mock.patch('wqflask.api.gen_menu.phenotypes_exist') - def test_get_types_with_existing_genotype_and_phenotypes( - self, - phenotypes_exist_mock, - genotypes_exist_mock, - build_types_mock): - """Test that build types are constructed correctly if phenotypes and genotypes - exist - - """ - phenotypes_exist_mock.return_value = True - genotypes_exist_mock.return_value = True - - expected_result = self.test_type - - build_types_mock.return_value = [ - ['M', 'M', 'Molecular Trait Datasets'] - ] - self.assertEqual(get_types(self.test_group), expected_result) - - @mock.patch('wqflask.api.gen_menu.build_types') - @mock.patch('wqflask.api.gen_menu.genotypes_exist') - @mock.patch('wqflask.api.gen_menu.phenotypes_exist') - def test_get_types_with_buildtype_and_non_existent_genotype_and_phenotypes( - self, - phenotypes_exist_mock, - genotypes_exist_mock, - build_types_mock): - """Test that build types are constructed correctly if phenotypes_exist and - genotypes_exist are false but build_type is falsy - - """ - phenotypes_exist_mock.return_value = False - genotypes_exist_mock.return_value = False - - build_types_mock.return_value = [] - self.assertEqual(get_types(self.test_group), { - 'mouse': {}, - 'human': {} - }) - - @mock.patch('wqflask.api.gen_menu.build_types') - @mock.patch('wqflask.api.gen_menu.genotypes_exist') - @mock.patch('wqflask.api.gen_menu.phenotypes_exist') - def test_get_types_with_non_existent_genotype_phenotypes_and_buildtype( - self, - phenotypes_exist_mock, - genotypes_exist_mock, - build_types_mock): - """Test that build types are constructed correctly if phenotypes_exist, - genotypes_exist and build_types are truthy - - """ - phenotypes_exist_mock.return_value = False - genotypes_exist_mock.return_value = False - - build_types_mock.return_value = [ - ['M', 'M', 'Molecular Trait Datasets'] - ] - expected_result = { - 'mouse': { - 'H_T2': [['M', 'M', 'Molecular Trait Datasets']], - 'H_T1': [['M', 'M', 'Molecular Trait Datasets']]}, - 'human': { - 'HLC': [['M', 'M', 'Molecular Trait Datasets']], - 'BXD': [['M', 'M', 'Molecular Trait Datasets']]}} - self.assertEqual(get_types(self.test_group), - expected_result) - - @mock.patch('wqflask.api.gen_menu.build_datasets') - def test_get_datasets_with_existent_datasets(self, - build_datasets_mock): - """Test correct dataset is returned with existent build_datasets""" - build_datasets_mock.return_value = "Test" - expected_result = { - 'mouse': { - 'H_T2': {'Genotypes': 'Test', - 'M': 'Test', - 'Phenotypes': 'Test'}, - 'H_T1': {'Genotypes': 'Test', - 'M': 'Test', - 'Phenotypes': 'Test'}}, - 'human': {'HLC': {'Genotypes': 'Test', - 'M': 'Test', - 'Phenotypes': 'Test'}, - 'BXD': {'Genotypes': 'Test', - 'M': 'Test', - 'Phenotypes': 'Test'}}} - self.assertEqual(get_datasets(self.test_type), - expected_result) - - @mock.patch('wqflask.api.gen_menu.build_datasets') - def test_get_datasets_with_non_existent_datasets(self, - build_datasets_mock): - """Test correct dataset is returned with non-existent build_datasets""" - build_datasets_mock.return_value = None - expected_result = { - 'mouse': { - 'H_T2': {}, - 'H_T1': {}}, - 'human': {'HLC': {}, - 'BXD': {}}} - self.assertEqual(get_datasets(self.test_type), - expected_result) - - @mock.patch('wqflask.api.gen_menu.get_datasets') - @mock.patch('wqflask.api.gen_menu.get_types') - @mock.patch('wqflask.api.gen_menu.get_groups') - @mock.patch('wqflask.api.gen_menu.get_species') - def test_gen_dropdown_json(self, - species_mock, - groups_mock, - types_mock, - datasets_mock): - "Test that the correct dictionary is constructed properly" - species_mock.return_value = ("speciesA speciesB speciesC speciesD" - .split(" ")) - datasets_mock.return_value = ("datasetA datasetB datasetC datasetD" - .split(" ")) - groups_mock.return_value = ("groupA groupB groupC groupD" - .split(" ")) - types_mock.return_value = ("typeA typeB typeC typeD" - .split(" ")) - datasets_mock.return_value = ("datasetA datasetB datasetC datasetD" - .split(" ")) - - expected_result = { - 'datasets': ['datasetA', 'datasetB', 'datasetC', 'datasetD'], - 'types': ['typeA', 'typeB', 'typeC', 'typeD'], - 'groups': ['groupA', 'groupB', 'groupC', 'groupD'], - 'species': ['speciesA', 'speciesB', 'speciesC', 'speciesD']} - - self.assertEqual(gen_dropdown_json(), expected_result) diff --git a/wqflask/tests/wqflask/marker_regression/__init__.py b/wqflask/tests/wqflask/marker_regression/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/wqflask/tests/wqflask/marker_regression/test_display_mapping_results.py b/wqflask/tests/wqflask/marker_regression/test_display_mapping_results.py deleted file mode 100644 index 8ae0f09f..00000000 --- a/wqflask/tests/wqflask/marker_regression/test_display_mapping_results.py +++ /dev/null @@ -1,156 +0,0 @@ -import unittest - -import htmlgen as HT -from wqflask.marker_regression.display_mapping_results import ( - DisplayMappingResults, - HtmlGenWrapper -) - - -class TestDisplayMappingResults(unittest.TestCase): - """Basic Methods to test Mapping Results""" - def test_pil_colors(self): - """Test that colors use PILLOW color format""" - self.assertEqual(DisplayMappingResults.CLICKABLE_WEBQTL_REGION_COLOR, - (245, 211, 211)) - - -class TestHtmlGenWrapper(unittest.TestCase): - """Test Wrapper around HTMLGen""" - def test_create_image(self): - """Test HT.Image method""" - self.assertEqual( - str(HtmlGenWrapper.create_image_tag(src="test.png", - alt="random", - border="0", - width="10", - height="13", - usemap="#webqtlmap")), - ("""random""") - ) - - def test_create_form(self): - """Test HT.Form method""" - test_form = HtmlGenWrapper.create_form_tag( - cgi="/testing/", - enctype='multipart/form-data', - name="formName", - submit=HtmlGenWrapper.create_input_tag(type_='hidden', name='Default_Name') - ) - test_image = HtmlGenWrapper.create_image_tag( - src="test.png", - alt="random", - border="0", - width="10", - height="13", - usemap="#webqtlmap" - ) - self.assertEqual( - str(test_form).replace("\n", ""), - ("""
""")) - hddn = { - 'FormID': 'showDatabase', - 'ProbeSetID': '_', - 'database': "TestGeno", - 'CellID': '_', - 'RISet': "Test", - 'incparentsf1': 'ON' - } - for key in hddn.keys(): - test_form.append( - HtmlGenWrapper.create_input_tag( - name=key, - value=hddn[key], - type_='hidden')) - test_form.append(test_image) - - self.assertEqual(str(test_form).replace("\n", ""), ( - """
""" - """""" - """""" - """""" - """""" - """""" - """""" - """""" - """random""" - """
""")) - - def test_create_paragraph(self): - """Test HT.Paragraph method""" - test_p_element = HtmlGenWrapper.create_p_tag(id="smallSize") - par_text = ( - "Mapping using genotype data as " - "a trait will result in infinity LRS at one locus. " - "In order to display the result properly, all LRSs " - "higher than 100 are capped at 100." - ) - self.assertEqual( - str(test_p_element), - """

""" - ) - test_p_element.append(HtmlGenWrapper.create_br_tag()) - test_p_element.append(par_text) - self.assertEqual( - str(test_p_element), - """


{}

""".format(par_text) - ) - - def test_create_br_tag(self): - """Test HT.BR() method""" - self.assertEqual(str(HtmlGenWrapper.create_br_tag()), - "
") - - def test_create_input_tag(self): - """Test HT.Input method""" - self.assertEqual( - str(HtmlGenWrapper.create_input_tag( - type_="hidden", - name="name", - value="key", - Class="trait trait_")).replace("\n", ""), - ("""""")) - - def test_create_map_tag(self): - """Test HT.Map method""" - self.assertEqual(str(HtmlGenWrapper.create_map_tag( - name="WebqTLImageMap")).replace("\n", ""), - """""") - gifmap = HtmlGenWrapper.create_map_tag(name="test") - gifmap.append(HtmlGenWrapper.create_area_tag(shape="rect", - coords='1 2 3', href='#area1')) - gifmap.append(HtmlGenWrapper.create_area_tag(shape="rect", - coords='1 2 3', href='#area2')) - self.assertEqual( - str(gifmap).replace("\n", ""), - ("""""" - """""" - """""" - """""")) - - def test_create_area_tag(self): - """Test HT.Area method""" - self.assertEqual( - str(HtmlGenWrapper.create_area_tag( - shape="rect", - coords="1 2", - href="http://test.com", - title="Some Title")).replace("\n", ""), - ("""""")) - - def test_create_link_tag(self): - """Test HT.HREF method""" - self.assertEqual( - str(HtmlGenWrapper.create_link_tag( - "www.test.com", "test", target="_blank")).replace("\n", ""), - """test""") diff --git a/wqflask/tests/wqflask/show_trait/__init__.py b/wqflask/tests/wqflask/show_trait/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/wqflask/tests/wqflask/show_trait/test_export_trait_data.py b/wqflask/tests/wqflask/show_trait/test_export_trait_data.py deleted file mode 100644 index 41761944..00000000 --- a/wqflask/tests/wqflask/show_trait/test_export_trait_data.py +++ /dev/null @@ -1,212 +0,0 @@ -import unittest -from unittest import mock -from wqflask.show_trait.export_trait_data import dict_to_sorted_list -from wqflask.show_trait.export_trait_data import cmp_samples -from wqflask.show_trait.export_trait_data import export_sample_table -from wqflask.show_trait.export_trait_data import get_export_metadata - - -class AttributesSetter: - def __init__(self, obj): - for key, value in obj.items(): - setattr(self, key, value) - - -class TestExportTraits(unittest.TestCase): - """Test methods related to converting dict to sortedlist""" - @mock.patch("wqflask.show_trait.export_trait_data.create_trait") - @mock.patch("wqflask.show_trait.export_trait_data.data_set") - def test_get_export_metadata_no_publish(self, mock_dataset, mock_trait): - """test for exporting metadata with no publish""" - mock_dataset_attributes = AttributesSetter( - {"type": "no_publish", "dataset_name": "Temp", "name": "Temp"}) - - mock_nested_attributes = AttributesSetter({"name": "name"}) - mock_dataset_attributes.group = mock_nested_attributes - mock_dataset.create_dataset.return_value = mock_dataset_attributes - mock_trait.return_value = AttributesSetter({"symbol": "", "description_display": "Description", - "title": "research1", "journal": "", "authors": ""}) - - results = get_export_metadata("random_id", "Temp") - expected = [["Record ID: random_id"], - ["Trait URL: http://genenetwork.org/show_trait?trait_id=random_id&dataset=Temp"], - ["Dataset: Temp"], - ["Group: name"], []] - - mock_dataset.create_dataset.assert_called_with("Temp") - mock_trait.assert_called_with( - dataset=mock_dataset_attributes, name="random_id", cellid=None, get_qtl_info=False) - self.assertEqual(results, expected) - - @mock.patch("wqflask.show_trait.export_trait_data.create_trait") - @mock.patch("wqflask.show_trait.export_trait_data.data_set") - def test_get_export_metadata_with_publish(self, data_mock, trait_mock): - """test for exporting metadata with dataset.type=Publish""" - mock_dataset_attributes = AttributesSetter({"type": "Publish", "dataset_name": "Temp", - "name": "Temp", "description_display": "Description goes here"}) - - mock_nested_attributes = AttributesSetter({"name": "name"}) - mock_dataset_attributes.group = mock_nested_attributes - data_mock.create_dataset.return_value = mock_dataset_attributes - trait_instance = AttributesSetter({"symbol": "", "description_display": "Description", - "title": "research1", "journal": "", "authors": ""}) - trait_mock.return_value = trait_instance - - results = get_export_metadata( - "29ae0615-0d77-4814-97c7-c9e91f6bfd7b", "Temp") - - expected = [['Phenotype ID: 29ae0615-0d77-4814-97c7-c9e91f6bfd7b'], - ['Phenotype URL: http://genenetwork.org/show_trait?trait_id=29ae0615-0d77-4814-97c7-c9e91f6bfd7b&dataset=Temp'], [ - 'Group: name'], ['Phenotype: Description'], - ['Authors: N/A'], ['Title: research1'], - ['Journal: N/A'], ['Dataset Link: http://gn1.genenetwork.org/webqtl/main.py?FormID=sharinginfo&InfoPageName=Temp'], []] - - self.assertEqual(results, expected) - - @mock.patch("wqflask.show_trait.export_trait_data.dict_to_sorted_list") - @mock.patch("wqflask.show_trait.export_trait_data.get_export_metadata") - def test_export_sample_table(self, exp_metadata, dict_list): - """test for exporting sample table""" - targs_obj = { - "export_data": """{ - "primary_samples": [ - { - "other": "germanotta", - "name": "Sauroniops", - "se":{ - "name":"S2" - }, - "num_cases":{ - "k1":"value" - - } - } - ], - "other_samples": [ - { - "se": 1, - "num_cases": 4, - "value": 6, - "name": 3 - } - ] - }""", - "trait_display_name": "Hair_color", - "trait_id": "23177fdc-312e-4084-ad0c-f3eae785fff5", - "dataset": { - } - } - exp_metadata.return_value = [ - ["Phenotype ID:0a2be192-57f5-400b-bbbd-0cf50135995f"], ['Group:gp1'], - ["Phenotype:p1"], [ - "Authors:N/A"], - ["Title:research1"], - ["Journal:N/A"], - ["Dataset Link: http://gn1.genenetwork.org/webqtl/main.py?FormID=sharinginfo&InfoPageName=name1"], []] - expected = ('Hair_color', - [['Phenotype ID:0a2be192-57f5-400b-bbbd-0cf50135995f'], - ['Group:gp1'], - ['Phenotype:p1'], - ['Authors:N/A'], - ['Title:research1'], - ['Journal:N/A'], - ['Dataset Link: ' - 'http://gn1.genenetwork.org/webqtl/main.py?FormID=sharinginfo&InfoPageName=name1'], - [], - ['Name', 'Value', 'SE', 'N'], - ['Sauroniops', 'germanotta'], - [3, 6, 1, 4]]) - - dict_list.side_effect = [['Sauroniops', 'germanotta'], [3, 6, 1, 4]] - - self.assertEqual(export_sample_table(targs_obj), expected) - exp_metadata.assert_called_with( - "23177fdc-312e-4084-ad0c-f3eae785fff5", {}) - self.assertEqual(dict_list.call_count, 2) - - def test_dict_to_sortedlist(self): - """test for conversion of dict to sorted list""" - sample1 = { - "other": "exp1", - "name": "exp2" - } - sample2 = { - "se": 1, - "num_cases": 4, - "value": 6, - "name": 3 - - } - rever = { - "name": 3, - "value": 6, - "num_cases": 4, - "se": 1 - } - oneItem = { - "item1": "one" - } - - self.assertEqual(["exp2", "exp1"], dict_to_sorted_list(sample1)) - self.assertEqual([3, 6, 1, 4], dict_to_sorted_list(sample2)) - self.assertEqual([3, 6, 1, 4], dict_to_sorted_list(rever)) - self.assertEqual(["one"], dict_to_sorted_list(oneItem)) - """test that the func returns the values not the keys""" - self.assertFalse(["other", "name"] == dict_to_sorted_list(sample1)) - - def test_cmp_samples(self): - """test for comparing samples function""" - sampleA = [ - [ - ("value", "other"), - ("name", "test_name") - ] - ] - sampleB = [ - [ - ("value", "other"), - ("unknown", "test_name") - ] - ] - sampleC = [ - [("other", "value"), - ("name", "value") - ], - [ - ("name", "value"), - ("value", "name") - ], - [ - ("other", "value"), - ("name", "value" - )], - [ - ("name", "name1"), - ("se", "valuex") - ], - [( - "value", "name1"), - ("se", "valuex") - ], - [( - "other", "name1"), - ("se", "valuex" - ) - ], - [( - "name", "name_val"), - ("num_cases", "num_val") - ], - [( - "other_a", "val_a"), - ("other_b", "val" - ) - ] - ] - results = [cmp_samples(val[0], val[1]) for val in sampleA] - resultB = [cmp_samples(val[0], val[1]) for val in sampleB] - resultC = [cmp_samples(val[0], val[1]) for val in sampleC] - - self.assertEqual(1, *results) - self.assertEqual(-1, *resultB) - self.assertEqual([1, -1, 1, -1, -1, 1, -1, -1], resultC) diff --git a/wqflask/tests/wqflask/test_collect.py b/wqflask/tests/wqflask/test_collect.py deleted file mode 100644 index 9a36132d..00000000 --- a/wqflask/tests/wqflask/test_collect.py +++ /dev/null @@ -1,73 +0,0 @@ -"""Test cases for some methods in collect.py""" - -import unittest -from unittest import mock - -from flask import Flask -from wqflask.collect import process_traits - -app = Flask(__name__) - - -class MockSession: - """Helper class for mocking wqflask.collect.g.user_session.logged_in""" - def __init__(self, is_logged_in=False): - self.is_logged_in = is_logged_in - - @property - def logged_in(self): - return self.is_logged_in - - -class MockFlaskG: - """Helper class for mocking wqflask.collect.g.user_session""" - def __init__(self, is_logged_in=False): - self.is_logged_in = is_logged_in - - @property - def user_session(self): - if self.is_logged_in: - return MockSession(is_logged_in=True) - return MockSession() - - -class TestCollect(unittest.TestCase): - - def setUp(self): - self.app_context = app.app_context() - self.app_context.push() - - def tearDown(self): - self.app_context.pop() - - @mock.patch("wqflask.collect.g", MockFlaskG()) - def test_process_traits_with_bytestring(self): - """ - Test that the correct traits are returned when the user is logged - out and bytes are used. - """ - self.assertEqual(process_traits( - b'1452452_at:HC_M2_0606_P:163d04f7db7c9e110de6,' - b'1452447_at:HC_M2_0606_P:eeece8fceb67072debea,' - b'1451401_a_at:HC_M2_0606_P:a043d23b3b3906d8318e,' - b'1429252_at:HC_M2_0606_P:6fa378b349bc9180e8f5'), - set(['1429252_at:HC_M2_0606_P', - '1451401_a_at:HC_M2_0606_P', - '1452447_at:HC_M2_0606_P', - '1452452_at:HC_M2_0606_P'])) - - @mock.patch("wqflask.collect.g", MockFlaskG()) - def test_process_traits_with_normal_string(self): - """ - Test that the correct traits are returned when the user is logged - out and a normal string is used. - """ - self.assertEqual(process_traits( - '1452452_at:HC_M2_0606_P:163d04f7db7c9e110de6,' - '1452447_at:HC_M2_0606_P:eeece8fceb67072debea,' - '1451401_a_at:HC_M2_0606_P:a043d23b3b3906d8318e,' - '1429252_at:HC_M2_0606_P:6fa378b349bc9180e8f5'), - set(['1429252_at:HC_M2_0606_P', - '1451401_a_at:HC_M2_0606_P', - '1452447_at:HC_M2_0606_P', - '1452452_at:HC_M2_0606_P'])) diff --git a/wqflask/tests/wqflask/test_pbkdf2.py b/wqflask/tests/wqflask/test_pbkdf2.py deleted file mode 100644 index a33fbd4f..00000000 --- a/wqflask/tests/wqflask/test_pbkdf2.py +++ /dev/null @@ -1,61 +0,0 @@ -"""Test cases pbkdf2""" - -import unittest -from wqflask.pbkdf2 import pbkdf2_hex - - -class TestPbkdf2(unittest.TestCase): - def test_pbkdf2_hex(self): - """ - Test pbkdf2_hex function - """ - - for password, salt, iterations, keylen, expected_value in [ - ('password', 'salt', 1, 20, - '0c60c80f961f0e71f3a9b524af6012062fe037a6'), - ('password', 'salt', 2, 20, - 'ea6c014dc72d6f8ccd1ed92ace1d41f0d8de8957'), - ('password', 'salt', 4096, 20, - '4b007901b765489abead49d926f721d065a429c1'), - ('passwordPASSWORDpassword', - 'saltSALTsaltSALTsaltSALTsaltSALTsalt', - 4096, 25, - '3d2eec4fe41c849b80c8d83662c0e44a8b291a964cf2f07038'), - ('pass\x00word', 'sa\x00lt', 4096, 16, - '56fa6aa75548099dcc37d7f03425e0c3'), - ('password', 'ATHENA.MIT.EDUraeburn', 1, 16, - 'cdedb5281bb2f801565a1122b2563515'), - ('password', 'ATHENA.MIT.EDUraeburn', 1, 32, - ('cdedb5281bb2f80' - '1565a1122b256351' - '50ad1f7a04bb9f3a33' - '3ecc0e2e1f70837')), - ('password', 'ATHENA.MIT.EDUraeburn', 2, 16, - '01dbee7f4a9e243e988b62c73cda935d'), - ('password', 'ATHENA.MIT.EDUraeburn', 2, 32, - ('01dbee7f4a9e243e9' - '88b62c73cda935da05' - '378b93244ec8f48a99' - 'e61ad799d86')), - ('password', 'ATHENA.MIT.EDUraeburn', 1200, 32, - ('5c08eb61fdf71e' - '4e4ec3cf6ba1f55' - '12ba7e52ddbc5e51' - '42f708a31e2e62b1e13')), - ('X' * 64, 'pass phrase equals block size', 1200, 32, - ('139c30c0966bc32ba' - '55fdbf212530ac9c5' - 'ec59f1a452f5cc9ad' - '940fea0598ed1')), - ('X' * 65, 'pass phrase exceeds block size', 1200, 32, - ('9ccad6d468770cd' - '51b10e6a68721be6' - '11a8b4d282601db3' - 'b36be9246915ec82a')) - ]: - self.assertEqual( - pbkdf2_hex(data=password, - salt=salt, - iterations=iterations, - keylen=keylen), - expected_value) diff --git a/wqflask/tests/wqflask/test_user_login.py b/wqflask/tests/wqflask/test_user_login.py deleted file mode 100644 index 61cd9ab9..00000000 --- a/wqflask/tests/wqflask/test_user_login.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Test cases for some methods in login.py""" - -import unittest -from wqflask.user_login import encode_password - - -class TestUserLogin(unittest.TestCase): - def test_encode_password(self): - """ - Test encode password - """ - pass_gen_fields = { - "salt": "salt", - "hashfunc": "sha1", - "iterations": 4096, - "keylength": 20, - } - self.assertEqual( - encode_password(pass_gen_fields, - "password").get("password"), - '4b007901b765489abead49d926f721d065a429c1') diff --git a/wqflask/tests/wqflask/test_user_session.py b/wqflask/tests/wqflask/test_user_session.py deleted file mode 100644 index ebb0334a..00000000 --- a/wqflask/tests/wqflask/test_user_session.py +++ /dev/null @@ -1,15 +0,0 @@ -"""Test cases for some methods in user_session.py""" - -import unittest -from wqflask.user_session import verify_cookie - - -class TestUserSession(unittest.TestCase): - def test_verify_cookie(self): - """ - Test cookie verification - """ - self.assertEqual( - "3f4c1dbf-5b56-4260-87d6-f35445bda37e", - verify_cookie(("3f4c1dbf-5b56-4260-87d6-" - "f35445bda37e:af4fcf5eace9e7c864ce"))) diff --git a/wqflask/wqflask/templates/glossary.html b/wqflask/wqflask/templates/glossary.html index 988297d3..80e74ceb 100644 --- a/wqflask/wqflask/templates/glossary.html +++ b/wqflask/wqflask/templates/glossary.html @@ -3,5 +3,5 @@ {% block title %}Glossary{% endblock %} {% block content %} -Test +

Test

{% endblock %} -- cgit v1.2.3 From 390dcc3c46495a8e316df36ceb57dae2089456da Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 19:14:24 +0300 Subject: Remove encoding header for file In python3 the default encoding is utf-8 so this is redundant. --- wqflask/tests/unit/utility/test_hmac.py | 1 - wqflask/wqflask/views.py | 4 +--- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/wqflask/tests/unit/utility/test_hmac.py b/wqflask/tests/unit/utility/test_hmac.py index 4e3652f8..13d6261d 100644 --- a/wqflask/tests/unit/utility/test_hmac.py +++ b/wqflask/tests/unit/utility/test_hmac.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """Test hmac utility functions""" import unittest diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py index 08673f79..b7c4d142 100644 --- a/wqflask/wqflask/views.py +++ b/wqflask/wqflask/views.py @@ -1,6 +1,4 @@ -# -*- coding: utf-8 -*- -# -# Main routing table for GN2 +"""Main routing table for GN2""" import traceback # for error page import os # for error gifs -- cgit v1.2.3 From 4b6d964ccf484225f568361b3bc6136a758cd475 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Tue, 3 Nov 2020 20:15:39 +0300 Subject: add tests for the get_categorical_variables function --- wqflask/tests/wqflask/show_trait/test_show_trait.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index ec45d558..f7133292 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -155,7 +155,22 @@ class TestTraits(unittest.TestCase): self.assertEqual(get_categorical_variables(trait, sample_list), []) def test_get_categorical_variables_with_sample_attributes(self): - pass + this_trait=TraitObject({"data":{ + "Gene1":TraitObject({"extra_attributes":{"ex1":"ex1value"}}), + "Gene2":TraitObject({"extra_attributes":{"ex2":"ex2value"}}), + "Gene3":TraitObject({"extra_attributes":{"ex3":"ex3value"}}) + }}) + sample_list=TraitObject({"attributes":{ + "sample_attribute_1":TraitObject({"name":"ex1"}), + "sample_attribute_2":TraitObject({"name":"ex2"}), + "sample_attribute_3":TraitObject({"name":"ex3"}), + "sample_attribute_4":TraitObject({"name":"not_in_extra_attributes"}), + + + }}) + + results=get_categorical_variables(this_trait,sample_list) + self.assertEqual(["ex1","ex2","ex3","not_in_extra_attributes"],results) def test_get_trait_units(self): """test for getting trait units""" -- cgit v1.2.3 From 68f71e11343789266ccac6e079dd0bc1b16d407f Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Tue, 3 Nov 2020 20:23:19 +0300 Subject: add pep8 formatting --- wqflask/tests/wqflask/show_trait/test_show_trait.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index f7133292..f41b7288 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -155,6 +155,7 @@ class TestTraits(unittest.TestCase): self.assertEqual(get_categorical_variables(trait, sample_list), []) def test_get_categorical_variables_with_sample_attributes(self): + """test for getting categorical variable names with no samples""" this_trait=TraitObject({"data":{ "Gene1":TraitObject({"extra_attributes":{"ex1":"ex1value"}}), "Gene2":TraitObject({"extra_attributes":{"ex2":"ex2value"}}), @@ -169,6 +170,8 @@ class TestTraits(unittest.TestCase): }}) + # + results=get_categorical_variables(this_trait,sample_list) self.assertEqual(["ex1","ex2","ex3","not_in_extra_attributes"],results) -- cgit v1.2.3 From 8630b727d80f52b3e846c9eb3bf7dd4e844fa626 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Tue, 3 Nov 2020 20:34:00 +0300 Subject: remove unnecessary comments --- .../tests/wqflask/show_trait/test_show_trait.py | 35 +++++++++++----------- 1 file changed, 17 insertions(+), 18 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index f41b7288..c617e9f2 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -156,24 +156,23 @@ class TestTraits(unittest.TestCase): def test_get_categorical_variables_with_sample_attributes(self): """test for getting categorical variable names with no samples""" - this_trait=TraitObject({"data":{ - "Gene1":TraitObject({"extra_attributes":{"ex1":"ex1value"}}), - "Gene2":TraitObject({"extra_attributes":{"ex2":"ex2value"}}), - "Gene3":TraitObject({"extra_attributes":{"ex3":"ex3value"}}) - }}) - sample_list=TraitObject({"attributes":{ - "sample_attribute_1":TraitObject({"name":"ex1"}), - "sample_attribute_2":TraitObject({"name":"ex2"}), - "sample_attribute_3":TraitObject({"name":"ex3"}), - "sample_attribute_4":TraitObject({"name":"not_in_extra_attributes"}), - - - }}) - - # - - results=get_categorical_variables(this_trait,sample_list) - self.assertEqual(["ex1","ex2","ex3","not_in_extra_attributes"],results) + this_trait = TraitObject({"data": { + "Gene1": TraitObject({"extra_attributes": {"ex1": "ex1value"}}), + "Gene2": TraitObject({"extra_attributes": {"ex2": "ex2value"}}), + "Gene3": TraitObject({"extra_attributes": {"ex3": "ex3value"}}) + }}) + sample_list = TraitObject({"attributes": { + "sample_attribute_1": TraitObject({"name": "ex1"}), + "sample_attribute_2": TraitObject({"name": "ex2"}), + "sample_attribute_3": TraitObject({"name": "ex3"}), + "sample_attribute_4": TraitObject({"name": "not_in_extra_attributes"}), + + + }}) + + results = get_categorical_variables(this_trait, sample_list) + self.assertEqual( + ["ex1", "ex2", "ex3", "not_in_extra_attributes"], results) def test_get_trait_units(self): """test for getting trait units""" -- cgit v1.2.3 From ddd071b2bd50d243c01c335e06955316df08fc45 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 19:16:17 +0300 Subject: Rename glossary.py to markdown_routes.py Evary page with markdown content will have routes added in markdown_routes.py * markdown_routes.py: New file. * wqflask/wqflask/glossary.py: Delete it. * wqflask/wqflask/__init__.py: Update import. --- wqflask/wqflask/__init__.py | 2 +- wqflask/wqflask/glossary.py | 9 --------- wqflask/wqflask/markdown_routes.py | 29 +++++++++++++++++++++++++++++ 3 files changed, 30 insertions(+), 10 deletions(-) delete mode 100644 wqflask/wqflask/glossary.py create mode 100644 wqflask/wqflask/markdown_routes.py diff --git a/wqflask/wqflask/__init__.py b/wqflask/wqflask/__init__.py index a3870ce6..874cde17 100644 --- a/wqflask/wqflask/__init__.py +++ b/wqflask/wqflask/__init__.py @@ -6,7 +6,7 @@ import jinja2 from flask import g from flask import Flask from utility import formatting -from wqflask.glossary import glossary_blueprint +from wqflask.markdown_routes import glossary_blueprint app = Flask(__name__) diff --git a/wqflask/wqflask/glossary.py b/wqflask/wqflask/glossary.py deleted file mode 100644 index a44e7c45..00000000 --- a/wqflask/wqflask/glossary.py +++ /dev/null @@ -1,9 +0,0 @@ -from flask import Blueprint -from flask import render_template - -glossary_blueprint = Blueprint('glossary_blueprint', __name__) - - -@glossary_blueprint.route('/') -def glossary(): - return render_template("glossary.html"), 200 diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py new file mode 100644 index 00000000..e7e3c33e --- /dev/null +++ b/wqflask/wqflask/markdown_routes.py @@ -0,0 +1,29 @@ +"""Markdown routes + +Render pages from github, or if they are unavailable, look for it else where +""" +import requests +import mistune + +from flask import Blueprint +from flask import render_template + +glossary_blueprint = Blueprint('glossary_blueprint', __name__) + + +@glossary_blueprint.route('/') +def glossary(): + markdown_url = ("https://raw.githubusercontent.com" + "/genenetwork/genenetwork2/" + "wqflask/wqflask/static" + "/glossary.md") + md_content = requests.get(markdown_url) + if md_content.status_code == 200: + return render_template( + "glossary_html", + rendered_markdown=mistune.html( + md_content.content.decode("utf-8"))), 200 + + return render_template( + "glossary.html", + rendered_markdown=mistune.html("# Github Down!")), 200 -- cgit v1.2.3 From f2ffa6a8bcd94a3435a19d82700465c4b9508f00 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 20:54:09 +0300 Subject: Update glossary template with markdown content --- wqflask/wqflask/templates/glossary.html | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/wqflask/wqflask/templates/glossary.html b/wqflask/wqflask/templates/glossary.html index 80e74ceb..718baf13 100644 --- a/wqflask/wqflask/templates/glossary.html +++ b/wqflask/wqflask/templates/glossary.html @@ -3,5 +3,7 @@ {% block title %}Glossary{% endblock %} {% block content %} -

Test

+ +{{ rendered_markdown|safe }} + {% endblock %} -- cgit v1.2.3 From f31fe80702cd24fa3687e7254e9ad212340715fd Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 21:05:52 +0300 Subject: Add simple md content in glossary.md --- wqflask/wqflask/static/markdown/glossary.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 wqflask/wqflask/static/markdown/glossary.md diff --git a/wqflask/wqflask/static/markdown/glossary.md b/wqflask/wqflask/static/markdown/glossary.md new file mode 100644 index 00000000..241dbf0a --- /dev/null +++ b/wqflask/wqflask/static/markdown/glossary.md @@ -0,0 +1 @@ +# Content -- cgit v1.2.3 From b526fc6f6b6f450e169c01db5ffea92f94512393 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 21:54:36 +0300 Subject: Use python3-genenetwork2 on commit bdce85d in container * .github/workflows/main.yml: Update gn2 container. --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index f27feb5f..d76a5433 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -11,7 +11,7 @@ on: jobs: unittest: runs-on: ubuntu-latest - container: bonfacekilz/python3-genenetwork2:latest + container: bonfacekilz/python3-genenetwork2:bdce85d steps: # First start with mariadb set then checkout. The checkout gives -- cgit v1.2.3 From a7622fc3d996407799cec166968c1e56baf07ea9 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 21:59:49 +0300 Subject: Move logic for fetching md files to it's own function * wqflask/wqflask/markdown_routes.py (render_markdown): New function. (glossary): use render_markdown function. --- wqflask/wqflask/markdown_routes.py | 31 ++++++++++++++++++++----------- 1 file changed, 20 insertions(+), 11 deletions(-) diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py index e7e3c33e..33092947 100644 --- a/wqflask/wqflask/markdown_routes.py +++ b/wqflask/wqflask/markdown_routes.py @@ -2,6 +2,7 @@ Render pages from github, or if they are unavailable, look for it else where """ +import os import requests import mistune @@ -11,19 +12,27 @@ from flask import render_template glossary_blueprint = Blueprint('glossary_blueprint', __name__) -@glossary_blueprint.route('/') -def glossary(): - markdown_url = ("https://raw.githubusercontent.com" - "/genenetwork/genenetwork2/" - "wqflask/wqflask/static" - "/glossary.md") +def render_markdown(file_name): + """Try to fetch the file name from Github and if that fails, try to +look for it inside the file system + + """ + markdown_url = (f"https://raw.githubusercontent.com" + f"/genenetwork/genenetwork2/" + f"wqflask/wqflask/static/" + f"{file_name}") md_content = requests.get(markdown_url) if md_content.status_code == 200: - return render_template( - "glossary_html", - rendered_markdown=mistune.html( - md_content.content.decode("utf-8"))), 200 + return mistune.html(md_content.content.decode("utf-8")) + + with open(os.path.join(os.path.abspath(os.path.dirname(__file__)), + f"static/markdown/{file_name}")) as md_file: + markdown = md_file.read() + return mistune.html(markdown) + +@glossary_blueprint.route('/') +def glossary(): return render_template( "glossary.html", - rendered_markdown=mistune.html("# Github Down!")), 200 + rendered_markdown=render_markdown("glossary.md")), 200 -- cgit v1.2.3 From bd8c764f49703ecc5cf25153144ad439c1ca2442 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 22:08:59 +0300 Subject: Update issue and PR templates * .github/ISSUE_TEMPLATE/bug_report.md: Provide explanations in commented out blocks. * .github/ISSUE_TEMPLATE/feature_request.md: Ditto. * .github/PULL_REQUEST_TEMPLATE.md: Ditto. * .github/ISSUE_TEMPLATE/user_story.md: Ditto. --- .github/ISSUE_TEMPLATE/bug_report.md | 16 ++++++++-------- .github/ISSUE_TEMPLATE/feature_request.md | 23 +++++++++++++++-------- .github/ISSUE_TEMPLATE/user_story.md | 8 ++++---- .github/PULL_REQUEST_TEMPLATE.md | 9 +++++++++ 4 files changed, 36 insertions(+), 20 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 7d7e34a5..48bad39b 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -9,26 +9,26 @@ assignees: '' **Describe the bug** -A clear and concise description of what the bug is. + **To Reproduce** -Steps to reproduce the behavior + **Expected behavior** -A clear and concise description of what you expected to happen. + **Screenshots** -If applicable, add screenshots to help explain your problem. + **Environment setup (please complete the following information):** -- OS: [e.g. Linux] -- Guix Version (optional) -- [Anything else you think is relevant] + + + **Additional context** -Add any other context about the problem here. + diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index 254754c6..eb6c3e4b 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -9,26 +9,33 @@ assignees: '' ## Is your feature request related to a problem? Please describe. -A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + + + ## Describe the solution you'd like -A clear and concise description of what you want to happen. + ## Describe alternatives you've considered -A clear and concise description of any alternative solutions or features you've considered. + ## User Stories (optional) -As a _[role or persona]_, I want _[goal/ need]_ so that _[why]_ + + + + + -**Feature:** _[Brief description of feature]_ + -_[Any additional descriptions on feature]_ + ## Additional context -Add any other context or screenshots about the feature request here. + diff --git a/.github/ISSUE_TEMPLATE/user_story.md b/.github/ISSUE_TEMPLATE/user_story.md index 2682fc98..52ae775d 100644 --- a/.github/ISSUE_TEMPLATE/user_story.md +++ b/.github/ISSUE_TEMPLATE/user_story.md @@ -7,10 +7,10 @@ assignees: '' --- -As a _[role or persona]_, I want _[goal/ need]_ so that _[why]_ + -**Feature:** _[Brief description of feature]_ + -_[Any additional descriptions on feature]_ + -**Scenario:** Please use _[Gherkin](https://cucumber.io/docs/gherkin/reference/)_ here + diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 223e24ef..f3371183 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,11 +1,20 @@ #### Description + #### How should this be tested? + #### Any background context you want to provide? + + #### What are the relevant pivotal tracker stories? + + #### Screenshots (if appropriate) #### Questions + + -- cgit v1.2.3 From 74550ef0c76a941c473c8d024ccc0a0403631c49 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 22:03:26 +0300 Subject: Add basic structure for "/glossary" routes test --- wqflask/tests/integration/test_markdown_routes.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 wqflask/tests/integration/test_markdown_routes.py diff --git a/wqflask/tests/integration/test_markdown_routes.py b/wqflask/tests/integration/test_markdown_routes.py new file mode 100644 index 00000000..5e3e5045 --- /dev/null +++ b/wqflask/tests/integration/test_markdown_routes.py @@ -0,0 +1,21 @@ +"Integration tests for markdown routes" +import unittest + +from bs4 import BeautifulSoup + +from wqflask import app + + +class TestGenMenu(unittest.TestCase): + """Tests for glossary""" + + def setUp(self): + self.app = app.test_client() + + def tearDown(self): + pass + + def test_glossary_page(self): + """Test that the glossary page is rendered properly""" + response = self.app.get('/glossary', follow_redirects=True) + pass -- cgit v1.2.3 From bb46ab063cc86525946563c809a896532d87147a Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 22:45:44 +0300 Subject: Add basic tests for rendering_markdown * wqflask/tests/unit/wqflask/test_markdown_routes.py: New tests. --- wqflask/tests/unit/wqflask/test_markdown_routes.py | 43 ++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 wqflask/tests/unit/wqflask/test_markdown_routes.py diff --git a/wqflask/tests/unit/wqflask/test_markdown_routes.py b/wqflask/tests/unit/wqflask/test_markdown_routes.py new file mode 100644 index 00000000..8b6f7490 --- /dev/null +++ b/wqflask/tests/unit/wqflask/test_markdown_routes.py @@ -0,0 +1,43 @@ +"""Test functions in markdown utils""" + +import unittest +from unittest import mock + +from wqflask.markdown_routes import render_markdown + + +class MockRequests404: + @property + def status_code(): + return 404 + +class MockRequests200: + @property + def status_code(): + return 200 + + @property + def content(): + return """ + # Glossary + + This is some content + + ## Sub-heading + This is another sub-heading + """ + +class TestMarkdownRoutesFunctions(unittest.TestCase): + """Test cases for functions in markdown_routes""" + + @mock.patch('wqflask.markdown_routes.requests.get') + def test_render_markdown(self, requests_mock): + requests_mock.return_value = MockRequests404 + markdown_content = render_markdown("glossary.md") + requests_mock.assert_called_with( + "https://raw.githubusercontent.com" + "/genenetwork/genenetwork2/" + "wqflask/wqflask/static/" + "glossary.md") + self.assertEqual("

Content

\n", + markdown_content) -- cgit v1.2.3 From 3ca276a1e9d3aa29f037696b640c64b2d8629c7f Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 22:50:52 +0300 Subject: Delete file * wqflask/tests/integration/test_glossary.py: Delete it. Earlier renamed to test_markdown_routes. --- wqflask/tests/integration/test_glossary.py | 28 ---------------------------- 1 file changed, 28 deletions(-) delete mode 100644 wqflask/tests/integration/test_glossary.py diff --git a/wqflask/tests/integration/test_glossary.py b/wqflask/tests/integration/test_glossary.py deleted file mode 100644 index c9f1e62a..00000000 --- a/wqflask/tests/integration/test_glossary.py +++ /dev/null @@ -1,28 +0,0 @@ -"Integration tests for glossary" -import unittest - -from bs4 import BeautifulSoup - -from wqflask import app - - -class TestGenMenu(unittest.TestCase): - """Tests for glossary""" - - def setUp(self): - self.app = app.test_client() - - def tearDown(self): - pass - - def test_glossary_page(self): - """Test that the glossary page is rendered properly""" - response = self.app.get('/glossary', follow_redirects=True) - html_content = BeautifulSoup(response.data, "lxml") - self.assertEqual(html_content.find("title").get_text(), - "Glossary GeneNetwork 2") - self.assertEqual( - html_content.find( - 'p', - attrs={'id': 'mytest'}).get_text(), - "Test") -- cgit v1.2.3 From 1d00c9ad8348f7d58e3a6371a67df479d78ae45e Mon Sep 17 00:00:00 2001 From: zsloan Date: Tue, 3 Nov 2020 13:52:29 -0600 Subject: Joined sample names in primary_samples and all_samples with commas because this list was being split to get the N of samples; not sure why this was working before * wqflask/wqflask/show_trait/show_trait.py primary_samples and all_samples passed as hidden form inputs now have names split by commas so they can be counted correctly when displaying the N on the mapping loading screen --- wqflask/wqflask/show_trait/show_trait.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/wqflask/wqflask/show_trait/show_trait.py b/wqflask/wqflask/show_trait/show_trait.py index edf9638c..25ba1a1d 100644 --- a/wqflask/wqflask/show_trait/show_trait.py +++ b/wqflask/wqflask/show_trait/show_trait.py @@ -228,8 +228,8 @@ class ShowTrait(object): hddn = OrderedDict() if self.dataset.group.allsamples: - hddn['allsamples'] = ''.join(self.dataset.group.allsamples) - hddn['primary_samples'] = ''.join(self.primary_sample_names) + hddn['allsamples'] = ','.join(self.dataset.group.allsamples) + hddn['primary_samples'] = ','.join(self.primary_sample_names) hddn['trait_id'] = self.trait_id hddn['trait_display_name'] = self.this_trait.display_name hddn['dataset'] = self.dataset.name -- cgit v1.2.3 From 5f6756f05baeeb6be34a079726d0df749a0557ec Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 3 Nov 2020 23:04:53 +0300 Subject: Fix false-positive tests * wqflask/tests/unit/wqflask/test_markdown_routes.py: (MockRequests404): Pass self in all properties. (MockRequests200): Ditto. (test_render_markdown): Rename to test_render_markdown_when_fetching_locally. (test_render_markdown_when_fetching_remotely): New test. --- wqflask/tests/unit/wqflask/test_markdown_routes.py | 37 +++++++++++++++------- 1 file changed, 26 insertions(+), 11 deletions(-) diff --git a/wqflask/tests/unit/wqflask/test_markdown_routes.py b/wqflask/tests/unit/wqflask/test_markdown_routes.py index 8b6f7490..3de14276 100644 --- a/wqflask/tests/unit/wqflask/test_markdown_routes.py +++ b/wqflask/tests/unit/wqflask/test_markdown_routes.py @@ -8,31 +8,30 @@ from wqflask.markdown_routes import render_markdown class MockRequests404: @property - def status_code(): + def status_code(self): return 404 class MockRequests200: @property - def status_code(): + def status_code(self): return 200 @property - def content(): - return """ - # Glossary + def content(self): + return b""" +# Glossary +This is some content - This is some content - - ## Sub-heading - This is another sub-heading +## Sub-heading +This is another sub-heading """ class TestMarkdownRoutesFunctions(unittest.TestCase): """Test cases for functions in markdown_routes""" @mock.patch('wqflask.markdown_routes.requests.get') - def test_render_markdown(self, requests_mock): - requests_mock.return_value = MockRequests404 + def test_render_markdown_when_fetching_locally(self, requests_mock): + requests_mock.return_value = MockRequests404() markdown_content = render_markdown("glossary.md") requests_mock.assert_called_with( "https://raw.githubusercontent.com" @@ -41,3 +40,19 @@ class TestMarkdownRoutesFunctions(unittest.TestCase): "glossary.md") self.assertEqual("

Content

\n", markdown_content) + + @mock.patch('wqflask.markdown_routes.requests.get') + def test_render_markdown_when_fetching_remotely(self, requests_mock): + requests_mock.return_value = MockRequests200() + markdown_content = render_markdown("glossary.md") + requests_mock.assert_called_with( + "https://raw.githubusercontent.com" + "/genenetwork/genenetwork2/" + "wqflask/wqflask/static/" + "glossary.md") + self.assertEqual("""

Glossary

+

This is some content

+

Sub-heading

+

This is another sub-heading

+""", + markdown_content) -- cgit v1.2.3 From 5e08316b491a6225eddbc27cfdd799cff1bc0283 Mon Sep 17 00:00:00 2001 From: zsloan Date: Tue, 3 Nov 2020 15:18:25 -0600 Subject: Fixed a sort used when doing the haplotype analysis to be compatible with Python 3 * wqflask/wqflask/marker_regression/display_mapping_results.py - The arguments for sort are different in Python 3, so it was changed to be compatible --- wqflask/wqflask/marker_regression/display_mapping_results.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/wqflask/wqflask/marker_regression/display_mapping_results.py b/wqflask/wqflask/marker_regression/display_mapping_results.py index 7e43f6bd..b5686b70 100644 --- a/wqflask/wqflask/marker_regression/display_mapping_results.py +++ b/wqflask/wqflask/marker_regression/display_mapping_results.py @@ -1460,7 +1460,7 @@ class DisplayMappingResults(object): else: continue - smd.sort(lambda A, B: cmp(A.value, B.value)) + smd.sort(key = lambda A: A.value) smd.reverse() samplelist = list(self.genotype.prgy) -- cgit v1.2.3 From 96b095aa83ed113250442e4c6f180c7dd66d3e0e Mon Sep 17 00:00:00 2001 From: zsloan Date: Tue, 3 Nov 2020 15:20:05 -0600 Subject: Changed the syntax for the "sort" function in the docstring of GeneralObject to be compatible with Python 3 syntax * wqflask/base/GeneralObject.py - edited docstring to be correct for Python 3 --- wqflask/base/GeneralObject.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/wqflask/base/GeneralObject.py b/wqflask/base/GeneralObject.py index 0122ee32..249195e2 100644 --- a/wqflask/base/GeneralObject.py +++ b/wqflask/base/GeneralObject.py @@ -28,7 +28,7 @@ class GeneralObject: """ Base class to define an Object. a = [Spam(1, 4), Spam(9, 3), Spam(4,6)] - a.sort(lambda x, y: cmp(x.eggs, y.eggs)) + a.sort(key = lambda x: x.eggs) """ def __init__(self, *args, **kw): -- cgit v1.2.3 From 13db311203483f5ef6082e49e15158840ce072ef Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Wed, 4 Nov 2020 02:24:47 +0300 Subject: Use python3-genenetwork2 docker image on commit 0bf4ee6 --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index d76a5433..c78f6d85 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -11,7 +11,7 @@ on: jobs: unittest: runs-on: ubuntu-latest - container: bonfacekilz/python3-genenetwork2:bdce85d + container: bonfacekilz/python3-genenetwork2:0bf4ee6 steps: # First start with mariadb set then checkout. The checkout gives -- cgit v1.2.3 From 23915d8102b8a05930cf3fd16d651a460ebd2f58 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Wed, 4 Nov 2020 09:49:13 +0300 Subject: remove whitespace and assertIs --- .../tests/wqflask/show_trait/test_show_trait.py | 30 ++++++++++++---------- 1 file changed, 17 insertions(+), 13 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index c617e9f2..04c68045 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -2,11 +2,15 @@ import unittest from unittest import mock from wqflask import app -from wqflask.show_trait.show_trait import (check_if_attr_exists, get_ncbi_summary, - has_num_cases, get_table_widths, - get_categorical_variables, - get_trait_units, get_nearest_marker, get_genotype_scales, requests) - +from wqflask.show_trait.show_trait import check_if_attr_exists +from wqflask.show_trait.show_trait import get_ncbi_summary +from wqflask.show_trait.show_trait import has_num_cases +from wqflask.show_trait.show_trait import get_table_widths +from wqflask.show_trait.show_trait import get_categorical_variables +from wqflask.show_trait.show_trait import get_trait_units +from wqflask.show_trait.show_trait import get_nearest_marker +from wqflask.show_trait.show_trait import get_genotype_scales +from wqflask.show_trait.show_trait import requests class TraitObject: def __init__(self, obj): @@ -29,8 +33,8 @@ class TestTraits(unittest.TestCase): results = check_if_attr_exists(trait_obj, "id_type") result2 = check_if_attr_exists(trait_obj2, "sample_name") self.assertIsInstance(trait_obj, TraitObject) - self.assertIs(results, True) - self.assertIs(result2, True) + self.assertTrue(results) + self.assertTrue(result2) def test_check_if_attr_exists_empty_attr(self): """test if attributes exists with false return""" @@ -38,14 +42,14 @@ class TestTraits(unittest.TestCase): trait_obj2 = TraitObject({"group": None}) result = check_if_attr_exists(trait_obj, "sample") result2 = check_if_attr_exists(trait_obj, "group") - self.assertIs(result, False) - self.assertIs(result2, False) + self.assertFalse(result) + self.assertFalse(result2) def test_check_if_attr_exists_falsey(self): """check if attribute exists with empty attributes""" trait_obj = TraitObject({}) results = check_if_attr_exists(trait_obj, "any") - self.assertIs(results, False) + self.assertFalse(results) @mock.patch("wqflask.show_trait.show_trait.requests.get") @mock.patch("wqflask.show_trait.show_trait.check_if_attr_exists") @@ -97,7 +101,7 @@ class TestTraits(unittest.TestCase): create_dataset = TraitObject({"type": "ProbeSet"}) create_trait = TraitObject({"dataset": create_dataset}) - self.assertIs(has_num_cases(create_trait), False) + self.assertFalse(has_num_cases(create_trait)) def test_hash_num_cases_no_probeset(self): """test for hash num cases with dataset.type not Probeset""" @@ -123,8 +127,8 @@ class TestTraits(unittest.TestCase): results = has_num_cases(create_trait) - self.assertIs(has_num_cases(create_trait), True) - self.assertIs(has_num_cases(create_trait2), False) + self.assertTrue(has_num_cases(create_trait)) + self.assertFalse(has_num_cases(create_trait2)) def test_get_table_widths(self): """test for getting table widths""" -- cgit v1.2.3 From 9d2134202c0435ad4a18e0ac74ce4a99508f1a76 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Wed, 4 Nov 2020 09:57:35 +0300 Subject: remove whitespace --- wqflask/tests/wqflask/show_trait/test_show_trait.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index 04c68045..f82a4c04 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -3,7 +3,7 @@ import unittest from unittest import mock from wqflask import app from wqflask.show_trait.show_trait import check_if_attr_exists -from wqflask.show_trait.show_trait import get_ncbi_summary +from wqflask.show_trait.show_trait import get_ncbi_summary from wqflask.show_trait.show_trait import has_num_cases from wqflask.show_trait.show_trait import get_table_widths from wqflask.show_trait.show_trait import get_categorical_variables @@ -12,6 +12,7 @@ from wqflask.show_trait.show_trait import get_nearest_marker from wqflask.show_trait.show_trait import get_genotype_scales from wqflask.show_trait.show_trait import requests + class TraitObject: def __init__(self, obj): for key, value in obj.items(): @@ -169,9 +170,7 @@ class TestTraits(unittest.TestCase): "sample_attribute_1": TraitObject({"name": "ex1"}), "sample_attribute_2": TraitObject({"name": "ex2"}), "sample_attribute_3": TraitObject({"name": "ex3"}), - "sample_attribute_4": TraitObject({"name": "not_in_extra_attributes"}), - - + "sample_attribute_4": TraitObject({"name": "not_in_extra_attributes"}) }}) results = get_categorical_variables(this_trait, sample_list) -- cgit v1.2.3 From 1ef7c3cd7bc148bac8a6b47f62ba02c3819bf1f3 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Wed, 4 Nov 2020 10:08:32 +0300 Subject: remove whitespace in line 119-120 --- wqflask/tests/wqflask/show_trait/test_show_trait.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index f82a4c04..70061e2d 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -117,8 +117,6 @@ class TestTraits(unittest.TestCase): "nm1": TraitObject({"num_cases": False}), "nm2": TraitObject({"num_cases": False}), "nm3": TraitObject({"num_cases": False}) - - } create_trait = TraitObject( -- cgit v1.2.3 From f90e77fab78c4504ac9dabb188459ab5d03ac287 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Wed, 4 Nov 2020 10:15:05 +0300 Subject: remove extra whitespace --- wqflask/tests/wqflask/show_trait/test_show_trait.py | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index 70061e2d..ce850c8b 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -67,11 +67,8 @@ class TestTraits(unittest.TestCase): } } """ - get_return_obj = TraitObject({"content": content_json_string}) - mock_get.return_value = get_return_obj - results = get_ncbi_summary(trait) mock_exists.assert_called_once() mock_get.assert_called_once_with(f"http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=gene&id={trait.geneid}&retmode=json") @@ -93,7 +90,6 @@ class TestTraits(unittest.TestCase): } } """ - results = get_ncbi_summary(trait) self.assertEqual(results, None) @@ -101,13 +97,11 @@ class TestTraits(unittest.TestCase): """test for hash num_cases with dataset.type set to Probeset""" create_dataset = TraitObject({"type": "ProbeSet"}) create_trait = TraitObject({"dataset": create_dataset}) - self.assertFalse(has_num_cases(create_trait)) def test_hash_num_cases_no_probeset(self): """test for hash num cases with dataset.type not Probeset""" create_dataset = TraitObject({"type": "Temp"}) - construct_data = { "nm1": TraitObject({"num_cases": False}), "nm2": TraitObject({"num_cases": True}), @@ -125,7 +119,6 @@ class TestTraits(unittest.TestCase): {"dataset": create_dataset, "data": construct_data2}) results = has_num_cases(create_trait) - self.assertTrue(has_num_cases(create_trait)) self.assertFalse(has_num_cases(create_trait2)) @@ -144,7 +137,6 @@ class TestTraits(unittest.TestCase): expected_with_numcase = (450, "750px") expected_no_numcase = (450, "670px") expected_one_sample = (250, "540px") - self.assertEqual(results_with_numcase, expected_with_numcase) self.assertEqual(result_no_numcase, expected_no_numcase) self.assertEqual(results_one_sample, @@ -154,7 +146,6 @@ class TestTraits(unittest.TestCase): """test for getting categorical variable names with no samples""" trait = TraitObject({}) sample_list = TraitObject({"se_exists": True, "attributes": []}) - self.assertEqual(get_categorical_variables(trait, sample_list), []) def test_get_categorical_variables_with_sample_attributes(self): @@ -179,9 +170,7 @@ class TestTraits(unittest.TestCase): """test for getting trait units""" trait = TraitObject( {"description_fmt": "[this is a description] another test [N/A]"}) - trait_no_unit_type = TraitObject({"description_fmt": ""}) - results = get_trait_units(trait) results_no_unit = get_trait_units(trait_no_unit_type) self.assertEqual(results, "this is a descriptionN/A") @@ -195,13 +184,9 @@ class TestTraits(unittest.TestCase): ["Geno1", "Geno2"], ["Geno3"]] trait = TraitObject({"locus_chr": "test_chr", "locus_mb": "test_mb"}) - group_name = TraitObject({"name": "group_name"}) - this_db = TraitObject({"group": group_name}) - results_with_item_db = get_nearest_marker(trait, this_db) - called_with_value = """SELECT Geno.Name FROM Geno, GenoXRef, GenoFreeze WHERE Geno.Chr = 'test_chr' AND @@ -218,11 +203,9 @@ class TestTraits(unittest.TestCase): def test_get_nearest_marker_empty_db(self, mock_db): """test for getting nearest marker with empty db""" mock_db.db.execute.return_value.fetchall.return_value = [] - trait = TraitObject({"locus_chr": "test_chr", "locus_mb": "test_mb"}) group_name = TraitObject({"name": "group_name"}) this_db = TraitObject({"group": group_name}) - results_empty_db = get_nearest_marker(trait, this_db) mock_db.db.execute.assert_called_once() self.assertEqual(results_empty_db, "") @@ -240,7 +223,6 @@ class TestTraits(unittest.TestCase): [["physic", "Mb"]]] results = get_genotype_scales(genofiles_list) - expected_results = { "~/data/files/f1": [["morgan", "cM"]], "~/data/files/f2": [["morgan", "cM"]], @@ -249,7 +231,6 @@ class TestTraits(unittest.TestCase): multiple_calls = [mock.call('~/data/files/f1'), mock.call('~/data/files/f2'), mock.call('~/data/files/f3')] - mock_get_scales.assert_has_calls(multiple_calls) self.assertEqual(results, expected_results) @@ -258,8 +239,6 @@ class TestTraits(unittest.TestCase): """test for getting genotype scales with genofile as a string""" file_location = "~/another_file_location" mock_get_scales.return_value = [["physic", "Mb"]] - expected_results = {f"{file_location}": [["physic", "Mb"]]} - self.assertEqual(get_genotype_scales(file_location), expected_results) mock_get_scales.assert_called_once_with(file_location) -- cgit v1.2.3 From 3f08f54fe3d42eca0ccf98831e4e339defa17720 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Wed, 4 Nov 2020 10:25:50 +0300 Subject: correct a typo --- wqflask/tests/wqflask/show_trait/test_show_trait.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index ce850c8b..24150738 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -112,7 +112,6 @@ class TestTraits(unittest.TestCase): "nm2": TraitObject({"num_cases": False}), "nm3": TraitObject({"num_cases": False}) } - create_trait = TraitObject( {"dataset": create_dataset, "data": construct_data}) create_trait2 = TraitObject( @@ -161,7 +160,6 @@ class TestTraits(unittest.TestCase): "sample_attribute_3": TraitObject({"name": "ex3"}), "sample_attribute_4": TraitObject({"name": "not_in_extra_attributes"}) }}) - results = get_categorical_variables(this_trait, sample_list) self.assertEqual( ["ex1", "ex2", "ex3", "not_in_extra_attributes"], results) -- cgit v1.2.3 From 5899240349f6a943dc4dc1093ffbec08646c0376 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Wed, 4 Nov 2020 17:41:53 +0300 Subject: Update glossary markdown file --- wqflask/wqflask/static/Congenic.png | Bin 0 -> 56578 bytes .../wqflask/static/images/Belknap_Fig1_1998.png | Bin 0 -> 117246 bytes wqflask/wqflask/static/images/Chrna1vsMyf6.gif | Bin 0 -> 59529 bytes wqflask/wqflask/static/images/Normal_Plot.gif | Bin 0 -> 47289 bytes wqflask/wqflask/static/images/SilverFig3_2.png | Bin 0 -> 61570 bytes wqflask/wqflask/static/images/SilverFig3_6.png | Bin 0 -> 22017 bytes wqflask/wqflask/static/images/Winsorize1.png | Bin 0 -> 15117 bytes wqflask/wqflask/static/images/Winsorize3.png | Bin 0 -> 17317 bytes wqflask/wqflask/static/markdown/glossary.md | 595 ++++++++++++++++++++- 9 files changed, 594 insertions(+), 1 deletion(-) create mode 100644 wqflask/wqflask/static/Congenic.png create mode 100644 wqflask/wqflask/static/images/Belknap_Fig1_1998.png create mode 100644 wqflask/wqflask/static/images/Chrna1vsMyf6.gif create mode 100644 wqflask/wqflask/static/images/Normal_Plot.gif create mode 100644 wqflask/wqflask/static/images/SilverFig3_2.png create mode 100644 wqflask/wqflask/static/images/SilverFig3_6.png create mode 100644 wqflask/wqflask/static/images/Winsorize1.png create mode 100644 wqflask/wqflask/static/images/Winsorize3.png diff --git a/wqflask/wqflask/static/Congenic.png b/wqflask/wqflask/static/Congenic.png new file mode 100644 index 00000000..8cd489a4 Binary files /dev/null and b/wqflask/wqflask/static/Congenic.png differ diff --git a/wqflask/wqflask/static/images/Belknap_Fig1_1998.png b/wqflask/wqflask/static/images/Belknap_Fig1_1998.png new file mode 100644 index 00000000..46305fa1 Binary files /dev/null and b/wqflask/wqflask/static/images/Belknap_Fig1_1998.png differ diff --git a/wqflask/wqflask/static/images/Chrna1vsMyf6.gif b/wqflask/wqflask/static/images/Chrna1vsMyf6.gif new file mode 100644 index 00000000..881a08e8 Binary files /dev/null and b/wqflask/wqflask/static/images/Chrna1vsMyf6.gif differ diff --git a/wqflask/wqflask/static/images/Normal_Plot.gif b/wqflask/wqflask/static/images/Normal_Plot.gif new file mode 100644 index 00000000..dc239f8e Binary files /dev/null and b/wqflask/wqflask/static/images/Normal_Plot.gif differ diff --git a/wqflask/wqflask/static/images/SilverFig3_2.png b/wqflask/wqflask/static/images/SilverFig3_2.png new file mode 100644 index 00000000..5b4b2c70 Binary files /dev/null and b/wqflask/wqflask/static/images/SilverFig3_2.png differ diff --git a/wqflask/wqflask/static/images/SilverFig3_6.png b/wqflask/wqflask/static/images/SilverFig3_6.png new file mode 100644 index 00000000..5b91d991 Binary files /dev/null and b/wqflask/wqflask/static/images/SilverFig3_6.png differ diff --git a/wqflask/wqflask/static/images/Winsorize1.png b/wqflask/wqflask/static/images/Winsorize1.png new file mode 100644 index 00000000..f3a65f29 Binary files /dev/null and b/wqflask/wqflask/static/images/Winsorize1.png differ diff --git a/wqflask/wqflask/static/images/Winsorize3.png b/wqflask/wqflask/static/images/Winsorize3.png new file mode 100644 index 00000000..a9ed95d6 Binary files /dev/null and b/wqflask/wqflask/static/images/Winsorize3.png differ diff --git a/wqflask/wqflask/static/markdown/glossary.md b/wqflask/wqflask/static/markdown/glossary.md index 241dbf0a..3c14ab78 100644 --- a/wqflask/wqflask/static/markdown/glossary.md +++ b/wqflask/wqflask/static/markdown/glossary.md @@ -1 +1,594 @@ -# Content +# Glossary of Terms and Features + +
+ +[A](#a) | [B](#b) | [C](#c)| [D](#d) | [E](#e) | [F](#f) | [G](#g) | [H](#h) | [I](#i) | [J](#j) | [K](#k) | [L](#l) | [M](#m) | [N](#n) | [O](#o) | [P](#p) | [Q](#q) | [R](#r) | [S](#s) | [T](#t) | [U](#u) | [V](#v) | [W](#w) | [X](#x) | [Y](#y) | [Z](#z) + +You are welcome to cite or reproduce these glossary +definitions. Please cite or link: Author AA. "Insert Glossary Term +Here." From The WebQTL Glossary--A GeneNetwork +Resource. gn1.genenetwork.org/glossary.html + +
+ +## A + +#### Additive Allele Effect: + +The additive allele effect is an estimate of the change in the average phenotype that would be produced by substituting a single allele of one type with that of another type (e.g., a replaced by A) in a population. In a standard F2 intercross between two inbred parental lines there are two alleles at every polymorphic locus that are often referred to as the little "a" allele and big "A" allele. F2 progeny inherit the a/a, a/A, or A/A genotypes at every genetic locus in a ratio close to 1:2:1. The additive effect is half of the difference between the mean of all cases that are homozygous for one parental allele (aa) compared to the mean of all cases that are homozygous for the other parental allele (AA): + +[(mean of AA cases)-(mean of aa cases)]/2 + +GeneNetwork displays the additive values on the far right of many trait/QTL maps, usually as red or green lines along the maps. The units of measurement of additive effects (and dominance effects) are defined by the trait itself and are shown in Trait Data and Analysis windows. For mRNA estimates these units are usually normalized log2 expression values. For this reason an additive effect of 0.5 units indicates that the A/A and a/a genotypes at a particular locus or marker differ by 1 unit (twice the effect of swapping a single A allele for an a allele). On this log2 scale this is equivalent to a 2-fold difference (2 raised to the power of 1). + +On the QTL map plots the polarity of allele effects is represented by the color of the line. For example, in mouse BXD family maps, if the DBA/2J allele produces higher values than the C57BL/6J allele then the additive effect line is colored in green. In contrast, if the C57BL/6J allele produces higher values then the line is colored in red. For computational purposes, C57BL/6J red values are considered negative. + +The dominance effects of alleles are also computed on maps for F2 populations (e.g., B6D2F2 and B6BTBRF2). Orange and purple line colors are used to distinguish the polarity of effects. Purple is the positive dominance effect that matches the polarity of the green additive effect, whereas orange is the negative dominance effect that matches the polarity of the red additive effect. [Please also see entry on Dominance Effects: Williams RW, Oct 15, 2004; Sept 3, 2005; Dec 4, 2005; Oct 25, 2011] + +[Go back to index](#index) + +
+ +#### Bootstrap: + +A bootstrap sample is a randomly drawn sample (or resample) that is taken from the original data set and that has the same number of samples as the original data set. In a single bootstrap sample, some cases will by chance be represented one or more times; other cases may not be represented at all (in other words, the sampling is done "with replacement" after each selection). To get a better intuitive feel for the method, imagine a bag of 26 Scrabble pieces that contain each letter of the English alphabet. In a bootstrap sample of these 26 pieces, you would shake the bag, insert your hand, and draw out one piece. You would then write down that letter on a piece of paper, and the place that Scrabble piece back in the bag in preparation for the next random selection. You would repeat this process (shake, draw, replace) 25 more times to generate a single bootstrap resample of the alphabet. Some letters will be represented several time in each sample and others will not be represented at al. If you repeat this procedure 1000 times you would have a set of bootstrap resamples of the type that GN uses to remap data sets. + +Bootstrap resampling is a method that can be used to estimate statistical parameters and error terms. GeneNetwork uses a bootstrap procedure to evaluate approximate confidence limits of QTL peaks using a method proposed by Peter Visscher and colleagues (1996). We generate 2000 bootstraps, remap each, and keep track of the location of the single locus with the highest LRS score locations (equivalent to a "letter" in the Scrabble example). The 2000 "best" locations are used to produce the yellow histograms plotted on some of the QTL maps. If the position of a QTL is firm, then the particular composition of the sample, will not shift the position of the QTL peak by very much. In such a case, the histogram of "best QTLs" (yellow bars in the maps) that is displayed in WebQTL maps will tend to have a sharp peak (the scale is the percentage of bootstrap resamples that fall into each bar of the bootstrap histogram). In contrast, if the the yellow bootstrap histograms are spread out along a chromosome, then the precise location of a QTL may not be accurate, even in the original correct data set. Bootstrap results naturally vary between runs due to the random generation of the samples. See the related entry "Frequency of Peak LRS." + +KNOWN PROBLEMS and INTERPRETATION of BOOTSTRAP RESULTS: The reliability of bootstrap analysis of QTL confidence intervals has been criticized by Manichaikul and colleagues (2006). Their work applies in particular to standard intercrosses and backcrosses in which markers are spaced every 2 cM. They recommend that confidence intervals be estimated either by a conventional 1.5 to 2.0 LOD drop-off interval or by a Bayes credible Interval method. + +There is a known flaw in the way in which GeneNetwork displays bootstrap results (Sept 2011). If a map has two or more adjacent markers with identical LOD score and identical strain distribution patterns, all of the bootstrap results are assigned incorrectly to just one of the "twin" markers. This results in a false perception of precision. + +QTL mapping methods can be highly sensitive to cases with very high or very low phenotype values (outliers). The bootstrap method does not provide protection against the effects of outliers and their effects on QTL maps. Make sure you review your data for outliers before mapping. Options include (1) Do nothing, (2) Delete the outliers and see what happens to your maps, (3) Winsorize the values of the outliers. You might try all three options and determine if your main results are stable or not. With small samples or extreme outliers, you may find the mapping results to be quite volatile. In general, if the results (QTL position or value) depend highly on one or two outliers (5-10% of the samples) then you should probably delete or winsorize the outliers. [Williams RW, Oct 15, 2004, Mar 15, 2008, Mar 26, 2008; Sept 2011] + +[Go back to index](#index) + +
+ +#### CEL and DAT Files (Affymetrix): + +Array data begin as raw image files that are generated using a confocal microscope and video system. Affymetrix refers to these image data files as DAT files. The DAT image needs to be registered to a template that assigns pixel values to expected array coordinates (cells). The result is an assignment of a set of image intensity values (pixel intensities) to each probe. For example, each cell/probe value generated using Affymetrix arrays is associated with approximately 36 pixels (a 6x6 set of pixels, usually with an effective 11 or 12-bit range of intensity). Affymetrix uses a method that simply ranks the values of these pixels and picks as the "representative value" the pixel that is has rank 24 from low to high. The range of variation in intensity amoung these ranked pixels provides a way to estimate the error of the estimate. The Affymetrix CEL files therefore consist of XY coordinates, the consensus value, and an error term. [Williams RW, April 30, 2005] + +#### Cluster Map or QTL Cluster Map: + +Cluster maps are sets of QTL maps for a group of traits. The QTL maps for the individual traits (up to 100) are run side by side to enable easy detection of common and unique QTLs. Traits are clustered along one axis of the map by phenotypic similarity (hierarchical clustering) using the Pearson product-moment correlation r as a measurement of similarity (we plot 1-r as the distance). Traits that are positively correlated will be located near to each other. The genome location is shown along the other, long axis of the cluster map, marker by marker, from Chromosome 1 to Chromosome X. Colors are used to encode the probability of linkage, as well as the additive effect polarity of alleles at each marker. These QTL maps are computed using the fast Marker Regression algorithm. P values for each trait are computed by permuting each trait 1000 times. Cluster maps could be considered trait gels because each lane is loaded with a trait that is run out along the genome. Cluster maps are a unique feature of the GeneNetwork developed by Elissa Chesler and implemented in WebQTL by J Wang and RW Williams, April 2004. [Williams RW, Dec 23, 2004, rev June 15, 2006 RWW]. + +#### Collections and Trait Collections: + +One of the most powerful features of GeneNetwork (GN) is the ability to study large sets of traits that have been measured using a common genetic reference population or panel (GRP). This is one of the key requirements of systems genetics--many traits studied in common. Under the main GN menu Search heading you will see a link to Trait Collections. You can assemble you own collection for any GRP by simply adding items using the Add to Collection button that you will find in many windows. Once you have a collection you will have access to a new set of tools for analysis of your collection, including QTL Cluster Map, Network Graph, Correlation Matrix, and Compare Correlates. [Williams RW, April 7, 2006] + +#### Complex Trait Analysis: + +Complex trait analysis is the study of multiple causes of variation of phenotypes within species. Essentially all traits that vary within a population are modulated by a set of genetic and environmental factors. Finding and characterizing the multiple genetic sources of variation is referred to as "genetic dissection" or "QTL mapping." In comparison, complex trait analysis has a slightly broader focus and includes the analysis of the effects of environmental perturbation, and gene-by-environment interactions on phenotypes; the "norm of reaction." Please also see the glossary term "Systems Genetics." [Williams RW, April 12, 2005] + +#### Composite Interval Mapping: + +Composite interval mapping is a method of mapping chromosomal regions that controls for some fraction of the genetic variability in a quantitative trait. Unlike simple interval mapping, composite interval mapping usually controls for variation produced at one or more background marker loci. These background markers are generally chosen because they are already known to be close to the location of a significant QTL. By factoring out a portion of the genetic variance produced by a major QTL, one can occasionally detect secondary QTLs. WebQTL allows users to control for a single background marker. To select this marker, first run the Marker Regression analysis (and if necessary, check the box labeled display all LRS, select the appropriate locus, and the click on either Composite Interval Mapping or Composite Regression. A more powerful and effective alternative to composite interval mapping is pair-scan analysis. This latter method takes into accounts (models) both the independent effects of two loci and possible two-locus epistatic interactions. [Williams RW, Dec 20, 2004] + +#### Correlations: Pearson and Spearman: + +GeneNetwork provides tools to compute both Pearson product-moment correlations (the standard type of correlation), Spearman rank order correlations. Wikipedia and introductory statistics text will have a discussion of these major types of correlation. The quick advice is to use the more robust Spearman rank order correlation if the number of pairs of observations in a data set is less than about 30 and to use the more powerful but much more sensitive Pearson product-moment correlation when the number of observations is greater than 30 AND after you have dealt with any outliers. GeneNetwork automatically flags outliers for you in the Trait Data and Analysis form. GeneNetwork also allows you to modify values by either deleting or winsorising them. That means that you can use Pearson correlations even with smaller sample sizes after making sure that data are well distributed. Be sure to view the scatterplots associated with correlation values (just click on the value to generate a plot). Look for bivariate outliers. + +#### Cross: + +The term Cross refers to a group of offspring made by mating (crossing) one strain with another strain. There are several types of crosses including intercrosses, backcrosses, advanced intercrosses, and recombinant inbred intercrosses. Genetic crosses are almost always started by mating two different but fully inbred strains to each other. For example, a B6D2F2 cross is made by breeding C57BL/6J females (B6 or B for short) with DBA/2J males (D2 or D) and then intercrossing their F1 progeny to make the second filial generation (F2). By convention the female is always listed first in cross nomenclature; B6D2F2 and D2B6F2 are therefore so-called reciprocal F2 intercrosses (B6D2F1 females to B6D2F1 males or D2B6F1 females to D2B6F1 males). A cross may also consist of a set of recombinant inbred (RI) strains such as the BXD strains, that are actually inbred progeny of a set of B6D2F2s. Crosses can be thought of as a method to randomize the assignment of blocks of chromosomes and genetic variants to different individuals or strains. This random assignment is a key feature in testing for causal relations. The strength with which one can assert that a causal relation exists between a chromosomal location and a phenotypic variant is measured by the LOD score or the LRS score (they are directly convertable, where LOD = LRS/4.61) [Williams RW, Dec 26, 2004; Dec 4, 2005]. + +[Go back to index](#index) + +
+ +#### Dominance Effects: + +The term dominance indicates that the phenotype of intercross progeny closely resemble one of the two parental lines, rather than having an intermediate phenotype. Geneticists commonly refer to an allele as having a dominance effect or dominance deviation on a phenotype. Dominance deviation at a particular marker are calculated as the difference between the average phenotype of all cases that have the Aa genotype at that marker and the expected value half way between the all casese that have the aa genotype and the AA genotype. For example, if the average phenotype value of 50 individuals with the aa genotype is 10 units whereas that of 50 individuals with the AA genotype is 20 units, then we would expect the average of 100 cases with the Aa genotype to be 15 units. We are assuming a linear and perfectly additive model of how the a and A alleles interact. If these 100 Aa cases actually have a mean of 11 units, then this additive model would be inadequate. A non-linear dominance terms is now needed. In this case the low a alleles is almost perfectly dominant (or semi-dominant) and the dominance deviation is -4 units. + +The dominance effects are computed at each location on the maps generated by the WebQTL module for F2 populations (e.g., B6D2F2 and B6BTBRF2). Orange and purple line colors are used to distinguish the polarity of the dominance effects. Purple is the positive dominance effect that matches the polarity of the green additive effect, whereas orange is the negative dominance effect that matches the polarity of the red additive effect. + +Note that dominance deviations cannot be computed from a set of recombinant inbred strains because there are only two classes of genotypes at any marker (aa and AA, more usuually written AA and BB). However, when data for F1 hybrids are available one can estimate the dominance of the trait. This global phenotypic dominance has almost nothing to do with the dominance deviation at a single marker in the genome. In other words, the dominance deviation detected at a single marker may be reversed or neutralized by the action of many other polymorphic genes. [Williams RW, Dec 21, 2004; Sept 3, 2005] + +[Go back to index](#index) + +
+ +#### Epistasis: + +Epistasis means that combined effects of two or more different loci or polymorphic genes are not what one would expect given the addition of their individual effects. There is, in other words, evidence for non-linear interactions among two or more loci. This is similar to the dominance effects mentioned above, but now generalized to two or more distinct loci, rather than to two or more alleles at a single locus. For example, if QTL 1 has an A allele that has an additive effects of +5 and QTL 2 has an A alleles that has an additive effect of +2, then the two locus genotype combination A/A would be expected to boost the mean by +7 units. But if the value of these A/A individuals was actually -7 we would be quite surprised and would refer to this as an epistatic interaction between QTL 1 and QTL 2. WebQTL will search for all possible epistatic interactions between pairs of loci in the genome. This function is called a Pair Scan becasue the software analyzes the LRS score for all possible pairs of loci. Instead of viewing an LRS plot along a single dimension, we now view a two-dimensional plot that shows a field of LRS scores computed for pairs of loci. Pair scan plots are extremely sensitive to outlier data. Be sure to review the primary data carefully using Basic Statistics. Also note that this more sophisiticated method also demands a significantly larger sample size. While 25 to 50 cases may be adequate for a conventional LRS plot (sometimes called a "main scan"), a Pair-Scan is hard to apply safely with fewer than 60 cases. [Williams RW, Dec 21, 2004; Dec 5, 2005] + +#### Effect Size of a QTL: + +QTLs can be ranked by the amount of variance that they explain--their so-called "effect size"--when they are included in a statistical model. The concept of a genetic model may seem odd to some users of GeneNetwork. A model is just an explicit hypothesis of how QTLs and other factors cause variation in a trait. QTL mapping involves comparisons of the explanatory power of different models. Effect sizes can be measured in different units including (1) the percentage of total or genetic variance that is explained by adding the QTL into the model, (2) the mean shift in Z score, or (3) the additive effect size expressed in the original measurement scale. Effects of single QTLs are often dependent on genetic background (i.e., other QTLs and their interactions) and on the numbers and types of cases used in a study. For example, the variance explained is influenced strongly by whether the sample are from a family cohort, a case-control cohort, a group of fully inbred strains such as recombinant inbred lines, an outcross or backcross population. + +Please note that the functional importance of a locus, QTL, or GWAS hit can not be predicted by the size of its effect on the trait in one environment, at one stage of development, and in one population. Estimates of the effect size of QTLs are usually both noisy and upwardly biased (overestimated), and both of these problems are particularly acute when sample sizes are small. + +Estimates of effect size for families of inbred lines, such as the BXD, HXB, CC, and hybrid diversity panels (e.g. the hybrid mouse diversity panel and the hybrid rat diversity panel) are typically (and correctly) much higher than those measured in otherwise similar analysis of intercrosses, heterogeneous stock (HS), or diversity outbred stock. Two factors contribute to the much higher level of explained variance of QTLs when using inbred strain panels. + + +1. *Replication Rate:* The variance that can be explained by a locus is increased by sampling multiple cases that have identical genomes and by using the strain mean for genetic analysis. Increasing replication rates from 1 to 6 can easily double the apparent heritability of a trait and therefore the effect size of a locus. The reason is simple—resampling decrease the standard error of mean, boosting the effective heritability (see Glossary entry on Heritability and focus on figure 1 from the Belknap 1998 paper reproduced below). Compare the genetically explained variance (labeled h2RI in this figure) of a single case (no replication) on the x-axis with the function at a replication rate of 4 on the y-axis. If the explained variance is 0.1 (10% of all variance explained) then the value is boosted to 0.3 (30% of strain mean variance explained) with n = 4. + +2. *Homozygosity:* The second factor has to do with the inherent genetic variance of populations. Recombinant inbred lines are homozygous at nearly all loci. This doubles the genetic variance in a family of recombinant inbred lines compared to a matched number of F2s. This also quadruples the variance compared to a matched number of backcross cases. As a result 40 BXDs sampled just one per genometype will average 2X the genetic variance and 2X the heritability of 40 BDF2 cases. Note that panels made up of isogenic F1 hybrids (so-called diallel crosses, DX) made by crossing recombinant inbred strains (BXD, CC, or HXB) are no longer homozygous at all loci, and while they do expose important new sources of variance associated with dominance, they do not benefit from the 2X gain in genetic variance relative to an F2 intercross. + +Homozygosity + +For the reasons listed above a QTL effect size of 0.4 detected a panel of BXD lines replicated four times each (160 cases total), corresponds approximately to an effect size of 0.18 in BXDs without replication (40 cases total), and to an effect size of 0.09 in an F2 of 40 cases total. [Williams RW, Dec 23, 2004; updated by RWW July 13, 2019] + +eQTL, cis eQTL, trans eQTL + +An expression QTL or eQTL. Differences in the expression of mRNA or proteins are often treated as standard phenotypes, much like body height or lung capacity. The variation in these microscopic traits (microtraits) can be mapped using conventional QTL methods. Damerval and colleagues were the first authors to use this kind of nomenclature and in their classic study of 1994 introduced the term PQLs for protein quantitative trait loci. Schadt and colleagues added the acronym eQTL in their early mRNA study of corn, mouse, and humans. We now are "blessed" with all kinds of prefixes to QTLs that highlight the type of trait that has been measured (m for metabolic, b for behavioral, p for physiological or protein). + +eQTLs of mRNAs and proteins have the unique property of (usually) having a single parent gene and genetic location. An eQTL that maps to the location of the parent gene that produces the mRNA or protein is referred to as a cis eQTL or local eQTL. In contrast, an eQTL that maps far away from its parent gene is referred to as a trans eQTL. You can use special search commands in GeneNetwork to find cis and trans eQTLs. [Williams RW, Nov 23, 2009, Dec 2009] + +[Go back to index](#index) + +
+ +## F + +#### Frequency of Peak LRS: + +The height of the yellow bars in some of the Map View windows provides a measure of the confidence with which a trait maps to a particular chromosomal region. WebQTL runs 2000 bootstrap samples of the original data. (A bootstrap sample is a "sample with replacement" of the same size as the original data set in which some samples will by chance be represented one of more times and others will not be represented at all.) For each of these 2000 bootstraps, WebQTL remaps each and keeps track of the location of the single locus with the highest LRS score. These accumulated locations are used to produce the yellow histogram of "best locations." A frequency of 10% means that 200 of 2000 bootstraps had a peak score at this location. It the mapping data are robust (for example, insensitive to the exclusion of an particular case), then the bootstrap bars should be confined to a short chromosomal interval. Bootstrap results will vary slightly between runs due to the random generation of the bootstrap samples. [Williams RW, Oct 15, 2004] + +#### False Discovery Rate (FDR): + +A false discovery is an apparently significant finding--usually determined using a particular P value alpha criterion--that given is known to be insignificant or false given other information. When performing a single statistical test we often accept a false discovery rate of 1 in 20 (p = .05). False discovery rates can climb to high levels in large genomic and genetic studies in which hundreds to millions of tests are run and summarized using standard "single test" p values. There are various statistical methods to estimate and control false discovery rate and to compute genome-wide p values that correct for large numbers of implicit or explicit statistical test. The Permutation test in GeneNetwork is one method that is used to prevent and excessive number of false QTL discoveries. Methods used to correct the FDR are approximations and may depend on a set of assumptions about data and sample structure. [Williams RW, April 5, 2008] + +[Go back to index](#index) + +
+ +## G + +#### Genes, GenBankID, UniGeneID, GeneID, LocusID: + +GeneNetwork provides summary information on most of the genes and their transcripts. Genes and their alternative splice variants are often are poorly annotated and may not have proper names or symbols. However, almost all entries have a valid GenBank accession identifier. This is a unique code associated with a single sequence deposited in GenBank (Entrez Nucleotide). A single gene may have hundreds of GenBank entries. GenBank entries that share a genomic location and possibly a single gene are generally combined into a single UniGene entry. For mouse, these always begin with "Mm" (Mus musculus) and are followed by a period and then a number. More than half of all mouse UniGene identifiers are associated with a reputable gene, and these genes will have gene identifiers (GeneID). GeneIDs are identical to LocusLink identifiers (LocusID). Even a 10 megabase locus such as human Myopia 4 (MYP4) that is not yet associated with a specific gene is assigned a GeneID--a minor misnomer and one reason to prefer the term LocusID. + +See the related FAQ on "How many genes and transcripts are in your databases and what fraction of the genome is being surveyed?" [Williams RW, Dec 23, 2004, updated Jan 2, 2005] + +#### Genetic Reference Population (GRP): + +A genetic reference population consists of a set of genetically well characterized lines that are often used over a long period of time to study a multitude of different phenotypes. Once a GRP has been genotyped, subsequent studies can focus on the analysis of interesting and important phenotypes and their joint and independent relations. Most of the mouse GRPs, such as the BXDs used in the GeneNetwork, have been typed using a common set of over 14,000 makers (SNPs and microsatellites). Many of these same GRPs have been phenotyped extensively for more than 25 years, resulting in rich sets of phenotypes. A GRP is an ideal long-term resource for systems genetics because of the relative ease with which vast amounts of diverse data can be accumulated, analyzed, and combined. + +The power of GRPs and their compelling scientific advantages derive from the ability to study multiple phenotypes and substantial numbers of genetically defined individuals under one or more environmental conditions. When accurate phenotypes from 20 or more lines in a GRP have been acquired it becomes practical to explore and test the genetic correlations between that trait and any previously measured trait in the same GRP. This fact underlies the use of the term reference in GRP. Since each genetic individual is represented by an entire isogenic line--usually an inbred strain or an isogenic F1 hybrid--it is possible to obtain accurate mean phenotypes associated with each line simply by typing several individuals. GRPs are also ideal for developmental and aging studies because the same genetic individual can be phenotyped at multiple stages. + +A GRP can also be used a conventional mapping panel. But unlike most other mapping panel, a GRP can be easily adapted to jointly map sets of functionally related traits (multitrait mapping); a more powerful method to extract causal relations from networks of genetic correlations. + +The largest GRPs now consist of more than 400 recombinant inbred lines of Arabidopsis and maize. The BayxSha Arabidopsis set in the GeneNetwork consists of 420 lines. Pioneer Hi-Bred International is rumored to have as many as 4000 maize RI lines. The largest mammalian GRPs are the LXS and BXD RI sets in the GeneNetwork. The Collaborative Cross is the largest mammalian GRP, and over 600 of these strains are now being bred by members of the Complex Trait Consortium. + +There are several subtypes of GRPs. In addition to recombinant inbred strains there are + + +- Recombinant congenic (RCC) strains such as the AcB set Consomic or chromosome substitution strains (CSS) of mice (Matin et al., 1999) and rats (Roman et al., 2002) + +- Recombinant intercross (RIX) F1 sets made by mating different RI strains to each other to generate large set of R! first generation (F1) progeny (RIX). This is a standard (diallel cross) of RI inbred strains. Genetic analysis of a set of RIX progeny has some advantages over a corresponding analysis of RI strains. The first of these is that while each set of F1 progeny is fully isogenic (AXB1 x AXB2 gives a set of isogenic F1s), these F1s are not inbred but are heterozygous at many loci across the genome. RIX therefore retain the advance of being genetically defined and replicable, but without the disadvantage of being fully inbred. RIX have a genetic architecture more like natural populations. The second correlated advantage is that it is possible to study patterns of dominance of allelic variants using an RIX cross. Almost all loci or genes that differs between the original stock strains (A and B) will be heterozygous among a sufficiently larges set of RIX. A set of RIX progeny can therefore be mapped using the same methods used to map an F2 intercross. Mapping of QTLs may have somewhat more power and precision than when RI strains are used alone. A third advantage is that RIX sets make it possible to expand often limited RI resources to very large sizes to confirm and extend models of genetic or GXE effects. For example a set of 30 AXB strains can be used to generate a full matrix of 30 x 29 unique RIX progeny. The main current disadvantage of RIX panels is the comparative lack of extant phenotype data. + +- Recombinant F1 line sets can also be made by backcrossing an entire RI sets to a single inbred line that carries an interesting mutation or transgene (RI backcross or RIB). GeneNetwork includes one RI backcross sets generated by Kent Hunter. In this RIB each of 18 AKXD RI strains were crossed to an FVB/N line that carries a tumor susceptibility allele (polyoma middle T). + + All of these sets of lines are GRPs since each line is genetically defined and because the set as a whole can in principle be easily regenerated and phenotyped. Finally, each of these resources can be used to track down genetic loci that are causes of variation in phenotype using variants of standard linkage analysis. + + A Diversity Panel such as that used by the Mouse Phenome Project is not a standard GRPs, although its also shares the ability to accumulate and study networks of phenotypes. The main difference is that a Diversity Panel cannot be used for conventional linkage analysis. A sufficiently large Diversity Panel could in principle be used for the equivalent of an assocation study. However, these are definitely NOT in silico studies, because hundreds of individuals need to be phenotyped for every trait. Surveys of many diverse isogenic lines (inbred or F1 hybrids) is statistically the equivalent of a human association study (the main difference is the ability to replicate measurements and study sets of traits) and therefore, like human association studies, does require very high sample size to map polygenic traits. Like human association studies there is also a high risk of false positive results due to population stratification and non-syntenic marker association. + + A good use of a Diversity Panel is as a fine-mapping resource with which to dissect chromosomal intervals already mapped using a conventional cross or GRP. GeneNetwork now includes Mouse Diversity Panel (MDP) data for several data sets. We now typically include all 16 sequenced strains of mice, and add PWK/PhJ, NZO/HiLtJ (two of the eight members of the Collaborative Cross), and several F1 hybrids. The MDP data is often appended at the bottom of the GRP data set with which is was acquired (e.g., BXD hippocampal and BXD eye data sets). [Williams RW, June 19, 2005; Dec 4, 2005] + + Genotype: The state of a gene or DNA sequence, usually used to describe a contrast between two or more states, such as that between the normal state (wildtype) and a mutant state (mutation) or between the alleles inherited from two parents. All species that are included in GeneNetwork are diploid (derived from two parents) and have two copies of most genes (genes located on the X and Y chromosomes are exceptions). As a result the genotype of a particular diploid individual is actually a pair of genotypes, one from each parents. For example, the offspring of a mating between strain A and strain B will have one copy of the A genotype and one copy of the B genotype and therefore have an A/B genotype. In contrast, offspring of a mating between a female strain A and a male strain A will inherit only A genotypes and have an A/A genotype. + +Genotypes can be measured or inferred in many different ways, even by visual inspection of animals (e.g. as Gregor Mendel did long before DNA was discovered). But now the typical method is to directly test DNA that has a well define chromosomal location that has been obtained from one or usually many cases using molecular tests that often rely on polymerase chain reaction steps and sequence analysis. Each case is genotyped at many chromosomal locations (loci, markers, or genes). The entire collection of genotypes (as many a 1 million for a single case) is also sometimes referred to as the cases genotype, but the word "genometype" might be more appropriate to highlight the fact that we are now dealing with a set of genotypes spanning the entire genome (all chromosomes) of the case. + +For gene mapping purposes, genotypes are often translated from letter codes (A/A, A/B, and B/B) to simple numerical codes that are more suitable for computation. A/A might be represented by the value -1, A/B by the value 0, and B/B by the value +1. This recoding makes it easy to determine if there is a statistically significant correlation between genotypes across of a set of cases (for example, an F2 population or a Genetic Reference Panel) and a variable phenotype measured in the same population. A sufficiently high correlation between genotypes and phenotypes is referred to as a quantitative trait locus (QTL). If the correlation is almost perfect (r > 0.9) then correlation is usually referred to as a Mendelian locus. Despite the fact that we use the term "correlation" in the preceding sentences, the genotype is actually the cause of the phenotype. More precisely, variation in the genotypes of individuals in the sample population cause the variation in the phenotype. The statistical confidence of this assertion of causality is often estimated using LOD and LRS scores and permutation methods. If the LOD score is above 10, then we can be extremely confident that we have located a genetic cause of variation in the phenotype. While the location is defined usually with a precision ranging from 10 million to 100 thousand basepairs (the locus), the individual sequence variant that is responsible may be quite difficult to extract. Think of this in terms of police work: we may know the neighborhood where the suspect lives, we may have clues as to identity and habits, but we still may have a large list of suspects. + +Text here [Williams RW, July 15, 2010] + +[Go back to index](#index) + +
+ +## H + +#### Heritability, h2: + +Heritability is a rough measure of the ability to use genetic information to predict the level of variation in phenotypes among progeny. Values range from 0 to 1 (or 0 to 100%). A value of 1 or 100% means that a trait is entirely predictable based on paternal/materinal and genetic data (in other words, a Mendelian trait), whereas a value of 0 means that a trait is not at all predictable from information on gene variants. Estimates of heritability are highly dependent on the environment, stage, and age. + +Important traits that affect fitness often have low heritabilities because stabilizing selection reduces the frequency of DNA variants that produce suboptimal phenotypes. Conversely, less critical traits for which substantial phenotypic variation is well tolerated, may have high heritability. The environment of laboratory rodents is unnatural, and this allows the accumulation of somewhat deleterious mutations (for example, mutations that lead to albinism). This leads to an upward trend in heritability of unselected traits in laboratory populations--a desirable feature from the point of view of the biomedical analysis of the genetic basis of trait variance. Heritability is a useful parameter to measure at an early stage of a genetic analysis, because it provides a rough gauge of the likelihood of successfully understanding the allelic sources of variation. Highly heritable traits are more amenable to mapping studies. There are numerous ways to estimate heritability, a few of which are described below. [Williams RW, Dec 23, 2004] + +#### h2 Estimated by Intraclass Correlation: + +Heritability can be estimated using the intraclass correlation coefficient. This is essentially a one-way repeated measures analysis of variance (ANOVA) of the reliability of trait data. Difference among strains are considered due to a random effect, whereas variation among samples within a single strain are considered due to measurement error. One can use the method implemented by SAS (PROC VARCOMP) that exploits a restricted maximum likelihood (REML) approach to estimate the intraclass correlation coefficient instead of an ordinary least squares method. The general equation for the intraclass correlation is: + +r = (Between-strain MS - Within-strain MS)/(Between-strain MS + (n-1)x Within-strain MS) + +where n is the average number of cases per strain. The intraclass correlation approaches 1 when there is minimal variation within strains, and strain means differ greatly. In contrast, if difference between strains are less than what would be predicted from the differences within strain, then the intraclass correlation will produce negative estimates of heritability. Negative heritability is usually a clue that the design of the experiment has injected excessive within-strain variance. It is easy for this to happen inadvertently by failing to correct for a batch effect. For example, if one collects the first batch of data for strains 1 through 20 during a full moon, and a second batch of data for these same strains during a rare blue moon, then the apparent variation within strain may greatly exceed the among strain variance. A technical batch effect has been confounded with the within-strain variation and has swamped any among-strain variance. What to do? Fix the batch effect, sex effect, age effect, etc., first! [Williams RW, Chesler EJ, Dec 23, 2004] + +#### h2 Estimated using Hegmann and Possidente's Method (Adjusted Heritability in the Basic Statisics): + +A simple estimate of heritability for inbred lines involves comparing the variance between strain means (Va) to the total variance (Vt) of the phenotype, where Va is the a rough estimate of the additive genetic variance and Vt is the equal to Va and the average environmental variance, Ve. For example, if we study 10 cases of each of 20 strains, we have a total variance of the phenotype across 200 samples, and a strain mean variance across 20 strain averages. We can use this simple equation to estimate the heritability: + +h2 = Va / Vt + +This estimate of heritability will be an overestimate, and the severity of this bias will be a function of the within-strain standard error of the mean. Even a random data set of 10 each of 20 strains that should have an h2 of 0, will often give h2 values of 0.10 to 0.20. (Try this in a spreadsheet program using random numbers.) + +However, this estimate of h2 cannot be compared directly to those calculated using standard intercrosses and backcrosses. The reason is that all cases above are fully inbred and no genotypes are heterozygous. As a result the estimate of Va will be inflated two-fold. Hegmann and Possidente (1981 suggested a simple solution; adjust the equation as follows: + +h2 = 0.5Va / (0.5Va+Ve) + +The factor 0.5 is applied to Va to adjust for the overestimation of additive genetic variance among inbred strains. This estimate of heritability also does not make allowances for the within-strain error term. The 0.5 adjustment factor is not recommended any more because h2 is severely underestimated. This adjustment is really only needed if the goal is to compare h2 between intercrosses and those generated using panels of inbred strains. + +#### h2RIx̅ + +Finally, heritability calculations using strain means, such as those listed above, do not provide estimates of the effective heritability achieved by resampling a given line, strain, or genometype many times. Belknap (1998) provides corrected estimates of the effective heritability. Figure 1 from his paper (reproduced below) illustrates how resampling helps a great deal. Simply resampling each strain 8 times can boost the effective heritability from 0.2 to 0.8. The graph also illustrates why it often does not make sense to resample much beyond 4 to 8, depending on heritability. Belknap used the term h2RIx̅ in this figure and paper, since he was focused on data generated using recombinant inbred (RI) strains, but the logic applies equally well to any panel of genomes for which replication of individual genometypes is practical. This h2RIx̅ can be calculated simply by: +h2RIx̅ = Va / (Va+(Ve/n)) where Va is the genetic variability (variability between strains), Ve is the environmental variability (variability within strains), and n is the number of within strain replicates. Of course, with many studies the number of within strain replicates will vary between strains, and this needs to be dealt with. A reasonable approach is to use the harmonic mean of n across all strains. + +Homozygosity + +An analysis of statistical power is useful to estimate numbers of replicates and strains needed to detect and resolve major sources of trait variance and covariance. A versatile method has been developed by Sen and colleagues (Sen et al., 2007) and implemented in the R program. qtlDesign. David Ashbrook implemented a version of this within Shiny that can help you estimate power for different heritability values QTL effect sizes, cohort sizes, and replication rates: + +### Power Calculator (D. Ashbrook) + +We can see that in all situations power is increased more by increasing the number of lines than by increasing the number of biological replicates. Dependent upon the heritability of the trait, there is little gain in power when going above 4-6 biological replicates. [DGA, Feb 1, 2019] [Chesler EJ, Dec 20, 2004; RWW updated March 7, 2018; Ashbrook DG, updated Feb 1, 2019] + +#### Hitchhiking Effect: + +Conventional knockout lines (KOs) of mice are often mixtures of the genomes of two strains of mice. One important consequence of this fact is that a conventional comparison of wildtype and KO litter mates does not only test of the effects of the KO gene itself but also tests the effects of thousands of "hitchhiking" sequence polymorphisms in genes that flank the KO gene. This experimental confound can be difficult to resolve (but see below). This problem was first highlighted by Robert Gerlai (1996). + +**Genetics of KO Lines**. The embryonic stem cells used to make KOs are usually derived from a 129 strain of mouse (e.g., 129/OlaHsd). Mutated stem cells are then added to a C57BL/6J blastocyst to generate B6x129 chimeric mice. Germline transmission of the KO allele is tested and carriers are then used to establish heterozygous +/- B6.129 KO stock. This stock is often crossed back to wildtype C57BL/6J strains for several generations. At each generation the transmission of the KO is checked by genotyping the gene or closely flanking markers in each litter of mice. Carriers are again selected for breeding. The end result of this process is a KO congenic line in which the genetic background is primarily C57BL/6J except for the region around the KO gene. + +It is often thought that 10 generations of backcrossing will result in a pure genetic background (99.8% C57BL/6J). Unfortunately, this is not true for the region around the KO, and even after many generations of backcrossing of KO stock to C57BL/6J, a large region around the KO is still derived from the 129 substrain (see the residual white "line" at N10 in the figure below. + +Congenic + +After 20 generations of backcrossing nearly +/-5 cM on either side of the KO will still usually be derived from 129 (see Figure 3.6) This amounts to an average of +/- 10 megabases of DNA around the KO. The wildtype littermates do NOT have this flanking DNA from 129 and they will be like a true C57BL/6J. The +/- 10 megabases to either side of the KO is known as the "hitchhiking" chromosomal interval. Any polymorphism between 129 and B6 in this interval has the potential to have significant downstream effects on gene expression, protein expression, and higher order traits such as anxiety, activity, and maternal behavior. Much of the conventional KO literature is highly suspect due to this hitchhiker effect (see Gerlai R, Trends in Neurosci 1996 19:177). + +As one example, consider the thyroid alpha receptor hormone gene Thra and its KO. Thra maps to Chr 11 at about 99 Mb. A conventional KO made as described above will have a hitchhiking 129 chromosomal interval extending from about 89 Mb to 109 Mb even after 20 generations of backcrossing to B6. Since the mouse genome is about 2.6 billion base pairs and contains about 26,000 genes, this 20 Mb region will typically contain about 200 genes. The particular region of Chr 11 around Thra has an unusually high density of genes (2-3X) and includes many highly expressed and polymorphic genes, including Nog, Car10, Cdc34, Col1a1, Dlx4, Myst2, Ngfr, Igf2bp1, Gip, the entire Hoxb complex, Sp6, Socs7, Lasp1, Cacnb1, Pparbp, Pnmt, Erbb2, Grb7, Nr1d1, Casc3, Igfbp4, and the entire Krt1 complex. Of these gene roughly half will be polymorphic between B6 and 129. It is like having a busload of noisy and possibly dangerous hitchhikers. Putative KO effects may be generated by a complex subset of these 100 polymorphic genes. + +What is the solution? + +1. Do not use litter mates as controls without great care. They are not really the correct genetic control. The correct genetic control is a congenic strain of the same general type without the KO or with a different KO in a nearby gene. These are often available as KOs in neighboring genes that are not of interest. For example, the gene Casc3 is located next to Thra. If a KO in Casc3 is available, then compare the two KOs and see if phenotypes of the two KOs differ ways predicted given the known molecular functions of the gene. + +2. Use a KO in which the KO has been backcrossed to a 129 strain--ideally the same strain from which ES cells were obtained. This eliminates the hitchhiker effect entirely and the KO, HET, and WT littermates really can be compared. + +3. Use a conditional KO. + +4. Compare the phenotype of the two parental strains--129 and C57BL/6J and see if they differ in ways that might be confounded with the effects of the KO. + +Homozygosity + +Legend:from Silver, L. (1995) Oxford University Press + +[Go back to index](#index) + +
+ +## I + +#### Interquartile Range: + +The interquartile range is the difference between the 75% and 25% percentiles of the distribution. We divide the sample into a high and low half and then compute the median for each of these halves. In other words we effectively split our sample into four ordered sets of values known as quartiles. The absolute value of the difference between the median of the lower half and the median of the upper half is also called the interquartile range. This estimate of range is insenstive to outliers. If you are curious you might double the IQR to get an interquartile-range-based estimate of the full range. Of course, keep in mind that range is dependent on the sample size. For theis reason the coeffficient of variation (the standard deviation divided by the mean) is a better overall indicator of dispersion of values around the mean that is less sensitive to sample size. [Williams RW, Oct 20, 2004; Jan 23, 2005] + +#### Interval Mapping: + +Interval mapping is a process in which the statistical significance of a hypothetical QTL is evaluated at regular points across a chromosome, even in the absence of explicit genotype data at those points. In the case of WebQTL, significance is calculated using an efficient and very rapid regression method, the Haley-Knott regression equations (Haley CS, Knott SA. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers; Heredity 69:315–324), in which trait values are compared to the known genotype at a marker or to the probability of a specific genotype at a test location between two flanking markers. (The three genotypes are coded as -1, 0, and +1 at known markers, but often have fractional values in the intervals between markers.) The inferred probability of the genotypes in regions that have not been genotyped can be estimated from genotypes of the closest flanking markers. GeneNetwork/WebQTL compute linkage at intervals of 1 cM or less. As a consequence of this approach to computing linkage statistics, interval maps often have a characteristic shape in which the markers appear as sharply defined inflection points, and the intervals between nodes are smooth curves. [Chesler EJ, Dec 20, 2004; RWW April 2005; RWW Man 2014] + +#### Interval Mapping Options: + +- Permutation Test: Select this option to determine the approximate LRS value that matches a genome-wide p-value of .05. + +- Bootstrap Test: Select this option to evaluate the consistency with which peak LRS scores cluster around a putative QTL. Deselect this option if it obscures the SNP track or the additive effect track. + +- Additive Effect: The additive effect (shown by the red lines in these plots) provide an estimate of the change in the average phenotype that is brought about by substituting a single allele of one type with that of another type. + +- SNP Track: The SNP Seismograph Track provides information on the regional density of segregating variants in the cross that may generate trait variants. It is plotted along the X axis. If a locus spans a region with both high and low SNP density, then the causal variant has a higher prior probability to be located in the region with high density than in the region with low density. + +- Gene Track: This track overlays the positions of known genes on the physical Interval Map Viewer. If you hover the cursor over genes on this track, minimal information (symbol, position, and exon number) will appear. + +- Display from X Mb to Y Mb: Enter values in megabases to regenerate a smaller or large map view. + +- Graph width (in pixels): Adjust this value to obtain larger or smaller map views (x axis only). + +[Go back to index](#index) + +
+ +## J + +[Go back to index](#index) + +
+ +## K + +[Go back to index](#index) + +
+ +## L + +#### Literature Correlation: + +The literature correlation is a unique feature in GeneNetwork that quantifies the similarity of words used to describe genes and their functions. Sets of words associated with genes were extracted from MEDLINE/PubMed abstracts (Jan 2017 by Ramin Homayouni, Diem-Trang Pham, and Sujoy Roy). For example, about 2500 PubMed abstracts contain reference to the gene "Sonic hedgehog" (Shh) in mouse, human, or rat. The words in all of these abstracts were extracted and categorize by their information content. A word such as "the" is not interesting, but words such as "dopamine" or "development" are useful in quantifying similarity. Sets of informative words are then compared—one gene's word set is compared the word set for all other genes. Similarity values are computed for a matrix of about 20,000 genes using latent semantic indexing (see Xu et al., 2011). Similarity values are also known as literature correlations. These values are always positive and range from 0 to 1. Values between 0.5 and 1.0 indicate moderate-to-high levels of overlap of vocabularies. + +The literature correlation can be used to compare the "semantic" signal-to-noise of different measurements of gene, mRNA, and protein expression. Consider this common situation:There are three probe sets that measure Kit gene expression (1459588\_at, 1415900\_a\_at, and 1452514\_a\_at) in the Mouse BXD Lung mRNA data set (HZI Lung M430v2 (Apr08) RMA). Which one of these three gives the best measurement of Kit expression? It is impractical to perform quantitative rtPCR studies to answer this question, but there is a solid statistical answer that relies on Literature Correlation. Do the following: For each of the three probe sets, generate the top 1000 literature correlates. This will generate three apparently identical lists of genes that are known from the PubMed literature to be associated with the Kit oncogene. But the three lists are NOT actually identical when we look at the Sample Correlation column. To answer the question "which of the three probe sets is best", review the actual performance of the probe sets against this set of 1000 "friends of Kit". Do this by sorting all three lists by their Sample Correlation column (high to low). The clear winner is probe set 1415900_a_at. The 100th row in this probe set's list has a Sample Correlation of 0.620 (absolute value). In comparison, the 100th row for probe set 1452514_a_at has a Sample Correlation of 0.289. The probe set that targets the intron comes in last at 0.275. In conclusion, the probe set that targets the proximal half of the 3' UTR (1415900_a_at) has the highest "agreement" between Literature Correlation and Sample Correlation, and is our preferred measurement of Kit expression in the lung in this data set. (Updated by RWW and Ramin Homayouni, April 2017.) + +#### LOD: + +The logarithm of the odds (LOD) provides a measure of the association between variation in a phenotype and genetic differences (alleles) at a particular chromosomal locus (see Nyholt 2000 for a lovely review of LOD scores). + +A LOD score is defined as the logarithm of the ratio of two likelihoods: (1) in the numerator the likelihood for the alternative hypothesis, namely that there is linkage at the chromosomal marker, and (2) the likelihood of the null hypothesis that there is no linkage. Likelihoods are probabilities, but they are not Pr(hypothesis | data) but rather Pr(data | two alternative hypotheses). That's why they are called likelihoods rather than probabilities. (The "|" symbol above translates to "given the"). Since LOD and LRS scores are associated with two particular hypotheses or models, they are also associated with the degrees of freedom of those two alternative models. When the model only has one degree of freedom this conversion between LOD to p value will work: +
+    lodToPval <-
+    function(x)
+    {
+    pchisq(x*(2*log(10)),df=1,lower.tail=FALSE)/2
+    }
+    # (from https://www.biostars.org/p/88495/ )    
+
+ +In the two likelihoods, one has maximized over the various nuisance parameters (the mean phenotypes for each genotype group, or overall for the null hypothesis, and the residual variance). Or one can say, one has plugged in the maximum likelihood estimates for these nuisance parameters. + +With complete data at a marker, the log likelihood for the normal model reduces to the (-n/2) times the log of the residual sum of squares. + +LOD values can be converted to LRS scores (likelihood ratio statistics) by multiplying by 4.61. The LOD is also roughly equivalent to the -log(P), where P is the probability of linkage (P = 0.001 => 3). The LOD itself is not a precise measurement of the probability of linkage, but in general for F2 crosses and RI strains, values above 3.3 will usually be worth attention for simple interval maps. [Williams RW, June 15, 2005, updated with text from Karl Broman, Oct 28, 2010, updated Apr 21, 2020 with Nyholt reference]. + +#### LRS: + +In the setting of mapping traits, the likelihood ratio statistic is used as a measurement of the association or linkage between differences in traits and differences in particular genotype markers. LRS or LOD values are usually plotted on the y-axis, whereas chromosomal location of the marker are usually plotted on the x-axis. In the case of a whole genome scan--a sequential analysis of many markers and locations across the entire genome--LRS values above 10 to 15 will usually be worth attention for when mapping with standard experimental crosses (e.g., F2 intercrosses or recombinant inbred strains). The term "likelihood ratio" is used to describe the relative probability (likelihood) of two different explanations of the variation in a trait. The first explanation (or model or hypothesis H1) is that the differences in the trait ARE associated with that particular DNA sequence difference or marker. Very small probability values indicate that H1 is probably true. The second "null" hypothesis (Hnull or H0) is that differences in the trait are NOT associated with that particular DNA sequence. We can use the ratio of these two probabilities and models (H1 divided by H0) as our score. The math is a little bit more complicated and the LRS score is actually equal to -2 times the ratio of the natural logarithms of the two probabilities. For example, if the probability of H0 is 0.05 (only a one-in-twenty probability that the marker is associated with the trait by chance), whereas and the probability of H1 is 1 (the marker is certainly not linked to the trait), then the LRS value is 5.991. In Excel the equation giving the LRS result of 5.991 would look like this "=-2*(LN(0.05)-LN(1)). [Williams RW, Dec 13, 2004, updated Nov 18, 2009, updated Dec 19, 2012] + +[Go back to index](#index) + +
+ +## M + +Marker Regression: + +The relationship between differences in a trait and differences in alleles at a marker (or gene variants) can be computed using a regression analysis (genotype vs phenotype) or as a simple Pearson product moment correlation. Here is a simple example that you can try in Excel to understand marker-phenotype regression or marker-phenotype correlation: enter a row of phenotype and genotype data for 20 strains in an Excel spreadsheet labeled "Brain weight." The strains are C57BL/6J, DBA/2J, and 20 BXD strains of mice (1, 2, 5, 6, 8, 9, 12, 13, 14, 15, 16, 18, 21, 22, 23, 24, 25, 27, 28, and 29. The brains of these strains weigh an average (in milligrams) of 465, 339, 450, 390, 477, 361, 421, 419, 412, 403, 429, 429, 436, 427, 409, 431, 432, 380, 394, 381, 389, and 375. (These values are taken from BXD Trait 10032; data by John Belknap and colleagues, 1992. Notice that data are missing for several strains including the extinct lines BXD3, 4, and 7. Data for BXD11 and BXD19 (not extinct) are also missing. In the second row enter the genotypes at a single SNP marker on Chr 4 called "rs13478021" for the subset of strains for which we have phenotype data. The genotypes at rs1347801 are as follows for 20 BXDs listed above: D B D B D B D D D D D B D B D B D B D B. This string of alleles in the parents and 20 BXDs is called a strains distribution pattern (SDP). Let's convert these SDP letters into more useful numbers, so that we can "compute" with genotypes. Each B allele gets converted into a -1 and each D allele gets converted into a +1. In the spreadsheet, the data set of phenotypes and genotypes should look like this. + +
+    Strain BXD1 BXD2 BXD5 6 8 9 12 13 14 15 16 18 21 22 23 24 25 27 28 29
+    Brain_weight 450 390 477 361 421 419 412 403 429 429 436 427 409 431 432 380 394 381 389 375
+    Marker_rs1347801 D B D B D B D D D D D B D B D B D B D B
+    Marker_code 1 -1 1 -1 1 -1 1 1 1 1 1 -1 1 -1 1 -1 1 -1 1 -1
+
+ +To compute the marker regression (or correlation) we just compare values in Rows 2 and 4. A Pearson product moment correlation gives a value of r = 0.494. A regression analysis indicates that on average those strains with a D allele have a heavier brain with roughly a 14 mg increase for each 1 unit change in genotype; that is a total of about 28 mg if all B-type strains are compared to all D-type strains at this particular marker. This difference is associated with a p value of 0.0268 (two-tailed test) and an LRS of about 9.8 (LOD = 9.8/4.6 or about 2.1). Note that the number of strains is modest and the results are therefore not robust. If you were to add the two parent strains (C57BL/6J and DBA/2J) back into this analysis, which is perfectly fair, then the significance of this maker is lost (r = 0.206 and p = 0.3569). Bootstrap and permutation analyses can help you decide whether results are robust or not and whether a nominally significant p value for a single marker is actually significant when you test many hundreds of markers across the whole genome (a so-called genome-wide test with a genome-wide p value that is estimated by permutation testing). [RWW, Feb 20, 2007, Dec 14, 2012] + +[Go back to index](#index) + +
+ +## N + +#### Normal Probability Plot: + +A normal probability plot is a powerful tool to evaluate the extent to which a distribution of values conforms to (or deviates from) a normal Gaussian distribution. The Basic Statistics tools in GeneNetwork provides these plots for any trait. If a distribution of numbers is normal then the actual values and the predicted values based on a z score (units of deviation from the mean measured in standard deviation units) will form a nearly straight line. These plots can also be used to efficiently flag outlier samples in either tail of the distribution. + +In genetic studies, the probability plot can be used to detect the effects of major effect loci. A classical Mendelian locus will typically be associated with either a bimodal or trimodal distribution. In the plot below based on 99 samples, the points definitely do not fall on a single line. Three samples (green squares) have unusually high values; the majority of samples fall on a straight line between z = -0.8 to z = 2; and 16 values have much lower trait values than would be predicted based on a single normal distribution (a low mode group). The abrupt discontinuity in the distribution at -0.8 z is due to the effect of a single major Mendelian effect. + +Deviations from normality of the sort in the figure below should be considered good news from the point of view of likely success of tracking down the locations of QTLs. However, small numbers of outliers may require special statistical handling, such as their exclusion or winsorising (see more below on "Winsorizing"). [RWW June 2011] + +Homozygosity + +[Go back to index](#index) + +
+ +## O + +#### Outliers: (also see Wikipedia) + +Statistical methods often assume that the distribution of trait values is close to a Gaussian normal bell-shaped curve and that there are no outlier values that are extremely high or low compared to the average. Some traits can be clearly split into two or more groups (affected cases and unaffected cases) and this is not a problem as long as the number of cases in each group is close to the number that you expected by chance and that your sample size is reasonable high (40 or more for recombinant inbred strains). Mapping functions and most statistical procedure in GeneNetwork should work reasonable well (the pair scan function for epistatic interactions is one possible exception). + +However, correlations and QTL mapping methods can be highly sensitive to outlier values. Make sure you review your data for outliers before mapping. GeneNetwork flags all outliers for you in the Trait Data and Analysis window and gives you the option of zapping these extreme values. Options include (1) do nothing, (2) delete the outliers and see what happens to your maps, (3) Winsorize the values of the outliers. You might try all three options and determine if your main results are stable or not. With small samples or extreme outliers, you may find the correlation and mapping results to be volatile. In general, if results (correlations, QTL positions or QTL LRS score) depend highly on one or two outliers (5-10% of the samples) then you should probably delete or winsorize the outliers. + +In order to calculate outliers, we first determine the Q1(25%) and Q3(75%) values and then multiply by a constant (in our case 1.5; a higher constant is less sensitive to outliers). This value is then subtracted from the Q1 value and added to the Q3 value in order to determine the lower and upper bounds. Values that fall above the upper bound or below the lower bound are considered outliers. + +The method is summarized [here](http://www.wikihow.com/Calculate-Outliers). [Sloan ZA, Oct 2013] + +[Go back to index](#index) + +
+ +## P + +#### Pair-Scan, 2D Genome Scan, or Two-QTL Model: + +The pair scan function evaluates pairs of intervals (loci) across the genome to determine how much of the variability in the trait can be explained jointly by two putative QTLs. The pair scan function in GeneNetwork is used to detect effects of pairs of QTLs that have epistatic interactions, although this function also evaluates summed additive effects of two loci. Trait variance is evaluated using a general linear model that has this structure (called a "model"): + +Variance V(trait) = QTL1 + QTL2 + QTL1xQTL2 + error (where the = sign should be read "a function of" + +This model is also known as the Full Model (LRS Full in the output table), where QTL1 and QTL2 are the independent additive effects associated with two unlinked loci (the so-called main effects) and QTL1xQTL2 is the interaction term (LRS Interact in the output table). An LRS score is computed for this full model. This is computation identical to computing an ANOVA that allows for an interaction term between two predictors. The additive model that neglects the QTL1XQTL2 term is also computed. + +The output table in GeneNetwork list the the two intervals at the top of the table (Interval 1 to the left and Interval 2 to the far right). The LRS values for different components of the model are shown in the middle of the table (LRS Full, LRS Additive, LRS Interact, LRS 1, and LRS 2). Note that LRS 1 and LRS 2 will usually NOT sum to LRS Additive. + +CAUTIONS and LIMITATIONS: Pair-scan is only implemented for recombinant inbred strains. We do not recommend the use of this function with sample sizes of less than 60 recombinant inbred strains. Pair-scan procedures need careful diagnostics and an be very sensitive to outliers and to the balance among the four possible two-locus genotype classes among a set of RI strains. Pair-scan is not yet implemented for F2 progeny. + +GeneNetwork implements a rapid but non-exhaustive DIRECT algorithm (Lundberg et al., 2004) that efficiently searches for epistatic interactions. This method is so fast that it is possible to compute 500 permutations to evaluate non-parametric significance of the joint LRS value within a minute. This makes DIRECT ideal for an interactive web service. Karl Broman's R/qtl implements an exhaustive search using the "scantwo" function. [RWW, May 2011] + +#### Partial Correlation: + +Partial correlation is the correlation between two variables that remains after controlling for one or more other variables. Idea and techniques used to compute partial correlations are important in testing causal models (Cause and Correlation in Biology, Bill Shipley, 2000). For instance, r1,2||3,4 is the partial correlation between variables 1 and 2, while controlling for variables 3 and 4 (the || symbol is equivalent to "while controlling for"). We can compare partial correlations (e.g., r1,2||3,4) with original correlations (e.g., r1,2). If there is an insignificant difference, we infer that the controlled variables have minimal effect and may not influence the variables or even be part of the model. In contrast, if the partial correlations change significantly, the inference is that the causal link between the two variables is dependent to some degree on the controlled variables. These control variables are either anteceding causes or intervening variables. (text adapted from D Garson's original by RWW). + +For more on partial correlation please link to this great site by David Garson at NC State. + +For more on dependence separation ( d-separation) and constructing causal models see Richard Scheines' site. + +Why would you use of need partial correlations in GeneNetwork? It is often useful to compute correlations among traits while controlling for additional variables. Partial correlations may reveal more about the causality of relations. In a genetic context, partial correlations can be used to remove much of the variance associated with linkage and linkage disequilibrium. You can also control for age, age, and other common cofactors. + +Please see the related Glossary terms "Tissue Correlation". [RWW, Aug 21, 2009; Jan 30, 2010] + +#### PCA Trait or Eigentrait: + +If you place a number of traits in a Trait Collection you can carry out some of the key steps of a principal component analysis, including defining the variance directed along specific principal component eigenvectors. You can also plot the positions of cases against the first two eigenvectors; in essence a type of scatterplot. Finally, GeneNetwork allows you to exploit PCA methods to make new "synthetic" eigentraits from collections of correlated traits. These synthetic traits are the values of cases along specific eigenvectors and they may be less noisy than single traits. If this seems puzzling, then have a look at these useful PCA explanation by G. Dallas and by Powell and Lehe. How to do it: You can select and assemble many different traits into a single Trait Collection window using the check boxes and Add To Collection buttons. One of the most important function buttons in the Collection window is labeled Correlation Matrix. This function computes Pearson product moment correlations and Spearman rank order correlations for all possible pairs of traits in the Collection window. It also perfoms a principal component or factor analysis. For example, if you have 20 traits in the Collection window, the correlation matrix will consist of 20*19 or 190 correlations and the identity diagonal. Principal components analysis is a linear algebraic procedure that finds a small number of independent factors or principal components that efficiently explain variation in the original 20 traits. It is a effective method to reduce the dimensionality of a group of traits. If the 20 traits share a great deal of variation, then only two or three factors may explain variation among the traits. Instead of analyzing 20 traits as if they were independent, we can now analyze the main principal components labeled PC01, PC02, etc. PC01 and PC02 can be treated as new synthetic traits that represent the main sources of variation among original traits. You can treat a PC trait like any other trait except that it is not stored permanently in a database table. You can put a PC trait in your Collection window and see how well correlated each of the 20 original traits is with this new synthetic trait. You can also map a PC trait. [RWW, Aug 23, 2005] + +#### Permutation Test: + +A permutation test is a computationally intensive but conceptually simple method used to evaluate the statisical significance of findings. Permutation tests are often used to evaluate QTL significance. Some background: In order to detect parts of chromosomes that apparently harbor genes that contribute to differences in a trait's value, it is common to search for associations (linkage) across the entire genome. This is referred to as a "whole genome" scan, and it usually involves testing hundreds of independently segregating regions of the genome using hundreds, or even thousands of genetic markers (SNPs and microsatellites). A parametric test such as a conventional t test of F test can be used to estimate the probability of the null hypothesis at any single location in the genome (the null hypothesis is that there is no QTL at this particular location). But a parametric test of this type makes assumptions about the distribution of the trait (its normality), and also does not provide a way to correct for the large number of independent tests that are performed while scanning the whole genome. We need protection against many false discoveries as well as some assurance that we are not neglecting truly interesting locations. A permutation test is an elegant solution to both problems. The procedure involves randomly reassigning (permuting) traits values and genotypes of all cases used in the analysis. The permuted data sets have the same set of phenotypes and genotypes (in other words, distributions are the same), but obviously the permutation procedure almost invariably obliterates genuine gene-to-phenotype relation in large data sets. We typically generate several thousand permutations of the data. Each of these is analyzed using precisely the same method that was used to analyze the correctly ordered data set. We then compare statistical results of the original data set with the collection of values generated by the many permuted data sets. The hope is that the correctly ordered data are associated with larger LRS and LOD values than more than 95% of the permuted data sets. This is how we define the p = .05 whole genome significance threshold for a QTL. Please see the related Glossary terms "Significant threshold" and "Suggestive threshold". [RWW, July 15, 2005] + +#### Power to detect QTLs: + +An analysis of statistical power is useful to estimate numbers of replicates and strains needed to detect and resolve major sources of trait variance and covariance. A versatile method has been developed by Sen and colleagues (Sen et al., 2007) and implemented in the R program. qtlDesign. David Ashbrook implemented a version of this within Shiny that can help you estimate power for different QTL effect sizes, cohort sizes, and replication rates: + +#### Power Calculator (D. Ashbrook) + +We can see that in all situations power is increased more by increasing the number of lines than by increasing the number of biological replicates. Dependent upon the heritability of the trait, there is little gain in power when going above 4-6 biological replicates. [DGA, Mar 3, 2018] + +#### Probes and Probe Sets: + +In microarray experiments the probe is the immobilized sequence on the array that is complementary to the target message washed over the array surface. Affymetrix probes are 25-mer DNA sequences synthesized on a quartz substrate. There are a few million of these 25-mers in each 120-square micron cell of the array. The abundance of a single transcript is usualy estimated by as many as 16 perfect match probes and 16 mismatch probes. The collection of probes that targets a particular message is called a probe set. [RWW, Dec 21, 2004] + + +[Go back to index](#index) + +
+ +## Q + +#### QTL: + +A quantitative trait locus is a chromosome region that contains one or more sequence variants that modulates the distribution of a variable trait measured in a sample of genetically diverse individuals from an interbreeding population. Variation in a quantitative trait may be generated by a single QTL with the addition of some environmental noise. Variation may be oligogenic and be modulated by a few independently segregating QTLs. In many cases however, variation in a trait will be polygenic and influenced by large number of QTLs distributed on many chromosomes. Environment, technique, experimental design and a host of other factors also affect the apparent distribution of a trait. Most quantitative traits are therefore the product of complex interactions of genetic factors, developmental and epigenetics factors, environmental variables, and measurement error. [Williams RW, Dec 21, 2004] + +[Go back to index](#index) + +
+ +## R + +#### Recombinant Inbred Strain (RI or RIS) or Recombinant Inbred Line (RIL): + +An inbred strain whose chromosomes incorporate a fixed and permanent set of recombinations of chromosomes originally descended from two or more parental strains. Sets of RI strains (from 10 to 5000) are often used to map the chromosomal positions of polymorphic loci that control variance in phenotypes. + +For a terrific short summary of the uses of RI strains see 2007). + +Chromosomes of RI strains typically consist of alternating haplotypes of highly variable length that are inherited intact from the parental strains. In the case of a typical rodent RI strain made by crossing maternal strain C with paternal strain B (called a CXB RI strain), a chromosome will typically incorporate 3 to 5 alternating haplotype blocks with a structure such as BBBBBCCCCBBBCCCCCCCC, where each letter represents a genotype, series of similar genotype represent haplotypes, and where a transition between haplotypes represents a recombination. Both pairs of each chromosome will have the same alternating pattern, and all markers will be homozygous. Each of the different chromosomes (Chr 1, Chr 2, etc.) will have a different pattern of haplotypes and recombinations. The only exception is that the Y chromosome and the mitochondial genome, both of which are inherited intact from the paternal and maternal strain, respectively. For an RI strain to be useful for mapping purposes, the approximate position of recombinations along each chromsome need to be well defined either in terms of centimorgan or DNA basepair position. The precision with which these recombinations are mapped is a function of the number and position of the genotypes used to type the chromosomes--20 in the example above. Because markers and genotypes are often space quite far apart, often more than 500 Kb, the actual data entered into GeneNetwork will have some ambiguity at each recombination locus. The haplotype block BBBBBCCCCBBBCCCCCCCC will be entered as BBBBB?CCCC?BBB?CCCCCCCC where the ? mark indicates incomplete information over some (we hope) short interval. + +RI strains are almost always studied in sets or panels. All else being equal, the larger the set of RI strains, the greater the power and precision with which phenotypes can be mapped to chromosomal locations. The first set of eight RIs, the CXB RIs, were generated by Donald Bailey (By) from an intercross between a female BALB/cBy mouse (abbreviated C) and a male C57BL/6By mouse in the 1960s. The small panel of 8 CXB strains was originally used to determine if the major histocompatibility (MHC) locus on proximal Chr 17 was a key factor accounting for different immune responses such as tissue rejection. The methods used to determine the locations of recombinations relied on visible markers (coat color phenotypes such as the C and B loci) and the electrophoretic mobility of proteins. Somewhat larger RI sets were generated by Benjamin Taylor to map Mendelian and other major effect loci. In the 1990s the utility of RI sets for mapping was significantly improved thanks to higher density genotypes made possible by the use of microsatellite markers. Between 2005 and 2017, virtually all extant mouse and rat RI strains were regenotyped at many thousands of SNP markers, providing highly accurate maps of recombinations. + +While the potential utility of RI strains in mapping complex polygenic traits was obvious from the outset, the small number of strains only made it feasible to map quantitative traits with large effects. The first large RI sets were generated by plant geneticists (Burr et al. 2000) and this the plant genetics community holds a strong lead in the production of very large RI sets to study multigenic and polygenic traits and trait covariance and pleiotropy. + +By 2010 the number of mouse RI strains had increased to the point where defining causal gene and sequence variant was more practical. As of 2018 there are about 150 BXD strains (152 have been fully sequenced), ~100 Collaborative Cross strains (also all fully sequenced), and at least another 100 RI strains belonging to smaller sets that have been extremely well genotyped. + +Making RI strains: The usual procedure typically involves sib mating of the progeny of an F1 intercross for more than 20 generations. Even by the 5th filial (F) generation of successive matings, the RI lines are homozygous at 50% of loci and by F13, the value is above 90%. At F20 the lines are nearly fully inbred (~98%) and by convention are now referred to as inbred strains rather than inbred lines. + + +[Go back to index](#index) + +
+ +## S + +#### Scree Plots: + +GeneNetwork will often automatically generate a Scree Plot and the associated principal components (PCs) when you compute a Correlation Matrix for a group of traits that you have placed in your Trait Collection (a set of phenotypes and/or expression data for a specific population). Here is a nice definition of what a Scree plot is trying to tell you adopted and adapted from IOS (www.improvedoutcomes.com). + +A Scree Plot is a simple line segment plot that shows the fraction of total variance in the data as explained or represented by each PC. The PCs are ordered, and by definition are therefore assigned a number label, by decreasing order of contribution to total variance. The PC with the largest fraction contribution is labeled PC01. Such a plot when read left-to-right across the abscissa can often show a clear separation in fraction of total variance where the 'most important' components cease and the 'least important' components begin. The point of separation is often called the 'elbow'. (In the PCA literature, the plot is called a 'Scree' Plot because it often looks like a 'scree' slope, where rocks have fallen down and accumulated on the side of a mountain.) [Williams RW, Dec 20, 2008] + +#### Significant threshold: + +The significant threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.05, or a 5% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This threshold is computed by evaluating the distribution of highest LRS scores generated by a set of 2000 random permutations of strain means. For example, a random permutation of the correctly ordered data may give a peak LRS score of 10 somewhere across the genome. The set of 1000 or more of these highest LRS scores is then compared to the actual LRS obtained for the correctly ordered (real) data at any location in the genome. If fewer than 50 (5%) of the 1000 permutations have peak LRS scores anywhere in the genome that exceed that obtained at a particular locus using the correctly ordered data, then one can usually claim that a QTL has been defined at a genome-wide p-value of .05. The threshold will vary slightly each time it is recomputed due to the random generation of the permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the Analysis Tools area of the Trait Data and Editing Form. WebQTL does make it possible to search through hundreds of traits for those that may have significant linkage somewhere in the genome. Keep in mind that this introduces a second tier of multiple testing problems for which the permutation test will not usually provide adequate protection. If you anticipate mapping many independent traits, then you will need to correct for the number of traits you have tested. [Williams RW, Nov 14, 2004] + +#### SNP Seismograph Track: + +SNP is an acronym for single nucleotide polymorphisms (SNPs). SNPs are simple one base pair variants that distinguish individuals and strains. The SNP Seismograph track is a unique feature of physical maps in the GeneNetwork. Each track is customized for a particular cross and shows only those SNPs that differ between the two parental strains. For example, on mouse BXD maps, only the SNPs that differ between C57BL/6J and DBA/2J will be displayed. Regions with high numbers of SNPs are characterised by wider excursions of the yellow traces that extends along the x axis. Since these regions have many SNPs they have a higher prior probability of containing functional sequence differences that might have downstream effects on phenotypes. Large genes with many SNPs close to the peak LRS and that also have a biological connection with the trait ypu are studying are high priority candidate genes. + +The SNP track in WebQTL exploits the complete Celera Discovery System SNP set but adds an additional 500,000 inferred SNPs in both BXD and AXB/BXA crosses. These SNPs were inferred based on common haplotype structure using an Monte Carlo Markov chain algorithm developed by Gary Churchill and Natalie Blades and implemented by Robert Crowell, and RWW in July 2004. Raw data used to generate the SNP seismograph track were generated by Alex Williams and Chris Vincent, July 2003. The BXD track exploits a database of 1.75 million B vs D SNPs, whereas the AXB/BXA track exploits a database of 1.80 million A vs B SNPs. The names, sequences, and precise locations of most of these SNPs are the property of Celera Discovery Systems, whom we thank for allowing us to provide this level of display in WebQTL. + +Approximately 2.8 million additional SNPs generated by Perlegen for the NIEHS have been added to the SNP track by Robert Crowell (July-Aug 2005). We have also added all Wellcome-CTC SNPs and all relevant mouse SNPs from dbSNP. [Williams RW, Dec 25, 2004; Sept 3, 2005] + +#### Standard Error of the Mean (SE or SEM): + +In most GeneNetwork data sets, the SEM is computed as: +Standard Deviation (SD) divided by the square root of n - 1 +where n is the number of independent biological samples used to estimate the population mean. What this means in practice is that when n = 2 (as in many microarray data sets), the SEM and the SD are identical. This method of computing the SEM is conservative, but corrects to some extent for well known bias of the SEM discussed by Gurland and Tripathi (1971, A simple approximation for unbiased estimation of the standard deviation. Amer Stat 25:30-32). [Williams RW, Dec 17, 2008] + +#### Strain Distribution Pattern: + +A marker such as a SNP or microsatellite is genotyped using DNA obtained from each member of the mapping population. In the case of a genetic reference population, such as the BXD strains or the BayXSha Arabadopsis lines, this results in a text string of genotypes (e.g., BDDDBDBBBBDDBDDDBBBB... for BXD1 through BXD100). Each marker is associated with its own particular text string of genotypes that is often called the strain distribution pattern of the marker. (A more appropriate term would be the marker genotype string.) This string is converted to a numerical version, a genotype vector: -1111-11-1-1-1-111-1111-1-1-1-1..., where D=1, B=-1, H=0. Mapping a trait boils down to performing correlations between each trait and all of the genotype vectors. The genotype vector with the highest correlation (absolute value) is a good candidate for a QTL. [Williams RW, June 18, 2005] + +#### Suggestive Threshold: + +The suggestive threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.63, or a 63% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This is not a typographical error. The Suggestive LRS threshold is defined as that which yields, on average, one false positive per genome scan. That is, roughly one-third of scans at this threshold will yield no false positive, one-third will yield one false positive, and one-third will yield two or more false positives. This is a very permissive threshold, but it is useful because it calls attention to loci that may be worth follow-up. Regions of the genome in which the LRS exceeds the suggestive threshold are often worth tracking and screening. They are particularly useful in combined multicross metaanalysis of traits. If two crosses pick up the same suggestive locus, then that locus may be significant when the joint probability is computed. The suggestive threshold may vary slightly each time it is recomputed due to the random generation of permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the Analysis Tools area of the Trait Data and Editing Form. [Williams RW and Manly KF, Nov 15, 2004] + +#### Systems Genetics: + +Systems genetics or "network genetics" is an emerging new branch of genetics that aims to understand complex causal networks of interactions at multiple levels of biological organization. To put this in a simple context: Mendelian genetics can be defined as the search for linkage between a single trait and a single gene variant (1 to 1); complex trait analysis can be defined as the search for linkage between a single trait and a set of gene variants (QTLs, QTGs, and QTNs) and environmental cofactors (1 to many); and systems genetics can be defined as the search for linkages among networks of traits and networks of gene and environmental variants (many to many). + +A hallmark of systems genetics is the simultaneous consideration of groups (systems) of phenotypes from the primary level of molecular and cellular interactions that ultimately modulate global phenotypes such as blood pressure, behavior, or disease resistance. Changes in environment are also often important determinants of multiscalar phenotypes; reversing the standard notion of causality as flowing inexorably upward from the genome. Scientists who use a systems genetics approach often have a broad interest in modules of linked phenotypes. Causality in these complex dynamic systems is often contingent on environmental or temporal context, and often will involve feedback modulation. A systems genetics approach can be unusually powerful, but does require the use of large numbers of observations (large sample size), and more advanced statistical and computational models. + +Systems genetics is not really a new field and traces back to Sewall Wright's classical paper (Wright, 1921, "Correlation and Causation") that introduced path analysis to study systems of related phenotypes. Two factors have invigorated this field. The first factor is the advent of more sophisticated statistical methods including Structural Equation Modeling (SEM), System Dynamics Modeling, and Bayesian Network Modeling combined with powerful computer systems and efficient algorithms. The second factor is the relative ease with which it is now possible to acquire extensive and diverse phenotype data sets across genetic reference populations such as the BXD set of mice, the HXB set of rats, and the BayXSha lines of Arabidopsis (data are incorporated in the GeneNetwork). In the case of the BXD strains, a large research community has collectively generated hundreds of thousands of transcript phenotypes in different tissues and cells (level of expression), as well as hundreds of protein, cellular, pharmacological, and behavioral data types across a single genetic reference panel. Evaluating and modeling the associative and causal relations among these phenotypes is a major, and still relatively new area of research. Complex trait analysis and QTL mapping are both part of systems genetics in which causality is inferred using conventional genetic linkage (Li et al., 2005). One can often assert with confidence that a particular module of phenotypes (component of the variance and covariance) is modulated by sequence variants at a common locus. This provides a causal constraint that can be extremely helpful in more accurately modeling network architecture. Most models are currently static, but as the field matures, more sophisticated dynamic models will supplant steady-state models. + +The term "systems genetics" was coined by Grant Morahan, October 2004, during a visit to Memphis, as a more general and appropriate term to use instead of "genetical genomics." [Williams RW, April 11, 2005, revised Oct 22, 2005, April, 2008] + +[Go back to index](#index) + +
+ +## T + +#### Tissue Correlation: + +The tissue correlation is an estimate of the similarity of expression of two genes across different cells, tissues, or organs. In order to compute this type of correlation we first generate expression data for multiple different cell types, tissues, or whole organs from a single individual. There will be significant differences in gene expression across this sample and this variation can then be used to compute either Pearson product-moment correlations (r) or Spearman rank order correlations (rho) between any pair of genes, transcripts, or even exons. Since the samples are ideally all from one individual there should not be any genetic or environmental differences among samples. The difficulty in computing tissue correlations is that samples are not independent. For example, three samples of the small intestine (jejunum, ilieum, and duodenum) will have expression patterns that are quite similar to each other in comparison to three other samples, such as heart, brain, and bone. For this reason the nature of the sampling and how those samples are combined will greatly affect the correlation values. The tissue correlations in GeneNetwork were computed in a way that attempts to reduce the impact of this fact by combining closely related sample types. For example multiple data sets for different brain region were combined to generate a single average CNS tissue sample (generating a whole brain sample would have been an alternative method). + +However, there is really not optimal way to minimize the effects of this type of non-independence of samples. Some genes will have high expression in only a few tissues, for example the cholinergic receptor, nicotinic, alpha polypeptide 1 gene Chrna1 has high expression in muscle tissues (skeletal muscle = Mus, tongue = Ton, and esophagus = Eso) but lower expression in most other tissues. The very high correlation between Chrna1 and other genes with high expression only in muscle reflects their joint bimodality of expression. It does not mean that these genes or their proteins necessarily cooperate directly in molecular processes. [Williams RW, Dec 26, 2008] + + + +#### Transcript Location: + +The small orange triangle on the x-axis indicates the approximate position of the gene that corresponds to the transcript. These values were taken from the latest assembly of genome of the particular species. + +#### Transform: + +Most of the data sets in the GeneNetwork are ultimately derived from high resolution images of the surfaces of microarrays. Estimates the gene expression therefore involves extensive low-level image analysis. These processesing steps attempt to compensate for low spatial frequency "background" variation in image intensity that cannot be related to the actual hybridization signal, for example, a gradation of intensity across the whole array surface due to illumination differences, uneven hybridization, optical performance, scanning characteristics, etc. High spatial frequeny artifacts are also removed if they are likely to be artifacts: dust, scrathes on the array surface, and other "sharp" blemishes. The raw image data (for example, the Affymetrix DAT file) also needs to be registered to a template that assigns pixel values to expected array spots (cells). This image registration is an important process that users can usually take for granted. The end result is the reliable assignment of a set of image intensity values (pixels) to each probe. Each cell value generated using the Affymetrix U74Av2 array is associated with approximately 36 pixel intensity values (a 6x6 set of pixels, usually an effective 11 or 12-bit range of intensity). Affymetrix uses a method that simply ranks the values of these pixels and picks as the "representative value" the pixel that is closest to a particular rank order value, for example, the 24th highest of 36 pixels. The range of variation in intensity values amoung these ranked pixels provides a way to estimate the error of the estimate. The Affymetrix CEL files therefore consist of XY coordinates, the consensus value, and an error term. [Williams RW, April 30, 2005] + +#### Transgression: + +Most of us are familiar with the phrase "regression toward the mean." This refers to the tendency of progeny of a cross to have phenotype that are intermediate to those of the parents. Transgression refers to the converse: progeny that have more phenotypes that are higher and lower than those of either parent. Transgression is common, and provided that a trait is influenced by many independent sequence variants (a polygenic trait), transgression is the expectation. This is particularly true if the parents are different genetically, but by chance have similar phenotypes. Consider a trait that is controlled by six independent genes, A through F. The "0" allele at these size genes lowers body weight whereas the "1" allele increases body weight. If one parent has a 000111 6-locus genotype and the other parent has 111000 genotype, then they will have closely matched weight. But their progeny may inherit combinations as extreme as 000000 and 111111. + +Transgression means that you can rarely predict the distribution of phenotypes among a set of progeny unless you already have a significant amount of information about the genetic architecture of a trait (numbers of segregating variants that affect the trait, either interactions, and GXE effects). In practical terms this means that if the parents of a cross do NOT differ and you have good reasons to believe that the trait you are interested in is genetically complex, then you can be fairly confident that the progeny will display a much wider range of variation that the parents. [May 2011 by RWW]. + +[Go back to index](#index) + +
+ +## U + +[Go back to index](#index) + +
+ +## V + +[Go back to index](#index) + +
+ +## W + +#### Winsorize, Winsorise: + +QTL mapping results can be greatly affected by inclusion of outlier data. GeneNetwork will do its best to flag outliers for you in the Trait Data and Analysis pages (yellow highlighting). Before mapping, review the data, and if necessary, change values. Options for handling outliers include: (1) do nothing, (2) delete the outliers (trimming), (3) transform the data (e.g., logarithmic, arcsine, or logistic regression transforms), or (4) winsorize the distribution of values. Winsorizing is usually the easiest method to implement directly in GeneNetwork. + +How to winsorize: First review the distribution of values and define outliers. You should only do this one time, so think before you leap. Look at the Probability Plot of the trait by going to Trait Data and Analysis page and selecting Basic Statistics). For example, the figure below from GeneNetwork shows that at many as seven cases have relatively high values and as many as three have relatively low values (this trait is taken from Species = Mouse, Group = LXS, Type = Phenotype, Trait 10182). GeneNetwork code only declares the highest two values to be outliers, but you can use a more liberal definition and give all seven high values a haircut. It is advisable to winsorizes equal numbers of cases on each side of the distribution (high and low cases). In this case, the seven highest values were changed to match that of the 8th highest value (0.860). To retain the original rank order I added an incremental value of 0.01 to each (0.861, 0.862, etc). I did the same thing to the lowest seven values. Adding this increment is not necessary. + +The result in this case: a suggestive QTL on Chr 16 now reaches the significance threshold. + +The danger of winsorizing is doing it multiple times in different ways. You should transform or winsorize the data before mapping. And you should ideally only do any transformation/correction one time. If you fool around with different methods of transforming your data then you are asking for trouble by adding yet another level of multiple testing. If you feel compelled to experiment with different transforms, then you should/must report this in publications and explain why you did so. Demonstrating that mapping results are robust even using multiple transforms is one good excuse. [Williams RW, Jan 2, 2014] + + + + + +[Go back to index](#index) + +
+ +## X + +[Go back to index](#index) + +
+ +## Y +[Go back to index](#index) + + +
+ +## Z + +[Go back to index](#index) -- cgit v1.2.3 From 922be125592001f0e616351adcc05f1dbb16b38b Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Wed, 4 Nov 2020 17:49:11 +0300 Subject: Wrap rendered content in div container --- wqflask/wqflask/templates/glossary.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/wqflask/wqflask/templates/glossary.html b/wqflask/wqflask/templates/glossary.html index 718baf13..ed6f6ff7 100644 --- a/wqflask/wqflask/templates/glossary.html +++ b/wqflask/wqflask/templates/glossary.html @@ -3,7 +3,7 @@ {% block title %}Glossary{% endblock %} {% block content %} - -{{ rendered_markdown|safe }} - +
+ {{ rendered_markdown|safe }} +
{% endblock %} -- cgit v1.2.3 From 92dbf80bb45e66a93be6fea0fc0a82644cdbb326 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Wed, 4 Nov 2020 17:53:49 +0300 Subject: Add an edit link button to glossary page --- wqflask/wqflask/templates/glossary.html | 3 +++ 1 file changed, 3 insertions(+) diff --git a/wqflask/wqflask/templates/glossary.html b/wqflask/wqflask/templates/glossary.html index ed6f6ff7..3b29f20e 100644 --- a/wqflask/wqflask/templates/glossary.html +++ b/wqflask/wqflask/templates/glossary.html @@ -3,7 +3,10 @@ {% block title %}Glossary{% endblock %} {% block content %} +
+ + [Edit on Github] {{ rendered_markdown|safe }}
{% endblock %} -- cgit v1.2.3 From fa852e4a293735215e10bff9198c812fb85912ce Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Wed, 4 Nov 2020 18:04:48 +0300 Subject: Update test_markdown_routes * wqflask/tests/unit/wqflask/test_markdown_routes.py (test_render_markdown_when_fetching_locally): Fix failing test. --- wqflask/tests/unit/wqflask/test_markdown_routes.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/wqflask/tests/unit/wqflask/test_markdown_routes.py b/wqflask/tests/unit/wqflask/test_markdown_routes.py index 3de14276..3adf63e5 100644 --- a/wqflask/tests/unit/wqflask/test_markdown_routes.py +++ b/wqflask/tests/unit/wqflask/test_markdown_routes.py @@ -38,8 +38,8 @@ class TestMarkdownRoutesFunctions(unittest.TestCase): "/genenetwork/genenetwork2/" "wqflask/wqflask/static/" "glossary.md") - self.assertEqual("

Content

\n", - markdown_content) + self.assertRegexpMatches(markdown_content, + "Glossary of Terms and Features") @mock.patch('wqflask.markdown_routes.requests.get') def test_render_markdown_when_fetching_remotely(self, requests_mock): -- cgit v1.2.3 From 057b322c2d8270231170c35bb735edf075635357 Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Wed, 4 Nov 2020 20:16:19 +0300 Subject: Add link markers to elements which are referenced from elsewhere --- wqflask/wqflask/static/markdown/glossary.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/wqflask/wqflask/static/markdown/glossary.md b/wqflask/wqflask/static/markdown/glossary.md index 3c14ab78..d2c80b40 100644 --- a/wqflask/wqflask/static/markdown/glossary.md +++ b/wqflask/wqflask/static/markdown/glossary.md @@ -13,6 +13,8 @@ Resource. gn1.genenetwork.org/glossary.html ## A +
+ #### Additive Allele Effect: The additive allele effect is an estimate of the change in the average phenotype that would be produced by substituting a single allele of one type with that of another type (e.g., a replaced by A) in a population. In a standard F2 intercross between two inbred parental lines there are two alleles at every polymorphic locus that are often referred to as the little "a" allele and big "A" allele. F2 progeny inherit the a/a, a/A, or A/A genotypes at every genetic locus in a ratio close to 1:2:1. The additive effect is half of the difference between the mean of all cases that are homozygous for one parental allele (aa) compared to the mean of all cases that are homozygous for the other parental allele (AA): @@ -29,6 +31,8 @@ The dominance effects of alleles are also computed on maps for F2 populations (e
+
+ #### Bootstrap: A bootstrap sample is a randomly drawn sample (or resample) that is taken from the original data set and that has the same number of samples as the original data set. In a single bootstrap sample, some cases will by chance be represented one or more times; other cases may not be represented at all (in other words, the sampling is done "with replacement" after each selection). To get a better intuitive feel for the method, imagine a bag of 26 Scrabble pieces that contain each letter of the English alphabet. In a bootstrap sample of these 26 pieces, you would shake the bag, insert your hand, and draw out one piece. You would then write down that letter on a piece of paper, and the place that Scrabble piece back in the bag in preparation for the next random selection. You would repeat this process (shake, draw, replace) 25 more times to generate a single bootstrap resample of the alphabet. Some letters will be represented several time in each sample and others will not be represented at al. If you repeat this procedure 1000 times you would have a set of bootstrap resamples of the type that GN uses to remap data sets. @@ -65,6 +69,8 @@ Complex trait analysis is the study of multiple causes of variation of phenotype Composite interval mapping is a method of mapping chromosomal regions that controls for some fraction of the genetic variability in a quantitative trait. Unlike simple interval mapping, composite interval mapping usually controls for variation produced at one or more background marker loci. These background markers are generally chosen because they are already known to be close to the location of a significant QTL. By factoring out a portion of the genetic variance produced by a major QTL, one can occasionally detect secondary QTLs. WebQTL allows users to control for a single background marker. To select this marker, first run the Marker Regression analysis (and if necessary, check the box labeled display all LRS, select the appropriate locus, and the click on either Composite Interval Mapping or Composite Regression. A more powerful and effective alternative to composite interval mapping is pair-scan analysis. This latter method takes into accounts (models) both the independent effects of two loci and possible two-locus epistatic interactions. [Williams RW, Dec 20, 2004] +
+ #### Correlations: Pearson and Spearman: GeneNetwork provides tools to compute both Pearson product-moment correlations (the standard type of correlation), Spearman rank order correlations. Wikipedia and introductory statistics text will have a discussion of these major types of correlation. The quick advice is to use the more robust Spearman rank order correlation if the number of pairs of observations in a data set is less than about 30 and to use the more powerful but much more sensitive Pearson product-moment correlation when the number of observations is greater than 30 AND after you have dealt with any outliers. GeneNetwork automatically flags outliers for you in the Trait Data and Analysis form. GeneNetwork also allows you to modify values by either deleting or winsorising them. That means that you can use Pearson correlations even with smaller sample sizes after making sure that data are well distributed. Be sure to view the scatterplots associated with correlation values (just click on the value to generate a plot). Look for bivariate outliers. @@ -298,12 +304,16 @@ Interval mapping is a process in which the statistical significance of a hypothe ## L +
+ #### Literature Correlation: The literature correlation is a unique feature in GeneNetwork that quantifies the similarity of words used to describe genes and their functions. Sets of words associated with genes were extracted from MEDLINE/PubMed abstracts (Jan 2017 by Ramin Homayouni, Diem-Trang Pham, and Sujoy Roy). For example, about 2500 PubMed abstracts contain reference to the gene "Sonic hedgehog" (Shh) in mouse, human, or rat. The words in all of these abstracts were extracted and categorize by their information content. A word such as "the" is not interesting, but words such as "dopamine" or "development" are useful in quantifying similarity. Sets of informative words are then compared—one gene's word set is compared the word set for all other genes. Similarity values are computed for a matrix of about 20,000 genes using latent semantic indexing (see Xu et al., 2011). Similarity values are also known as literature correlations. These values are always positive and range from 0 to 1. Values between 0.5 and 1.0 indicate moderate-to-high levels of overlap of vocabularies. The literature correlation can be used to compare the "semantic" signal-to-noise of different measurements of gene, mRNA, and protein expression. Consider this common situation:There are three probe sets that measure Kit gene expression (1459588\_at, 1415900\_a\_at, and 1452514\_a\_at) in the Mouse BXD Lung mRNA data set (HZI Lung M430v2 (Apr08) RMA). Which one of these three gives the best measurement of Kit expression? It is impractical to perform quantitative rtPCR studies to answer this question, but there is a solid statistical answer that relies on Literature Correlation. Do the following: For each of the three probe sets, generate the top 1000 literature correlates. This will generate three apparently identical lists of genes that are known from the PubMed literature to be associated with the Kit oncogene. But the three lists are NOT actually identical when we look at the Sample Correlation column. To answer the question "which of the three probe sets is best", review the actual performance of the probe sets against this set of 1000 "friends of Kit". Do this by sorting all three lists by their Sample Correlation column (high to low). The clear winner is probe set 1415900_a_at. The 100th row in this probe set's list has a Sample Correlation of 0.620 (absolute value). In comparison, the 100th row for probe set 1452514_a_at has a Sample Correlation of 0.289. The probe set that targets the intron comes in last at 0.275. In conclusion, the probe set that targets the proximal half of the 3' UTR (1415900_a_at) has the highest "agreement" between Literature Correlation and Sample Correlation, and is our preferred measurement of Kit expression in the lung in this data set. (Updated by RWW and Ramin Homayouni, April 2017.) +
+ #### LOD: The logarithm of the odds (LOD) provides a measure of the association between variation in a phenotype and genetic differences (alleles) at a particular chromosomal locus (see Nyholt 2000 for a lovely review of LOD scores). @@ -324,6 +334,8 @@ With complete data at a marker, the log likelihood for the normal model reduces LOD values can be converted to LRS scores (likelihood ratio statistics) by multiplying by 4.61. The LOD is also roughly equivalent to the -log(P), where P is the probability of linkage (P = 0.001 => 3). The LOD itself is not a precise measurement of the probability of linkage, but in general for F2 crosses and RI strains, values above 3.3 will usually be worth attention for simple interval maps. [Williams RW, June 15, 2005, updated with text from Karl Broman, Oct 28, 2010, updated Apr 21, 2020 with Nyholt reference]. +
+ #### LRS: In the setting of mapping traits, the likelihood ratio statistic is used as a measurement of the association or linkage between differences in traits and differences in particular genotype markers. LRS or LOD values are usually plotted on the y-axis, whereas chromosomal location of the marker are usually plotted on the x-axis. In the case of a whole genome scan--a sequential analysis of many markers and locations across the entire genome--LRS values above 10 to 15 will usually be worth attention for when mapping with standard experimental crosses (e.g., F2 intercrosses or recombinant inbred strains). The term "likelihood ratio" is used to describe the relative probability (likelihood) of two different explanations of the variation in a trait. The first explanation (or model or hypothesis H1) is that the differences in the trait ARE associated with that particular DNA sequence difference or marker. Very small probability values indicate that H1 is probably true. The second "null" hypothesis (Hnull or H0) is that differences in the trait are NOT associated with that particular DNA sequence. We can use the ratio of these two probabilities and models (H1 divided by H0) as our score. The math is a little bit more complicated and the LRS score is actually equal to -2 times the ratio of the natural logarithms of the two probabilities. For example, if the probability of H0 is 0.05 (only a one-in-twenty probability that the marker is associated with the trait by chance), whereas and the probability of H1 is 1 (the marker is certainly not linked to the trait), then the LRS value is 5.991. In Excel the equation giving the LRS result of 5.991 would look like this "=-2*(LN(0.05)-LN(1)). [Williams RW, Dec 13, 2004, updated Nov 18, 2009, updated Dec 19, 2012] @@ -415,6 +427,8 @@ Please see the related Glossary terms "Tissue Correlation". [RWW, Aug 21, 2009; If you place a number of traits in a Trait Collection you can carry out some of the key steps of a principal component analysis, including defining the variance directed along specific principal component eigenvectors. You can also plot the positions of cases against the first two eigenvectors; in essence a type of scatterplot. Finally, GeneNetwork allows you to exploit PCA methods to make new "synthetic" eigentraits from collections of correlated traits. These synthetic traits are the values of cases along specific eigenvectors and they may be less noisy than single traits. If this seems puzzling, then have a look at these useful PCA explanation by G. Dallas and by Powell and Lehe. How to do it: You can select and assemble many different traits into a single Trait Collection window using the check boxes and Add To Collection buttons. One of the most important function buttons in the Collection window is labeled Correlation Matrix. This function computes Pearson product moment correlations and Spearman rank order correlations for all possible pairs of traits in the Collection window. It also perfoms a principal component or factor analysis. For example, if you have 20 traits in the Collection window, the correlation matrix will consist of 20*19 or 190 correlations and the identity diagonal. Principal components analysis is a linear algebraic procedure that finds a small number of independent factors or principal components that efficiently explain variation in the original 20 traits. It is a effective method to reduce the dimensionality of a group of traits. If the 20 traits share a great deal of variation, then only two or three factors may explain variation among the traits. Instead of analyzing 20 traits as if they were independent, we can now analyze the main principal components labeled PC01, PC02, etc. PC01 and PC02 can be treated as new synthetic traits that represent the main sources of variation among original traits. You can treat a PC trait like any other trait except that it is not stored permanently in a database table. You can put a PC trait in your Collection window and see how well correlated each of the 20 original traits is with this new synthetic trait. You can also map a PC trait. [RWW, Aug 23, 2005] +
+ #### Permutation Test: A permutation test is a computationally intensive but conceptually simple method used to evaluate the statisical significance of findings. Permutation tests are often used to evaluate QTL significance. Some background: In order to detect parts of chromosomes that apparently harbor genes that contribute to differences in a trait's value, it is common to search for associations (linkage) across the entire genome. This is referred to as a "whole genome" scan, and it usually involves testing hundreds of independently segregating regions of the genome using hundreds, or even thousands of genetic markers (SNPs and microsatellites). A parametric test such as a conventional t test of F test can be used to estimate the probability of the null hypothesis at any single location in the genome (the null hypothesis is that there is no QTL at this particular location). But a parametric test of this type makes assumptions about the distribution of the trait (its normality), and also does not provide a way to correct for the large number of independent tests that are performed while scanning the whole genome. We need protection against many false discoveries as well as some assurance that we are not neglecting truly interesting locations. A permutation test is an elegant solution to both problems. The procedure involves randomly reassigning (permuting) traits values and genotypes of all cases used in the analysis. The permuted data sets have the same set of phenotypes and genotypes (in other words, distributions are the same), but obviously the permutation procedure almost invariably obliterates genuine gene-to-phenotype relation in large data sets. We typically generate several thousand permutations of the data. Each of these is analyzed using precisely the same method that was used to analyze the correctly ordered data set. We then compare statistical results of the original data set with the collection of values generated by the many permuted data sets. The hope is that the correctly ordered data are associated with larger LRS and LOD values than more than 95% of the permuted data sets. This is how we define the p = .05 whole genome significance threshold for a QTL. Please see the related Glossary terms "Significant threshold" and "Suggestive threshold". [RWW, July 15, 2005] @@ -481,6 +495,8 @@ A Scree Plot is a simple line segment plot that shows the fraction of total vari The significant threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.05, or a 5% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This threshold is computed by evaluating the distribution of highest LRS scores generated by a set of 2000 random permutations of strain means. For example, a random permutation of the correctly ordered data may give a peak LRS score of 10 somewhere across the genome. The set of 1000 or more of these highest LRS scores is then compared to the actual LRS obtained for the correctly ordered (real) data at any location in the genome. If fewer than 50 (5%) of the 1000 permutations have peak LRS scores anywhere in the genome that exceed that obtained at a particular locus using the correctly ordered data, then one can usually claim that a QTL has been defined at a genome-wide p-value of .05. The threshold will vary slightly each time it is recomputed due to the random generation of the permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the Analysis Tools area of the Trait Data and Editing Form. WebQTL does make it possible to search through hundreds of traits for those that may have significant linkage somewhere in the genome. Keep in mind that this introduces a second tier of multiple testing problems for which the permutation test will not usually provide adequate protection. If you anticipate mapping many independent traits, then you will need to correct for the number of traits you have tested. [Williams RW, Nov 14, 2004] +
+ #### SNP Seismograph Track: SNP is an acronym for single nucleotide polymorphisms (SNPs). SNPs are simple one base pair variants that distinguish individuals and strains. The SNP Seismograph track is a unique feature of physical maps in the GeneNetwork. Each track is customized for a particular cross and shows only those SNPs that differ between the two parental strains. For example, on mouse BXD maps, only the SNPs that differ between C57BL/6J and DBA/2J will be displayed. Regions with high numbers of SNPs are characterised by wider excursions of the yellow traces that extends along the x axis. Since these regions have many SNPs they have a higher prior probability of containing functional sequence differences that might have downstream effects on phenotypes. Large genes with many SNPs close to the peak LRS and that also have a biological connection with the trait ypu are studying are high priority candidate genes. @@ -519,6 +535,8 @@ The term "systems genetics" was coined by Grant Morahan, October 2004, during a ## T +
+ #### Tissue Correlation: The tissue correlation is an estimate of the similarity of expression of two genes across different cells, tissues, or organs. In order to compute this type of correlation we first generate expression data for multiple different cell types, tissues, or whole organs from a single individual. There will be significant differences in gene expression across this sample and this variation can then be used to compute either Pearson product-moment correlations (r) or Spearman rank order correlations (rho) between any pair of genes, transcripts, or even exons. Since the samples are ideally all from one individual there should not be any genetic or environmental differences among samples. The difficulty in computing tissue correlations is that samples are not independent. For example, three samples of the small intestine (jejunum, ilieum, and duodenum) will have expression patterns that are quite similar to each other in comparison to three other samples, such as heart, brain, and bone. For this reason the nature of the sampling and how those samples are combined will greatly affect the correlation values. The tissue correlations in GeneNetwork were computed in a way that attempts to reduce the impact of this fact by combining closely related sample types. For example multiple data sets for different brain region were combined to generate a single average CNS tissue sample (generating a whole brain sample would have been an alternative method). -- cgit v1.2.3 From 5fabe2763200b5607d2f8ec3eaa85a7c23c9875b Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Wed, 4 Nov 2020 20:33:45 +0300 Subject: Add missing image --- wqflask/wqflask/static/images/Congenic.png | Bin 0 -> 56578 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 wqflask/wqflask/static/images/Congenic.png diff --git a/wqflask/wqflask/static/images/Congenic.png b/wqflask/wqflask/static/images/Congenic.png new file mode 100644 index 00000000..8cd489a4 Binary files /dev/null and b/wqflask/wqflask/static/images/Congenic.png differ -- cgit v1.2.3 From 813204c39aebe17c43d8ea94c9e20a6a32914cba Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Wed, 4 Nov 2020 20:34:13 +0300 Subject: Use new glossary link from glossary_blueprint in html templates --- wqflask/wqflask/templates/base.html | 2 +- wqflask/wqflask/templates/collections/view.html | 4 ++-- wqflask/wqflask/templates/gsearch_gene.html | 8 ++++---- wqflask/wqflask/templates/gsearch_pheno.html | 4 ++-- wqflask/wqflask/templates/index_page.html | 6 +++--- wqflask/wqflask/templates/index_page_orig.html | 2 +- wqflask/wqflask/templates/search_result_page.html | 8 ++++---- wqflask/wqflask/templates/show_trait_calculate_correlations.html | 2 +- 8 files changed, 18 insertions(+), 18 deletions(-) diff --git a/wqflask/wqflask/templates/base.html b/wqflask/wqflask/templates/base.html index 0f4e5ef5..6b584751 100644 --- a/wqflask/wqflask/templates/base.html +++ b/wqflask/wqflask/templates/base.html @@ -66,7 +66,7 @@ diff --git a/wqflask/wqflask/templates/index_page_orig.html b/wqflask/wqflask/templates/index_page_orig.html index 6b3bec9a..13ca52df 100755 --- a/wqflask/wqflask/templates/index_page_orig.html +++ b/wqflask/wqflask/templates/index_page_orig.html @@ -177,7 +177,7 @@ Gene Ontology.
  • RIF=diabetes LRS=(9 999 Chr2 100 105) transLRS=(9 999 10) - finds diabetes-associated transcripts with peak + finds diabetes-associated transcripts with peak trans eQTLs on Chr 2 between 100 and 105 Mb with LRS scores between 9 and 999.
    • diff --git a/wqflask/wqflask/templates/search_result_page.html b/wqflask/wqflask/templates/search_result_page.html index 2318bfb8..6d9ea8fe 100644 --- a/wqflask/wqflask/templates/search_result_page.html +++ b/wqflask/wqflask/templates/search_result_page.html @@ -312,7 +312,7 @@ 'orderSequence': [ "desc", "asc"] }, { - 'title': "High P ?", + 'title': "High P ?", 'type': "natural-minus-na", 'data': "lrs_score", 'width': "60px", @@ -325,7 +325,7 @@ 'data': "lrs_location" }, { - 'title': "Effect Size ?", + 'title': "Effect Size ?", 'type': "natural-minus-na", 'data': "additive", 'width': "85px", @@ -381,7 +381,7 @@ 'orderSequence': [ "desc", "asc"] }, { - 'title': "Max LRS ?", + 'title': "Max LRS ?", 'type': "natural-minus-na", 'data': "lrs_score", 'width': "80px", @@ -394,7 +394,7 @@ 'data': "lrs_location" }, { - 'title': "Effect Size ?", + 'title': "Effect Size ?", 'type': "natural-minus-na", 'width': "120px", 'data': "additive", diff --git a/wqflask/wqflask/templates/show_trait_calculate_correlations.html b/wqflask/wqflask/templates/show_trait_calculate_correlations.html index bc1d4091..4f25e90a 100644 --- a/wqflask/wqflask/templates/show_trait_calculate_correlations.html +++ b/wqflask/wqflask/templates/show_trait_calculate_correlations.html @@ -120,7 +120,7 @@ is computed between trait data and any other traits in the sample database selected above. Use - Spearman + Spearman Rank when the sample size is small (<20) or when there are influential outliers. -- cgit v1.2.3 From 1948d1ed9e8647b1f54c87a59aa308b9d22c7f8e Mon Sep 17 00:00:00 2001 From: zsloan Date: Wed, 4 Nov 2020 15:57:58 -0600 Subject: Changed numeric-html sorting for the Tissue/Lit r columns to natural-minus-na so it deals with the "--" values * wqflask/wqflask/templates/correlation_page.html - Replaced numeric-html sorting with natural-minus-na for the lit/tissue r columns --- wqflask/wqflask/templates/correlation_page.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html index 134f15be..bc0b592c 100644 --- a/wqflask/wqflask/templates/correlation_page.html +++ b/wqflask/wqflask/templates/correlation_page.html @@ -367,8 +367,8 @@ { "orderDataType": "dom-innertext", 'orderSequence': [ "desc", "asc"] }, { "type": "natural" }, { "type": "scientific" }, - { "type": "numeric-html", 'orderSequence': [ "desc", "asc"] }, - { "type": "numeric-html", 'orderSequence': [ "desc", "asc"] }, + { "type": "natural-minus-na", 'orderSequence': [ "desc", "asc"] }, + { "type": "natural-minus-na", 'orderSequence': [ "desc", "asc"] }, { "type": "scientific" }, { "type": "natural-minus-na" }, { "type": "natural-minus-na" }, -- cgit v1.2.3 From 6b4bcc526c27a17f96704b54fbfa6a506b3a22f9 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Thu, 5 Nov 2020 22:30:33 +0300 Subject: add test for samplelist and show_trait --- wqflask/tests/wqflask/show_trait/testSampleList.py | 18 +++++++ .../tests/wqflask/show_trait/test_show_trait.py | 55 ++++++++++++++++++++++ 2 files changed, 73 insertions(+) create mode 100644 wqflask/tests/wqflask/show_trait/testSampleList.py diff --git a/wqflask/tests/wqflask/show_trait/testSampleList.py b/wqflask/tests/wqflask/show_trait/testSampleList.py new file mode 100644 index 00000000..4cc8c4da --- /dev/null +++ b/wqflask/tests/wqflask/show_trait/testSampleList.py @@ -0,0 +1,18 @@ +import unittest +import re +from unittest import mock +from wqflask.show_trait.SampleList import natural_sort + + +class TestSampleList(unittest.TestCase): + def test_natural_sort(self): + "Sort the list into natural alphanumeric order." + + characters_list = ["z", "f", "q", "s", "t", "a", "g"] + names_list = ["temp1", "publish", "Sample", "Dataset"] + + natural_sort(characters_list) + natural_sort(names_list) + + self.assertEqual(characters_list, ["a", "f", "g", "q", "s", "t", "z"]) + self.assertEqual(names_list, ["Dataset", "Sample", "publish", "temp1"]) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index 24150738..b1a91bbe 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -11,6 +11,7 @@ from wqflask.show_trait.show_trait import get_trait_units from wqflask.show_trait.show_trait import get_nearest_marker from wqflask.show_trait.show_trait import get_genotype_scales from wqflask.show_trait.show_trait import requests +from wqflask.show_trait.show_trait import get_scales_from_genofile class TraitObject: @@ -240,3 +241,57 @@ class TestTraits(unittest.TestCase): expected_results = {f"{file_location}": [["physic", "Mb"]]} self.assertEqual(get_genotype_scales(file_location), expected_results) mock_get_scales.assert_called_once_with(file_location) + + @mock.patch("wqflask.show_trait.show_trait.locate_ignore_error") + def test_get_scales_from_genofile_found(self, mock_location_ignore): + # test no complete to be continued with + # a .geno file + mock_location_ignore.return_value = True + mock_file_with_one_line = mock.mock_open( + read_data="Some data from opened file") + + mock_file = """#@scale and random data:is here_returned\n + This is second with spaced with tabs\n """ + mock_file_result = mock.mock_open(read_data=mock_file) + + with mock.patch("builtins.open", mock_file_with_one_line): + result = get_scales_from_genofile("~/data/file") + self.assertEqual(result, [['morgan', 'cM']]) + + with mock.patch("builtins.open", mock_file_result): + results = get_scales_from_genofile("~data/file_geno") + self.assertEqual(results, [['physic', 'Mb']]) + + @mock.patch("wqflask.show_trait.show_trait.locate_ignore_error") + def test_get_scales_from_genofile_found(self, mock_ingore_location): + """"add test for get scales from genofile where file is found""" + mock_ingore_location.return_value = True + geno_file = """ + #sample line with no @scales:other\n + #sample line @scales and :separated by semicolon\n + This attempts to check whether\n + """ + + geno_file_string = "@line start with @ and has @scale:morgan" + + file_location = "~/data/file.geno" + + mock_open_geno_file = mock.mock_open(read_data=geno_file) + with mock.patch("builtins.open", mock_open_geno_file): + results = get_scales_from_genofile(file_location) + self.assertEqual(results, [["morgan", "cM"]]) + + mock_open_string = mock.mock_open(read_data=geno_file_string) + + with mock.patch("builtins.open", mock_open_string): + result2 = get_scales_from_genofile(file_location) + self.assertEqual([['morgan', 'cM']], result2) + + @mock.patch("wqflask.show_trait.show_trait.locate_ignore_error") + def test_get_scales_from_genofile_not_found(self, mock_location_ignore): + mock_location_ignore.return_value = False + + expected_results = [["physic", "Mb"]] + results = get_scales_from_genofile("~/data/file") + mock_location_ignore.assert_called_once_with("~/data/file", "genotype") + self.assertEqual(results, expected_results) -- cgit v1.2.3 From 162e3e1acc1fea2548f7caa71fa3fc425c0a4ccd Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Thu, 5 Nov 2020 22:47:30 +0300 Subject: remove duplicates --- wqflask/tests/wqflask/show_trait/test_show_trait.py | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index b1a91bbe..600baefb 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -242,25 +242,6 @@ class TestTraits(unittest.TestCase): self.assertEqual(get_genotype_scales(file_location), expected_results) mock_get_scales.assert_called_once_with(file_location) - @mock.patch("wqflask.show_trait.show_trait.locate_ignore_error") - def test_get_scales_from_genofile_found(self, mock_location_ignore): - # test no complete to be continued with - # a .geno file - mock_location_ignore.return_value = True - mock_file_with_one_line = mock.mock_open( - read_data="Some data from opened file") - - mock_file = """#@scale and random data:is here_returned\n - This is second with spaced with tabs\n """ - mock_file_result = mock.mock_open(read_data=mock_file) - - with mock.patch("builtins.open", mock_file_with_one_line): - result = get_scales_from_genofile("~/data/file") - self.assertEqual(result, [['morgan', 'cM']]) - - with mock.patch("builtins.open", mock_file_result): - results = get_scales_from_genofile("~data/file_geno") - self.assertEqual(results, [['physic', 'Mb']]) @mock.patch("wqflask.show_trait.show_trait.locate_ignore_error") def test_get_scales_from_genofile_found(self, mock_ingore_location): -- cgit v1.2.3 From 7932f962aff0ac35dd53b84e483f405de81c0612 Mon Sep 17 00:00:00 2001 From: zsloan Date: Thu, 5 Nov 2020 15:07:58 -0600 Subject: Imported Scroller and used it with correlation page table --- wqflask/wqflask/templates/correlation_page.html | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html index bc0b592c..06499ec6 100644 --- a/wqflask/wqflask/templates/correlation_page.html +++ b/wqflask/wqflask/templates/correlation_page.html @@ -215,6 +215,7 @@ + {% endblock %} + -- cgit v1.2.3 From 9560e6337d00a82d8f36073b31215735eb9a8c01 Mon Sep 17 00:00:00 2001 From: zsloan Date: Fri, 6 Nov 2020 14:49:15 -0600 Subject: Removed the page selection from the top of the table because it created an awkward gap between the column show/hide buttons and the table, and this table will be changed to use Scroller instead of paging soon regardless --- wqflask/wqflask/templates/search_result_page.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/wqflask/wqflask/templates/search_result_page.html b/wqflask/wqflask/templates/search_result_page.html index 38f224e0..b3f56293 100644 --- a/wqflask/wqflask/templates/search_result_page.html +++ b/wqflask/wqflask/templates/search_result_page.html @@ -427,7 +427,7 @@ }{% endif %} ], "order": [[1, "asc" ]], - 'sDom': "pitirp", + 'sDom': "itirp", 'iDisplayLength': 500, 'deferRender': true, 'paging': true, -- cgit v1.2.3 From 8c0e0f0629d69672375159200c57136ced644572 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Sun, 8 Nov 2020 21:51:11 +0300 Subject: Change defaultline argument in the build_line_list from None to empty string --- wqflask/wqflask/marker_regression/plink_mapping.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/wqflask/wqflask/marker_regression/plink_mapping.py b/wqflask/wqflask/marker_regression/plink_mapping.py index fd91b6ca..8d57d556 100644 --- a/wqflask/wqflask/marker_regression/plink_mapping.py +++ b/wqflask/wqflask/marker_regression/plink_mapping.py @@ -154,7 +154,7 @@ def parse_plink_output(output_filename, species): # function: convert line from str to list; # output: lineList list ####################################################### -def build_line_list(line=None): +def build_line_list(line=""): line_list = line.strip().split(' ')# irregular number of whitespaces between columns line_list = [item for item in line_list if item !=''] line_list = [item.strip() for item in line_list] -- cgit v1.2.3 From 8e55eaf473d98c1d165fcaf60ab0e24abb6acd9e Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Sun, 8 Nov 2020 22:00:40 +0300 Subject: add tests for marker_regression/gemma_mapping --- .../marker_regression/test_gemma_mapping.py | 170 +++++++++++++++++++++ 1 file changed, 170 insertions(+) create mode 100644 wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py new file mode 100644 index 00000000..4a88509e --- /dev/null +++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py @@ -0,0 +1,170 @@ +#test for wqflask/marker_regression/gemma_mapping.py +import unittest +import random +from unittest import mock +from wqflask.marker_regression.gemma_mapping import run_gemma +from wqflask.marker_regression.gemma_mapping import gen_pheno_txt_file +from wqflask.marker_regression.gemma_mapping import gen_covariates_file +from wqflask.marker_regression.gemma_mapping import parse_gemma_output +from wqflask.marker_regression.gemma_mapping import parse_loco_output + + +class AttributeSetter: + def __init__(self, obj): + for key, val in obj.items(): + setattr(self, key, val) + + +class MockDatasetGroup(AttributeSetter): + def get_samplelist(self): + return None + + +class TestGemmaMapping(unittest.TestCase): + # def test_fail(self): + # self.assertEqual(2,3) + + @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data") + def setUp(self): + pass + + @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file") + @mock.patch("wqflask.marker_regression.gemma_mapping.os") + def test_run_gemma_first_run_set_true(self, mock_gen_pheno, mock_os): + chromosomes = AttributeSetter({"chromosomes": "SA"}) + covariates = "XI:X2,X4:X3,X6:X7" + dataset_group = MockDatasetGroup({"genofile": "fileX"}) + dataset = AttributeSetter( + {"group": dataset_group, "name": "dataset1_name", "species": chromosomes}) + trait = AttributeSetter({"name": "trait1"}) + mock_gen_pheno.side_effect = None + mock_gen_pheno.return_value = None + mock_os.path.isfile.return_value = True + + @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output") + def test_run_gemma_first_run_loco_set_false(self, mock_parse_loco): + dataset = AttributeSetter( + {"group": AttributeSetter({"genofile": "genofile.geno"})}) + + output_files = "file1" + use_loco = False + mock_parse_loco.side_effect = None + mock_parse_loco.return_value = [] + this_trait = AttributeSetter({"name": "t1"}) + + result = run_gemma(this_trait=this_trait, this_dataset=dataset, samples=[], vals=[ + ], covariates="", use_loco=True, first_run=False, output_files=output_files) + + expected_results = ([], "file1") + self.assertEqual(expected_results, result) + + @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data") + def test_gen_pheno_txt_file(self): + with mock.patch("builtins.open", mock.mock_open())as mock_open: + gen_pheno_txt_file(this_dataset={}, genofile_name="", vals=[ + "x", "w", "q", "we", "R"], trait_filename="fitr.re") + + mock_open.assert_called_once_with( + '/home/user/data/gn2/fitr.re.txt', 'w') + filehandler = mock_open() + values = ["x", "w", "q", "we", "R"] + write_calls = [mock.call('NA\n'), mock.call('w\n'), mock.call( + 'q\n'), mock.call('we\n'), mock.call('R\n')] + + filehandler.write.assert_has_calls(write_calls) + + @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files") + @mock.patch("wqflask.marker_regression.gemma_mapping.create_trait") + @mock.patch("wqflask.marker_regression.gemma_mapping.create_dataset") + def test_gen_covariates_file(self, create_dataset, create_trait, flat_files): + covariates = "X1:X2,Y1:Y2,M1:M3,V1:V2" + samplelist = ["X1", "X2", "X3", "X4"] + create_dataset_side_effect = [] + create_trait_side_effect = [] + + for i in range(4): + create_dataset_side_effect.append(AttributeSetter({"name": f'name_{i}'})) + create_trait_side_effect.append( + AttributeSetter({"data": [f'data_{i}']})) + + create_dataset.side_effect = create_trait_side_effect + create_trait.side_effect = create_trait_side_effect + + group = MockDatasetGroup({"name": "group_X", "samplelist": samplelist}) + this_dataset = AttributeSetter({"group": group}) + flat_files.return_value = "Home/Genenetwork" + + with mock.patch("builtins.open", mock.mock_open())as mock_open: + gen_covariates_file(this_dataset=this_dataset, covariates=covariates, + samples=["x1", "x2", "X3"]) + # test mocked methods + + create_dataset.assert_has_calls( + [mock.call('X2'), mock.call('Y2'), mock.call('M3'), mock.call('V2')]) + mock_calls = [] + trait_names = ["X1", "Y1", "M1", "V1"] + + for i, trait in enumerate(create_trait_side_effect): + mock_calls.append( + mock.call(dataset=trait, name=trait_names[i], cellid=None)) + + create_trait.assert_has_calls(mock_calls) + + # test writing of covariates.txt + + flat_files.assert_called_once_with('mapping') + mock_open.assert_called_once_with( + 'Home/Genenetwork/group_X_covariates.txt', 'w') + filehandler = mock_open() + # expected all-9 + filehandler.write.assert_has_calls([mock.call( + '-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('\n')]) + + @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img/") + def test_parse_gemma_output_obj_returned(self): + file = """X/Y\t gn2\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5 +X/Y\tgn2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5 +chr\tgn1\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7 +X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4 +125\tgn9\t433575\tQ\tE\tA\tP\tMMB\tCDE\t0.67 +""" + with mock.patch("builtins.open", mock.mock_open(read_data=file)) as mock_open: + results = parse_gemma_output(genofile_name="gema_file") + expected = [{'name': ' gn2', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'gn2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'gn7', 'chr': 'X', 'Mb': 2.324424, 'p_value': 0.4, 'lod_score': 0.3979400086720376}, {'name': 'gn9', 'chr': 125, 'Mb': 0.433575, 'p_value': 0.67, 'lod_score': 0.17392519729917352}] + + mock_open.assert_called_once_with( + "/home/user/img/gema_file_output.assoc.txt") + + self.assertEqual(results, expected) + + @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img") + def test_xparse_gemma_output_empty_return(self): + output_file_results = """chr\t today""" + with mock.patch("builtins.open", mock.mock_open(read_data=output_file_results)) as mock_open: + results = parse_gemma_output(genofile_name="gema_file") + self.assertEqual(results, []) + + @mock.patch("builtins.open", mock.mock_open(read_data="chr\t")) + def test_parse_gemma_output_empty_return(self): + #duplicate + string_read = parse_gemma_output(genofile_name="hdf") + # print(string_read) + + @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp") + @mock.patch("wqflask.marker_regression.gemma_mapping.os") + def test_parse_loco_output_file_found(self, mock_os): + mock_os.path.isfile.return_value = False + file_to_write = """{"files":["file_1","file_2"]}""" + + + @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp") + @mock.patch("wqflask.marker_regression.gemma_mapping.os") + def test_parse_loco_output_file_not_found(self, mock_os): + + mock_os.path.isfile.return_value = False + file_to_write = """{"files":["file_1","file_2"]}""" + + with mock.patch("builtins.open", mock.mock_open(read_data=file_to_write)) as mock_open: + results = parse_loco_output( + this_dataset={}, gwa_output_filename=".xw/") + self.assertEqual(results, []) -- cgit v1.2.3 From 5e50e19a014efdfcf0ce1397025879d4ada4d271 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Sun, 8 Nov 2020 22:04:52 +0300 Subject: add tests for marker_regression/plink_mapping.py --- .../marker_regression/test_plink_mapping.py | 83 ++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 wqflask/tests/wqflask/marker_regression/test_plink_mapping.py diff --git a/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py b/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py new file mode 100644 index 00000000..b827fd0f --- /dev/null +++ b/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py @@ -0,0 +1,83 @@ +# test for wqflask/marker_regression/plink_mapping.py +import unittest +from unittest import mock +from wqflask.marker_regression.plink_mapping import build_line_list +from wqflask.marker_regression.plink_mapping import get_samples_from_ped_file +from wqflask.marker_regression.plink_mapping import flat_files +from wqflask.marker_regression.plink_mapping import gen_pheno_txt_file_plink +from wqflask.marker_regression.plink_mapping import parse_plink_output + + +class AttributeSetter: + def __init__(self, obj): + for key, val in obj.items(): + setattr(self, key, val) + + +class TestPlinkMapping(unittest.TestCase): + + def test_build_line_list(self): + line_1 = "this is line one test" + irregular_line = " this is an, irregular line " + exp_line1 = ["this", "is", "line", "one", "test"] + + results = build_line_list(irregular_line) + self.assertEqual(exp_line1, build_line_list(line_1)) + self.assertEqual([], build_line_list()) + self.assertEqual(["this", "is", "an,", "irregular", "line"], results) + + @mock.patch("wqflask.marker_regression.plink_mapping.flat_files") + def test_get_samples_from_ped_file(self, mock_flat_files): + dataset = AttributeSetter({"group": AttributeSetter({"name": "n_1"})}) + file_sample = """Expected_1\tline test +Expected_2\there + Expected_3\tthree""" + mock_flat_files.return_value = "/home/user/" + with mock.patch("builtins.open", mock.mock_open(read_data=file_sample)) as mock_open: + results = get_samples_from_ped_file(dataset) + mock_flat_files.assert_called_once_with("mapping") + mock_open.assert_called_once_with("/home/user/n_1.ped", "r") + self.assertEqual( + ["Expected_1", "Expected_2", "Expected_3"], results) + + @mock.patch("wqflask.marker_regression.plink_mapping.TMPDIR", "/home/user/data/") + @mock.patch("wqflask.marker_regression.plink_mapping.get_samples_from_ped_file") + def test_gen_pheno_txt_file_plink(self, mock_samples): + mock_samples.return_value = ["Expected_1", "Expected_2", "Expected_3"] + + trait = AttributeSetter({"name": "TX"}) + dataset = AttributeSetter({"group": AttributeSetter({"name": "n_1"})}) + vals = ["value=K1", "value=K2", "value=K3"] + with mock.patch("builtins.open", mock.mock_open()) as mock_open: + results = gen_pheno_txt_file_plink(this_trait=trait, dataset=dataset, + vals=vals, pheno_filename="ph_file") + mock_open.assert_called_once_with( + "/home/user/data/ph_file.txt", "wb") + filehandler = mock_open() + calls_expected = [mock.call('FID\tIID\tTX\n'), + mock.call('Expected_1\tExpected_1\tK1\nExpected_2\tExpected_2\tK2\nExpected_3\tExpected_3\tK3\n')] + + filehandler.write.assert_has_calls(calls_expected) + + filehandler.close.assert_called_once() + + @mock.patch("wqflask.marker_regression.plink_mapping.TMPDIR", "/home/user/data/") + @mock.patch("wqflask.marker_regression.plink_mapping.build_line_list") + def test_parse_plink_output(self, mock_line_list): + chromosomes = [0, 34, 110, 89, 123, 23, 2] + species = AttributeSetter( + {"name": "S1", "chromosomes": AttributeSetter({"chromosomes": chromosomes})}) + + fake_file = """0 AACCAT T98.6 0.89\n2 AATA B45 0.3\n121 ACG B56.4 NA""" + + mock_line_list.side_effect = [["0", "AACCAT", "T98.6", "0.89"], [ + "2", "AATA", "B45", "0.3"], ["121", "ACG", "B56.4", "NA"]] + # print("sdfsfdf",species.chromosomes) + with mock.patch("builtins.open", mock.mock_open(read_data=fake_file)) as mock_open: + parse_results = parse_plink_output( + output_filename="P1_file", species=species) + mock_open.assert_called_once_with( + "/home/user/data/P1_file.qassoc", "rb") + expected = (2, {'AACCAT': 0.89, 'AATA': 0.3}) + + self.assertEqual(parse_results, expected) -- cgit v1.2.3 From 7aaa4b87fcf405c32c11e2e2d664941c43c14430 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 9 Nov 2020 13:20:53 +0300 Subject: refactor marker_regression/gemma-mapping.py run-gemma function to avoid Index Error --- wqflask/wqflask/marker_regression/gemma_mapping.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/wqflask/wqflask/marker_regression/gemma_mapping.py b/wqflask/wqflask/marker_regression/gemma_mapping.py index 68a8d5ba..02f91a32 100644 --- a/wqflask/wqflask/marker_regression/gemma_mapping.py +++ b/wqflask/wqflask/marker_regression/gemma_mapping.py @@ -31,16 +31,11 @@ def run_gemma(this_trait, this_dataset, samples, vals, covariates, use_loco, maf gwa_output_filename = this_dataset.group.name + "_GWA_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6)) this_chromosomes = this_dataset.species.chromosomes.chromosomes - chr_list_string = "" - for i in range(len(this_chromosomes)): - if i < (len(this_chromosomes) - 1): - chr_list_string += this_chromosomes[i+1].name + "," - else: - chr_list_string += this_chromosomes[i+1].name + this_chromosomes_name=[chromosome.name for chromosome in this_chromosomes] + chr_list_string=",".join(this_chromosomes_name) if covariates != "": gen_covariates_file(this_dataset, covariates, samples) - if use_loco == "True": generate_k_command = GEMMA_WRAPPER_COMMAND + ' --json --loco ' + chr_list_string + ' -- ' + GEMMAOPTS + ' -g %s/%s_geno.txt -p %s/gn2/%s.txt -a %s/%s_snps.txt -gk > %s/gn2/%s.json' % (flat_files('genotype/bimbam'), genofile_name, -- cgit v1.2.3 From 919763173289e523d5b3c9015fc6f1117ad59581 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 9 Nov 2020 13:31:42 +0300 Subject: add test for run_gemma function in marker_regression/gemma_mapping.py --- .../marker_regression/test_gemma_mapping.py | 70 +++++++++++++++++----- 1 file changed, 55 insertions(+), 15 deletions(-) diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py index 4a88509e..1b09afd6 100644 --- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py +++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py @@ -1,4 +1,4 @@ -#test for wqflask/marker_regression/gemma_mapping.py +# test for wqflask/marker_regression/gemma_mapping.py import unittest import random from unittest import mock @@ -24,22 +24,62 @@ class TestGemmaMapping(unittest.TestCase): # def test_fail(self): # self.assertEqual(2,3) - @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data") def setUp(self): - pass + pass - @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file") + @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img") + @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMAOPTS", "-debug") + @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMA_WRAPPER_COMMAND", "ghc") + @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data/") + @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output") + @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files") + @mock.patch("wqflask.marker_regression.gemma_mapping.gen_covariates_file") + @mock.patch("wqflask.marker_regression.gemma_mapping.string.ascii_uppercase", "R") + @mock.patch("wqflask.marker_regression.gemma_mapping.string.digits", "R") @mock.patch("wqflask.marker_regression.gemma_mapping.os") - def test_run_gemma_first_run_set_true(self, mock_gen_pheno, mock_os): - chromosomes = AttributeSetter({"chromosomes": "SA"}) - covariates = "XI:X2,X4:X3,X6:X7" - dataset_group = MockDatasetGroup({"genofile": "fileX"}) - dataset = AttributeSetter( - {"group": dataset_group, "name": "dataset1_name", "species": chromosomes}) + @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file") + def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os, mock_gen_covar, mock_flat_files, mock_parse_loco): + + chromosomes = [] + for i in range(1, 5): + chromosomes.append(AttributeSetter({"name": f"CH{i}"})) + + chromo = AttributeSetter({"chromosomes": chromosomes}) + + dataset_group = MockDatasetGroup( + {"name": "GP1", "genofile": "file_geno"}) + + dataset = AttributeSetter({"group": dataset_group, "name": "dataset1_name", + "species": AttributeSetter({"chromosomes": chromo})}) + trait = AttributeSetter({"name": "trait1"}) - mock_gen_pheno.side_effect = None - mock_gen_pheno.return_value = None + samples = [] + mock_gen_pheno_txt.side_effect = None + mock_gen_pheno_txt.return_value = None mock_os.path.isfile.return_value = True + mock_os.system.return_value = None + mock_os.system.side_effect = None + mock_gen_covar.side_effect = None + mock_gen_covar.return_value = None + mock_flat_files.return_value = "/home/genotype/bimbam" + mock_parse_loco.side_effect = None + mock_parse_loco.return_value = [] + results = run_gemma(this_trait=trait, this_dataset=dataset, samples=[ + ], vals=[], covariates="", use_loco=True) + # check results + self.assertEqual(results, ([], "GP1_GWA_RRRRRR")) + mock_gen_pheno_txt.assert_called_once() + self.assertEqual(mock_flat_files.call_count, 4) + + system_calls = [mock.call('ghc --json -- -debug -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt -a /home/genotype/bimbam/file_snps.txt -gk > /home/user/data//gn2/GP1_K_RRRRRR.json'), + mock.call('ghc --json --input /home/user/data//gn2/GP1_K_RRRRRR.json -- -debug -a /home/genotype/bimbam/file_snps.txt -lmm 2 -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt > /home/user/data//gn2/GP1_GWA_RRRRRR.json')] + + mock_os.system.assert_has_calls(system_calls) + + mock_os.path.isfile.assert_called_once_with(('/home/user/imgfile_output.assoc.txt') + ) + + mock_parse_loco.assert_called_once_with(dataset,"GP1_GWA_RRRRRR") @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output") def test_run_gemma_first_run_loco_set_false(self, mock_parse_loco): @@ -130,7 +170,8 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4 """ with mock.patch("builtins.open", mock.mock_open(read_data=file)) as mock_open: results = parse_gemma_output(genofile_name="gema_file") - expected = [{'name': ' gn2', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'gn2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'gn7', 'chr': 'X', 'Mb': 2.324424, 'p_value': 0.4, 'lod_score': 0.3979400086720376}, {'name': 'gn9', 'chr': 125, 'Mb': 0.433575, 'p_value': 0.67, 'lod_score': 0.17392519729917352}] + expected = [{'name': ' gn2', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'gn2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, + {'name': 'gn7', 'chr': 'X', 'Mb': 2.324424, 'p_value': 0.4, 'lod_score': 0.3979400086720376}, {'name': 'gn9', 'chr': 125, 'Mb': 0.433575, 'p_value': 0.67, 'lod_score': 0.17392519729917352}] mock_open.assert_called_once_with( "/home/user/img/gema_file_output.assoc.txt") @@ -146,7 +187,7 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4 @mock.patch("builtins.open", mock.mock_open(read_data="chr\t")) def test_parse_gemma_output_empty_return(self): - #duplicate + # duplicate string_read = parse_gemma_output(genofile_name="hdf") # print(string_read) @@ -156,7 +197,6 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4 mock_os.path.isfile.return_value = False file_to_write = """{"files":["file_1","file_2"]}""" - @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp") @mock.patch("wqflask.marker_regression.gemma_mapping.os") def test_parse_loco_output_file_not_found(self, mock_os): -- cgit v1.2.3 From 74c65eb99be3f9854bd774dae15877af7c8aa4f5 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 9 Nov 2020 14:03:08 +0300 Subject: mock logger in marker_regression/gemma_mapping.py --- .../marker_regression/test_gemma_mapping.py | 47 +++++++++++++--------- 1 file changed, 27 insertions(+), 20 deletions(-) diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py index 1b09afd6..fd45fd65 100644 --- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py +++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py @@ -27,18 +27,39 @@ class TestGemmaMapping(unittest.TestCase): def setUp(self): pass + + + @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output") + def test_run_gemma_first_run_loco_set_false(self, mock_parse_loco): + dataset = AttributeSetter( + {"group": AttributeSetter({"genofile": "genofile.geno"})}) + + output_files = "file1" + use_loco = False + mock_parse_loco.side_effect = None + mock_parse_loco.return_value = [] + this_trait = AttributeSetter({"name": "t1"}) + + result = run_gemma(this_trait=this_trait, this_dataset=dataset, samples=[], vals=[ + ], covariates="", use_loco=True, first_run=False, output_files=output_files) + + expected_results = ([], "file1") + self.assertEqual(expected_results, result) + + @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img") @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMAOPTS", "-debug") @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMA_WRAPPER_COMMAND", "ghc") @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data/") @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output") + @mock.patch("wqflask.marker_regression.gemma_mapping.logger") @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files") @mock.patch("wqflask.marker_regression.gemma_mapping.gen_covariates_file") @mock.patch("wqflask.marker_regression.gemma_mapping.string.ascii_uppercase", "R") @mock.patch("wqflask.marker_regression.gemma_mapping.string.digits", "R") @mock.patch("wqflask.marker_regression.gemma_mapping.os") @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file") - def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os, mock_gen_covar, mock_flat_files, mock_parse_loco): + def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os, mock_gen_covar, mock_flat_files,mock_logger,mock_parse_loco): chromosomes = [] for i in range(1, 5): @@ -71,32 +92,18 @@ class TestGemmaMapping(unittest.TestCase): mock_gen_pheno_txt.assert_called_once() self.assertEqual(mock_flat_files.call_count, 4) + system_calls = [mock.call('ghc --json -- -debug -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt -a /home/genotype/bimbam/file_snps.txt -gk > /home/user/data//gn2/GP1_K_RRRRRR.json'), mock.call('ghc --json --input /home/user/data//gn2/GP1_K_RRRRRR.json -- -debug -a /home/genotype/bimbam/file_snps.txt -lmm 2 -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt > /home/user/data//gn2/GP1_GWA_RRRRRR.json')] mock_os.system.assert_has_calls(system_calls) - mock_os.path.isfile.assert_called_once_with(('/home/user/imgfile_output.assoc.txt') - ) - - mock_parse_loco.assert_called_once_with(dataset,"GP1_GWA_RRRRRR") + mock_os.path.isfile.assert_called_once_with(('/home/user/imgfile_output.assoc.txt')) - @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output") - def test_run_gemma_first_run_loco_set_false(self, mock_parse_loco): - dataset = AttributeSetter( - {"group": AttributeSetter({"genofile": "genofile.geno"})}) + self.assertEqual(mock_logger.debug.call_count,2) + - output_files = "file1" - use_loco = False - mock_parse_loco.side_effect = None - mock_parse_loco.return_value = [] - this_trait = AttributeSetter({"name": "t1"}) - - result = run_gemma(this_trait=this_trait, this_dataset=dataset, samples=[], vals=[ - ], covariates="", use_loco=True, first_run=False, output_files=output_files) - - expected_results = ([], "file1") - self.assertEqual(expected_results, result) + mock_parse_loco.assert_called_once_with(dataset,"GP1_GWA_RRRRRR") @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data") def test_gen_pheno_txt_file(self): -- cgit v1.2.3 From 986b20363cc84be1822588dd7cc935fca7ef7f48 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 9 Nov 2020 16:53:12 +0300 Subject: add test for get_genofile_samplelist in marker_regression/run_mapping.py --- .../wqflask/marker_regression/test_run_mapping.py | 36 ++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 wqflask/tests/wqflask/marker_regression/test_run_mapping.py diff --git a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py new file mode 100644 index 00000000..0a5bc565 --- /dev/null +++ b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py @@ -0,0 +1,36 @@ +import unittest +from unittest import mock +from wqflask.marker_regression.run_mapping import get_genofile_samplelist + + +class AttributeSetter: + def __init__(self,obj): + for k,v in obj.items(): + setattr(self,k,v) + + +class MockDataSetGroup(AttributeSetter): + + def get_genofiles(self): + return [{"location":"~/genofiles/g1_file","sample_list":["S1","S2","S3","S4"]}] +class TestRunMapping(unittest.TestCase): + def setUp(self): + self.group=MockDataSetGroup({"genofile":"~/genofiles/g1_file"}) + self.dataset=AttributeSetter({"group":self.group}) + + def tearDown(self): + self.dataset=AttributeSetter({"group":{"location":"~/genofiles/g1_file"}}) + + + def test_get_genofile_samplelist(self): + #location true and sample list true + + results_1=get_genofile_samplelist(self.dataset) + self.assertEqual(results_1,["S1","S2","S3","S4"]) + #return empty array + self.group.genofile="~/genofiles/g2_file" + result_2=get_genofile_samplelist(self.dataset) + self.assertEqual(result_2,[]) + + + -- cgit v1.2.3 From e660984015e1bc6d48e9ccee5fe59691c4acd911 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 9 Nov 2020 17:22:27 +0300 Subject: add tests for geno_db_exists in marker_regression/run_mapping.py --- .../wqflask/marker_regression/test_run_mapping.py | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py index 0a5bc565..61bc8a1d 100644 --- a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py +++ b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py @@ -1,7 +1,7 @@ import unittest from unittest import mock from wqflask.marker_regression.run_mapping import get_genofile_samplelist - +from wqflask.marker_regression.run_mapping import geno_db_exists class AttributeSetter: def __init__(self,obj): @@ -15,7 +15,7 @@ class MockDataSetGroup(AttributeSetter): return [{"location":"~/genofiles/g1_file","sample_list":["S1","S2","S3","S4"]}] class TestRunMapping(unittest.TestCase): def setUp(self): - self.group=MockDataSetGroup({"genofile":"~/genofiles/g1_file"}) + self.group=MockDataSetGroup({"genofile":"~/genofiles/g1_file","name":"GP1_"}) self.dataset=AttributeSetter({"group":self.group}) def tearDown(self): @@ -32,5 +32,21 @@ class TestRunMapping(unittest.TestCase): result_2=get_genofile_samplelist(self.dataset) self.assertEqual(result_2,[]) + @mock.patch("wqflask.marker_regression.run_mapping.data_set") + def test_geno_db_exists(self,mock_data_set): + # mock_data_set.create_dataset_side_effect=None + mock_data_set.create_dataset.side_effect=[AttributeSetter({}),Exception()] + results_no_error=geno_db_exists(self.dataset) + results_with_error=geno_db_exists(self.dataset) + + self.assertEqual(mock_data_set.create_dataset.call_count,2) + self.assertEqual(results_with_error,"False") + self.assertEqual(results_no_error,"True") + + + + + + -- cgit v1.2.3 From c9c06e3e2df6d9290a7b65607dd819f68290712e Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 9 Nov 2020 20:10:21 +0300 Subject: remove unnecessary side_effect set to None --- .../marker_regression/test_gemma_mapping.py | 43 +++++----------------- 1 file changed, 9 insertions(+), 34 deletions(-) diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py index fd45fd65..963c131f 100644 --- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py +++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py @@ -21,13 +21,6 @@ class MockDatasetGroup(AttributeSetter): class TestGemmaMapping(unittest.TestCase): - # def test_fail(self): - # self.assertEqual(2,3) - - def setUp(self): - pass - - @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output") def test_run_gemma_first_run_loco_set_false(self, mock_parse_loco): @@ -36,7 +29,6 @@ class TestGemmaMapping(unittest.TestCase): output_files = "file1" use_loco = False - mock_parse_loco.side_effect = None mock_parse_loco.return_value = [] this_trait = AttributeSetter({"name": "t1"}) @@ -46,7 +38,6 @@ class TestGemmaMapping(unittest.TestCase): expected_results = ([], "file1") self.assertEqual(expected_results, result) - @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img") @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMAOPTS", "-debug") @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMA_WRAPPER_COMMAND", "ghc") @@ -59,7 +50,7 @@ class TestGemmaMapping(unittest.TestCase): @mock.patch("wqflask.marker_regression.gemma_mapping.string.digits", "R") @mock.patch("wqflask.marker_regression.gemma_mapping.os") @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file") - def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os, mock_gen_covar, mock_flat_files,mock_logger,mock_parse_loco): + def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco): chromosomes = [] for i in range(1, 5): @@ -75,35 +66,28 @@ class TestGemmaMapping(unittest.TestCase): trait = AttributeSetter({"name": "trait1"}) samples = [] - mock_gen_pheno_txt.side_effect = None + mock_gen_pheno_txt.return_value = None mock_os.path.isfile.return_value = True - mock_os.system.return_value = None - mock_os.system.side_effect = None - mock_gen_covar.side_effect = None mock_gen_covar.return_value = None mock_flat_files.return_value = "/home/genotype/bimbam" - mock_parse_loco.side_effect = None mock_parse_loco.return_value = [] results = run_gemma(this_trait=trait, this_dataset=dataset, samples=[ ], vals=[], covariates="", use_loco=True) - # check results self.assertEqual(results, ([], "GP1_GWA_RRRRRR")) mock_gen_pheno_txt.assert_called_once() self.assertEqual(mock_flat_files.call_count, 4) - - system_calls = [mock.call('ghc --json -- -debug -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt -a /home/genotype/bimbam/file_snps.txt -gk > /home/user/data//gn2/GP1_K_RRRRRR.json'), mock.call('ghc --json --input /home/user/data//gn2/GP1_K_RRRRRR.json -- -debug -a /home/genotype/bimbam/file_snps.txt -lmm 2 -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt > /home/user/data//gn2/GP1_GWA_RRRRRR.json')] mock_os.system.assert_has_calls(system_calls) - mock_os.path.isfile.assert_called_once_with(('/home/user/imgfile_output.assoc.txt')) + mock_os.path.isfile.assert_called_once_with( + ('/home/user/imgfile_output.assoc.txt')) - self.assertEqual(mock_logger.debug.call_count,2) - + self.assertEqual(mock_logger.debug.call_count, 2) - mock_parse_loco.assert_called_once_with(dataset,"GP1_GWA_RRRRRR") + mock_parse_loco.assert_called_once_with(dataset, "GP1_GWA_RRRRRR") @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data") def test_gen_pheno_txt_file(self): @@ -144,7 +128,6 @@ class TestGemmaMapping(unittest.TestCase): with mock.patch("builtins.open", mock.mock_open())as mock_open: gen_covariates_file(this_dataset=this_dataset, covariates=covariates, samples=["x1", "x2", "X3"]) - # test mocked methods create_dataset.assert_has_calls( [mock.call('X2'), mock.call('Y2'), mock.call('M3'), mock.call('V2')]) @@ -157,13 +140,10 @@ class TestGemmaMapping(unittest.TestCase): create_trait.assert_has_calls(mock_calls) - # test writing of covariates.txt - flat_files.assert_called_once_with('mapping') mock_open.assert_called_once_with( 'Home/Genenetwork/group_X_covariates.txt', 'w') filehandler = mock_open() - # expected all-9 filehandler.write.assert_has_calls([mock.call( '-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('\n')]) @@ -186,23 +166,18 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4 self.assertEqual(results, expected) @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img") - def test_xparse_gemma_output_empty_return(self): + def test_parse_gemma_output_empty_return(self): output_file_results = """chr\t today""" with mock.patch("builtins.open", mock.mock_open(read_data=output_file_results)) as mock_open: results = parse_gemma_output(genofile_name="gema_file") self.assertEqual(results, []) - @mock.patch("builtins.open", mock.mock_open(read_data="chr\t")) - def test_parse_gemma_output_empty_return(self): - # duplicate - string_read = parse_gemma_output(genofile_name="hdf") - # print(string_read) - @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp") @mock.patch("wqflask.marker_regression.gemma_mapping.os") def test_parse_loco_output_file_found(self, mock_os): - mock_os.path.isfile.return_value = False + mock_os.path.isfile.return_value = True file_to_write = """{"files":["file_1","file_2"]}""" + #incomplete @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp") @mock.patch("wqflask.marker_regression.gemma_mapping.os") -- cgit v1.2.3 From bf22cc3fbb37147b5e6c3012658fe5e43b696637 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 9 Nov 2020 20:26:21 +0300 Subject: remove whitespace in marker_regression --- wqflask/tests/wqflask/marker_regression/test_plink_mapping.py | 3 --- wqflask/tests/wqflask/marker_regression/test_run_mapping.py | 9 +-------- 2 files changed, 1 insertion(+), 11 deletions(-) diff --git a/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py b/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py index b827fd0f..a5fa0c04 100644 --- a/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py +++ b/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py @@ -12,8 +12,6 @@ class AttributeSetter: def __init__(self, obj): for key, val in obj.items(): setattr(self, key, val) - - class TestPlinkMapping(unittest.TestCase): def test_build_line_list(self): @@ -72,7 +70,6 @@ Expected_2\there mock_line_list.side_effect = [["0", "AACCAT", "T98.6", "0.89"], [ "2", "AATA", "B45", "0.3"], ["121", "ACG", "B56.4", "NA"]] - # print("sdfsfdf",species.chromosomes) with mock.patch("builtins.open", mock.mock_open(read_data=fake_file)) as mock_open: parse_results = parse_plink_output( output_filename="P1_file", species=species) diff --git a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py index 61bc8a1d..bba9de05 100644 --- a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py +++ b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py @@ -2,6 +2,7 @@ import unittest from unittest import mock from wqflask.marker_regression.run_mapping import get_genofile_samplelist from wqflask.marker_regression.run_mapping import geno_db_exists +from wqflask.marker_regression.run_mapping import write_input_for_browser class AttributeSetter: def __init__(self,obj): @@ -21,20 +22,16 @@ class TestRunMapping(unittest.TestCase): def tearDown(self): self.dataset=AttributeSetter({"group":{"location":"~/genofiles/g1_file"}}) - def test_get_genofile_samplelist(self): - #location true and sample list true results_1=get_genofile_samplelist(self.dataset) self.assertEqual(results_1,["S1","S2","S3","S4"]) - #return empty array self.group.genofile="~/genofiles/g2_file" result_2=get_genofile_samplelist(self.dataset) self.assertEqual(result_2,[]) @mock.patch("wqflask.marker_regression.run_mapping.data_set") def test_geno_db_exists(self,mock_data_set): - # mock_data_set.create_dataset_side_effect=None mock_data_set.create_dataset.side_effect=[AttributeSetter({}),Exception()] results_no_error=geno_db_exists(self.dataset) results_with_error=geno_db_exists(self.dataset) @@ -46,7 +43,3 @@ class TestRunMapping(unittest.TestCase): - - - - -- cgit v1.2.3 From 3ccc964fc466467c1c65f000fa7b43a38e10987a Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 9 Nov 2020 21:15:24 +0300 Subject: refactor test_run_gemma function --- wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py index 963c131f..bc0cb4e0 100644 --- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py +++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py @@ -46,11 +46,10 @@ class TestGemmaMapping(unittest.TestCase): @mock.patch("wqflask.marker_regression.gemma_mapping.logger") @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files") @mock.patch("wqflask.marker_regression.gemma_mapping.gen_covariates_file") - @mock.patch("wqflask.marker_regression.gemma_mapping.string.ascii_uppercase", "R") - @mock.patch("wqflask.marker_regression.gemma_mapping.string.digits", "R") + @mock.patch("wqflask.marker_regression.run_mapping.random.choice") @mock.patch("wqflask.marker_regression.gemma_mapping.os") @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file") - def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco): + def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os,mock_choice,mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco): chromosomes = [] for i in range(1, 5): @@ -70,6 +69,7 @@ class TestGemmaMapping(unittest.TestCase): mock_gen_pheno_txt.return_value = None mock_os.path.isfile.return_value = True mock_gen_covar.return_value = None + mock_choice.return_value="R" mock_flat_files.return_value = "/home/genotype/bimbam" mock_parse_loco.return_value = [] results = run_gemma(this_trait=trait, this_dataset=dataset, samples=[ @@ -177,7 +177,7 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4 def test_parse_loco_output_file_found(self, mock_os): mock_os.path.isfile.return_value = True file_to_write = """{"files":["file_1","file_2"]}""" - #incomplete + # incomplete @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp") @mock.patch("wqflask.marker_regression.gemma_mapping.os") -- cgit v1.2.3 From ba065fa885841dc39681d0e420ac06df1ea846ae Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Mon, 9 Nov 2020 21:27:54 +0300 Subject: correct typo and variable naming --- .../marker_regression/test_gemma_mapping.py | 30 +++++----------------- 1 file changed, 7 insertions(+), 23 deletions(-) diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py index bc0cb4e0..06ce55d8 100644 --- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py +++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py @@ -49,52 +49,41 @@ class TestGemmaMapping(unittest.TestCase): @mock.patch("wqflask.marker_regression.run_mapping.random.choice") @mock.patch("wqflask.marker_regression.gemma_mapping.os") @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file") - def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os,mock_choice,mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco): - + def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco): chromosomes = [] for i in range(1, 5): chromosomes.append(AttributeSetter({"name": f"CH{i}"})) - chromo = AttributeSetter({"chromosomes": chromosomes}) - dataset_group = MockDatasetGroup( {"name": "GP1", "genofile": "file_geno"}) - dataset = AttributeSetter({"group": dataset_group, "name": "dataset1_name", "species": AttributeSetter({"chromosomes": chromo})}) - trait = AttributeSetter({"name": "trait1"}) samples = [] - mock_gen_pheno_txt.return_value = None mock_os.path.isfile.return_value = True mock_gen_covar.return_value = None - mock_choice.return_value="R" + mock_choice.return_value = "R" mock_flat_files.return_value = "/home/genotype/bimbam" mock_parse_loco.return_value = [] results = run_gemma(this_trait=trait, this_dataset=dataset, samples=[ ], vals=[], covariates="", use_loco=True) - self.assertEqual(results, ([], "GP1_GWA_RRRRRR")) - mock_gen_pheno_txt.assert_called_once() - self.assertEqual(mock_flat_files.call_count, 4) system_calls = [mock.call('ghc --json -- -debug -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt -a /home/genotype/bimbam/file_snps.txt -gk > /home/user/data//gn2/GP1_K_RRRRRR.json'), mock.call('ghc --json --input /home/user/data//gn2/GP1_K_RRRRRR.json -- -debug -a /home/genotype/bimbam/file_snps.txt -lmm 2 -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt > /home/user/data//gn2/GP1_GWA_RRRRRR.json')] - mock_os.system.assert_has_calls(system_calls) - + mock_gen_pheno_txt.assert_called_once() + mock_parse_loco.assert_called_once_with(dataset, "GP1_GWA_RRRRRR") mock_os.path.isfile.assert_called_once_with( ('/home/user/imgfile_output.assoc.txt')) - self.assertEqual(mock_logger.debug.call_count, 2) - - mock_parse_loco.assert_called_once_with(dataset, "GP1_GWA_RRRRRR") + self.assertEqual(mock_flat_files.call_count, 4) + self.assertEqual(results, ([], "GP1_GWA_RRRRRR")) @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data") def test_gen_pheno_txt_file(self): with mock.patch("builtins.open", mock.mock_open())as mock_open: gen_pheno_txt_file(this_dataset={}, genofile_name="", vals=[ "x", "w", "q", "we", "R"], trait_filename="fitr.re") - mock_open.assert_called_once_with( '/home/user/data/gn2/fitr.re.txt', 'w') filehandler = mock_open() @@ -159,26 +148,21 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4 results = parse_gemma_output(genofile_name="gema_file") expected = [{'name': ' gn2', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'gn2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'gn7', 'chr': 'X', 'Mb': 2.324424, 'p_value': 0.4, 'lod_score': 0.3979400086720376}, {'name': 'gn9', 'chr': 125, 'Mb': 0.433575, 'p_value': 0.67, 'lod_score': 0.17392519729917352}] - mock_open.assert_called_once_with( "/home/user/img/gema_file_output.assoc.txt") - self.assertEqual(results, expected) - @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img") def test_parse_gemma_output_empty_return(self): output_file_results = """chr\t today""" with mock.patch("builtins.open", mock.mock_open(read_data=output_file_results)) as mock_open: results = parse_gemma_output(genofile_name="gema_file") self.assertEqual(results, []) - @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp") @mock.patch("wqflask.marker_regression.gemma_mapping.os") def test_parse_loco_output_file_found(self, mock_os): mock_os.path.isfile.return_value = True file_to_write = """{"files":["file_1","file_2"]}""" - # incomplete - + pass @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp") @mock.patch("wqflask.marker_regression.gemma_mapping.os") def test_parse_loco_output_file_not_found(self, mock_os): -- cgit v1.2.3 From 137dff191dd8b78e8207e6c5adc4e1c421a75ff0 Mon Sep 17 00:00:00 2001 From: Alexanderlacuna Date: Tue, 10 Nov 2020 13:57:24 +0300 Subject: add tests for export_mapping_results in marker_regression/run_mapping.py --- .../wqflask/marker_regression/test_run_mapping.py | 192 ++++++++++++++++++--- 1 file changed, 167 insertions(+), 25 deletions(-) diff --git a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py index bba9de05..e925ad28 100644 --- a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py +++ b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py @@ -1,45 +1,187 @@ import unittest +import datetime from unittest import mock + from wqflask.marker_regression.run_mapping import get_genofile_samplelist from wqflask.marker_regression.run_mapping import geno_db_exists from wqflask.marker_regression.run_mapping import write_input_for_browser +from wqflask.marker_regression.run_mapping import export_mapping_results +from wqflask.marker_regression.run_mapping import trim_markers_for_figure + class AttributeSetter: - def __init__(self,obj): - for k,v in obj.items(): - setattr(self,k,v) + def __init__(self, obj): + for k, v in obj.items(): + setattr(self, k, v) class MockDataSetGroup(AttributeSetter): - - def get_genofiles(self): - return [{"location":"~/genofiles/g1_file","sample_list":["S1","S2","S3","S4"]}] + + def get_genofiles(self): + return [{"location": "~/genofiles/g1_file", "sample_list": ["S1", "S2", "S3", "S4"]}] + + class TestRunMapping(unittest.TestCase): - def setUp(self): - self.group=MockDataSetGroup({"genofile":"~/genofiles/g1_file","name":"GP1_"}) - self.dataset=AttributeSetter({"group":self.group}) + def setUp(self): + self.group = MockDataSetGroup( + {"genofile": "~/genofiles/g1_file", "name": "GP1_","species":"Human"}) + self.dataset = AttributeSetter({"fullname":"dataser_1","group": self.group,"type":"ProbeSet"}) + self.trait=AttributeSetter({"symbol":"IGFI","chr":"X1","mb":123313}) + + def tearDown(self): + self.dataset = AttributeSetter( + {"group": {"location": "~/genofiles/g1_file"}}) + + def test_get_genofile_samplelist(self): + + results_1 = get_genofile_samplelist(self.dataset) + self.assertEqual(results_1, ["S1", "S2", "S3", "S4"]) + self.group.genofile = "~/genofiles/g2_file" + result_2 = get_genofile_samplelist(self.dataset) + self.assertEqual(result_2, []) + + @mock.patch("wqflask.marker_regression.run_mapping.data_set") + def test_geno_db_exists(self, mock_data_set): + mock_data_set.create_dataset.side_effect = [ + AttributeSetter({}), Exception()] + results_no_error = geno_db_exists(self.dataset) + results_with_error = geno_db_exists(self.dataset) + + self.assertEqual(mock_data_set.create_dataset.call_count, 2) + self.assertEqual(results_with_error, "False") + self.assertEqual(results_no_error, "True") + + + def test_trim_markers_for_figure(self): - def tearDown(self): - self.dataset=AttributeSetter({"group":{"location":"~/genofiles/g1_file"}}) - def test_get_genofile_samplelist(self): + markers=[{ + "name":"MK1", + "chr":"C1", + "cM":"1", + "Mb":"12000", + "genotypes":[], + "dominance":"TT", + "additive":"VA", + "lod_score":0.5 + }, + { + "name":"MK2", + "chr":"C2", + "cM":"15", + "Mb":"10000", + "genotypes":[], + "lod_score":0.7 + }, + { + "name":"MK1", + "chr":"C3", + "cM":"45", + "Mb":"1", + "genotypes":[], + "dominance":"Tt", + "additive":"VE", + "lod_score":1 + }] - results_1=get_genofile_samplelist(self.dataset) - self.assertEqual(results_1,["S1","S2","S3","S4"]) - self.group.genofile="~/genofiles/g2_file" - result_2=get_genofile_samplelist(self.dataset) - self.assertEqual(result_2,[]) + marker_2=[{ + "name":"MK1", + "chr":"C1", + "cM":"1", + "Mb":"12000", + "genotypes":[], + "dominance":"TT", + "additive":"VA", + "p_wald":4.6 + }] + results=trim_markers_for_figure(markers) + result_2=trim_markers_for_figure(marker_2) + expected=[ + { + "name":"MK1", + "chr":"C1", + "cM":"1", + "Mb":"12000", + "genotypes":[], + "dominance":"TT", + "additive":"VA", + "lod_score":0.5 + }, + { + "name":"MK1", + "chr":"C3", + "cM":"45", + "Mb":"1", + "genotypes":[], + "dominance":"Tt", + "additive":"VE", + "lod_score":1 + } - @mock.patch("wqflask.marker_regression.run_mapping.data_set") - def test_geno_db_exists(self,mock_data_set): - mock_data_set.create_dataset.side_effect=[AttributeSetter({}),Exception()] - results_no_error=geno_db_exists(self.dataset) - results_with_error=geno_db_exists(self.dataset) + ] + self.assertEqual(results,expected) + self.assertEqual(result_2,marker_2) - self.assertEqual(mock_data_set.create_dataset.call_count,2) - self.assertEqual(results_with_error,"False") - self.assertEqual(results_no_error,"True") + def test_export_mapping_results(self): + datetime_mock=mock.Mock(wraps=datetime.datetime) + datetime_mock.now.return_value=datetime.datetime(2019,9,1,10,12,12) + markers=[{ + "name":"MK1", + "chr":"C1", + "cM":"1", + "Mb":"12000", + "genotypes":[], + "dominance":"TT", + "additive":"VA", + "lod_score":3 + }, + { + "name":"MK2", + "chr":"C2", + "cM":"15", + "Mb":"10000", + "genotypes":[], + "lod_score":7 + }, + { + "name":"MK1", + "chr":"C3", + "cM":"45", + "Mb":"1", + "genotypes":[], + "dominance":"Tt", + "additive":"VE", + "lod_score":7 + }] + with mock.patch("builtins.open", mock.mock_open()) as mock_open: + + # mock_open.assert_called_once_with("~/results","w+") + # filehandler=mock_open() + with mock.patch("wqflask.marker_regression.run_mapping.datetime.datetime",new=datetime_mock): + export_mapping_results(dataset=self.dataset,trait=self.trait,markers=markers,results_path="~/results",mapping_scale="physic",score_type="-log(p)") + + write_calls=[ + mock.call('Time/Date: 09/01/19 / 10:12:12\n'), + mock.call('Population: Human GP1_\n'),mock.call('Data Set: dataser_1\n'), + mock.call('Gene Symbol: IGFI\n'), mock.call('Location: X1 @ 123313 Mb\n'), + mock.call('\n'), mock.call('Name,Chr,'), + mock.call('Mb,-log(p)'), mock.call('Cm,-log(p)'), + mock.call(',Additive'),mock.call(',Dominance'), + mock.call('\n'),mock.call('MK1,C1,'), + mock.call('12000,'), mock.call('1,'), + mock.call('3'), mock.call(',VA'), + mock.call(',TT'),mock.call('\n'), + mock.call('MK2,C2,'),mock.call('10000,'), + mock.call('15,'), mock.call('7'), + mock.call('\n'), mock.call('MK1,C3,'), + mock.call('1,'),mock.call('45,'), + mock.call('7'), mock.call(',VE'), + mock.call(',Tt') + ] + mock_open.assert_called_once_with("~/results","w+") + filehandler=mock_open() + filehandler.write.assert_has_calls(write_calls) \ No newline at end of file -- cgit v1.2.3 From 74ba91ade4ed0a875abf7417c33249f42f7c367e Mon Sep 17 00:00:00 2001 From: BonfaceKilz Date: Tue, 10 Nov 2020 16:48:14 +0300 Subject: Add missing links and some markdown formatting(underline, bold) * wqflask/wqflask/static/markdown/glossary.md: Update file. --- wqflask/wqflask/static/markdown/glossary.md | 154 +++++++++++++++------------- 1 file changed, 80 insertions(+), 74 deletions(-) diff --git a/wqflask/wqflask/static/markdown/glossary.md b/wqflask/wqflask/static/markdown/glossary.md index d2c80b40..68276796 100644 --- a/wqflask/wqflask/static/markdown/glossary.md +++ b/wqflask/wqflask/static/markdown/glossary.md @@ -21,11 +21,11 @@ The additive allele effect is an estimate of the change in the average phenotype [(mean of AA cases)-(mean of aa cases)]/2 -GeneNetwork displays the additive values on the far right of many trait/QTL maps, usually as red or green lines along the maps. The units of measurement of additive effects (and dominance effects) are defined by the trait itself and are shown in Trait Data and Analysis windows. For mRNA estimates these units are usually normalized log2 expression values. For this reason an additive effect of 0.5 units indicates that the A/A and a/a genotypes at a particular locus or marker differ by 1 unit (twice the effect of swapping a single A allele for an a allele). On this log2 scale this is equivalent to a 2-fold difference (2 raised to the power of 1). +GeneNetwork displays the additive values on the far right of many trait/QTL maps, usually as red or green lines along the maps. The units of measurement of additive effects (and dominance effects) are defined by the trait itself and are shown in **Trait Data and Analysis** windows. For mRNA estimates these units are usually normalized log2 expression values. For this reason an additive effect of 0.5 units indicates that the A/A and a/a genotypes at a particular locus or marker differ by 1 unit (twice the effect of swapping a single A allele for an a allele). On this log2 scale this is equivalent to a 2-fold difference (2 raised to the power of 1). On the QTL map plots the polarity of allele effects is represented by the color of the line. For example, in mouse BXD family maps, if the DBA/2J allele produces higher values than the C57BL/6J allele then the additive effect line is colored in green. In contrast, if the C57BL/6J allele produces higher values then the line is colored in red. For computational purposes, C57BL/6J red values are considered negative. -The dominance effects of alleles are also computed on maps for F2 populations (e.g., B6D2F2 and B6BTBRF2). Orange and purple line colors are used to distinguish the polarity of effects. Purple is the positive dominance effect that matches the polarity of the green additive effect, whereas orange is the negative dominance effect that matches the polarity of the red additive effect. [Please also see entry on Dominance Effects: Williams RW, Oct 15, 2004; Sept 3, 2005; Dec 4, 2005; Oct 25, 2011] +The dominance effects of alleles are also computed on maps for F2 populations (e.g., B6D2F2 and B6BTBRF2). Orange and purple line colors are used to distinguish the polarity of effects. Purple is the positive dominance effect that matches the polarity of the green additive effect, whereas orange is the negative dominance effect that matches the polarity of the red additive effect. [Please also see entry on **Dominance Effects**: Williams RW, Oct 15, 2004; Sept 3, 2005; Dec 4, 2005; Oct 25, 2011] [Go back to index](#index) @@ -35,15 +35,15 @@ The dominance effects of alleles are also computed on maps for F2 populations (e #### Bootstrap: -A bootstrap sample is a randomly drawn sample (or resample) that is taken from the original data set and that has the same number of samples as the original data set. In a single bootstrap sample, some cases will by chance be represented one or more times; other cases may not be represented at all (in other words, the sampling is done "with replacement" after each selection). To get a better intuitive feel for the method, imagine a bag of 26 Scrabble pieces that contain each letter of the English alphabet. In a bootstrap sample of these 26 pieces, you would shake the bag, insert your hand, and draw out one piece. You would then write down that letter on a piece of paper, and the place that Scrabble piece back in the bag in preparation for the next random selection. You would repeat this process (shake, draw, replace) 25 more times to generate a single bootstrap resample of the alphabet. Some letters will be represented several time in each sample and others will not be represented at al. If you repeat this procedure 1000 times you would have a set of bootstrap resamples of the type that GN uses to remap data sets. +A [bootstrap sample](http://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29) is a randomly drawn sample (or resample) that is taken from the original data set and that has the same number of samples as the original data set. In a single bootstrap sample, some cases will by chance be represented one or more times; other cases may not be represented at all (in other words, the sampling is done "with replacement" after each selection). To get a better intuitive feel for the method, imagine a bag of 26 Scrabble pieces that contain each letter of the English alphabet. In a bootstrap sample of these 26 pieces, you would shake the bag, insert your hand, and draw out one piece. You would then write down that letter on a piece of paper, and the place that Scrabble piece back in the bag in preparation for the next random selection. You would repeat this process (shake, draw, replace) 25 more times to generate a single bootstrap resample of the alphabet. Some letters will be represented several time in each sample and others will not be represented at al. If you repeat this procedure 1000 times you would have a set of bootstrap resamples of the type that GN uses to remap data sets. -Bootstrap resampling is a method that can be used to estimate statistical parameters and error terms. GeneNetwork uses a bootstrap procedure to evaluate approximate confidence limits of QTL peaks using a method proposed by Peter Visscher and colleagues (1996). We generate 2000 bootstraps, remap each, and keep track of the location of the single locus with the highest LRS score locations (equivalent to a "letter" in the Scrabble example). The 2000 "best" locations are used to produce the yellow histograms plotted on some of the QTL maps. If the position of a QTL is firm, then the particular composition of the sample, will not shift the position of the QTL peak by very much. In such a case, the histogram of "best QTLs" (yellow bars in the maps) that is displayed in WebQTL maps will tend to have a sharp peak (the scale is the percentage of bootstrap resamples that fall into each bar of the bootstrap histogram). In contrast, if the the yellow bootstrap histograms are spread out along a chromosome, then the precise location of a QTL may not be accurate, even in the original correct data set. Bootstrap results naturally vary between runs due to the random generation of the samples. See the related entry "Frequency of Peak LRS." +Bootstrap resampling is a method that can be used to estimate statistical parameters and error terms. GeneNetwork uses a bootstrap procedure to evaluate approximate confidence limits of QTL peaks using a method proposed by Peter Visscher and colleagues ([1996](http://www.genetics.org/content/143/2/1013.full.pdf)). We generate 2000 bootstraps, remap each, and keep track of the location of the single locus with the highest LRS score locations (equivalent to a "letter" in the Scrabble example). The 2000 "best" locations are used to produce the yellow histograms plotted on some of the QTL maps. If the position of a QTL is firm, then the particular composition of the sample, will not shift the position of the QTL peak by very much. In such a case, the histogram of "best QTLs" (yellow bars in the maps) that is displayed in WebQTL maps will tend to have a sharp peak (the scale is the percentage of bootstrap resamples that fall into each bar of the bootstrap histogram). In contrast, if the the yellow bootstrap histograms are spread out along a chromosome, then the precise location of a QTL may not be accurate, even in the original correct data set. Bootstrap results naturally vary between runs due to the random generation of the samples. See the related entry "Frequency of Peak LRS." -KNOWN PROBLEMS and INTERPRETATION of BOOTSTRAP RESULTS: The reliability of bootstrap analysis of QTL confidence intervals has been criticized by Manichaikul and colleagues (2006). Their work applies in particular to standard intercrosses and backcrosses in which markers are spaced every 2 cM. They recommend that confidence intervals be estimated either by a conventional 1.5 to 2.0 LOD drop-off interval or by a Bayes credible Interval method. +KNOWN PROBLEMS and INTERPRETATION of BOOTSTRAP RESULTS: The reliability of bootstrap analysis of QTL confidence intervals has been criticized by Manichaikul and colleagues ([2006](http://www.genetics.org/cgi/content/full/174/1/481)). Their work applies in particular to standard intercrosses and backcrosses in which markers are spaced every 2 cM. They recommend that confidence intervals be estimated either by a conventional 1.5 to 2.0 LOD drop-off interval or by a Bayes credible Interval method. There is a known flaw in the way in which GeneNetwork displays bootstrap results (Sept 2011). If a map has two or more adjacent markers with identical LOD score and identical strain distribution patterns, all of the bootstrap results are assigned incorrectly to just one of the "twin" markers. This results in a false perception of precision. -QTL mapping methods can be highly sensitive to cases with very high or very low phenotype values (outliers). The bootstrap method does not provide protection against the effects of outliers and their effects on QTL maps. Make sure you review your data for outliers before mapping. Options include (1) Do nothing, (2) Delete the outliers and see what happens to your maps, (3) Winsorize the values of the outliers. You might try all three options and determine if your main results are stable or not. With small samples or extreme outliers, you may find the mapping results to be quite volatile. In general, if the results (QTL position or value) depend highly on one or two outliers (5-10% of the samples) then you should probably delete or winsorize the outliers. [Williams RW, Oct 15, 2004, Mar 15, 2008, Mar 26, 2008; Sept 2011] +QTL mapping methods can be highly sensitive to cases with very high or very low phenotype values (outliers). The bootstrap method does not provide protection against the effects of outliers and their effects on QTL maps. Make sure you review your data for outliers before mapping. Options include (1) Do nothing, (2) Delete the outliers and see what happens to your maps, (3) [Winsorize](http://en.wikipedia.org/wiki/Winsorising) the values of the outliers. You might try all three options and determine if your main results are stable or not. With small samples or extreme outliers, you may find the mapping results to be quite volatile. In general, if the results (QTL position or value) depend highly on one or two outliers (5-10% of the samples) then you should probably delete or winsorize the outliers. [Williams RW, Oct 15, 2004, Mar 15, 2008, Mar 26, 2008; Sept 2011] [Go back to index](#index) @@ -59,7 +59,7 @@ Cluster maps are sets of QTL maps for a group of traits. The QTL maps for the in #### Collections and Trait Collections: -One of the most powerful features of GeneNetwork (GN) is the ability to study large sets of traits that have been measured using a common genetic reference population or panel (GRP). This is one of the key requirements of systems genetics--many traits studied in common. Under the main GN menu Search heading you will see a link to Trait Collections. You can assemble you own collection for any GRP by simply adding items using the Add to Collection button that you will find in many windows. Once you have a collection you will have access to a new set of tools for analysis of your collection, including QTL Cluster Map, Network Graph, Correlation Matrix, and Compare Correlates. [Williams RW, April 7, 2006] +One of the most powerful features of GeneNetwork (GN) is the ability to study large sets of traits that have been measured using a common genetic reference population or panel (GRP). This is one of the key requirements of systems genetics--many traits studied in common. Under the main GN menu **Search** heading you will see a link to **Trait Collections**. You can assemble you own collection for any GRP by simply adding items using the Add to Collection button that you will find in many windows. Once you have a collection you will have access to a new set of tools for analysis of your collection, including **QTL Cluster Map, Network Graph, Correlation Matrix**, and **Compare Correlates**. [Williams RW, April 7, 2006] #### Complex Trait Analysis: @@ -67,13 +67,13 @@ Complex trait analysis is the study of multiple causes of variation of phenotype #### Composite Interval Mapping: -Composite interval mapping is a method of mapping chromosomal regions that controls for some fraction of the genetic variability in a quantitative trait. Unlike simple interval mapping, composite interval mapping usually controls for variation produced at one or more background marker loci. These background markers are generally chosen because they are already known to be close to the location of a significant QTL. By factoring out a portion of the genetic variance produced by a major QTL, one can occasionally detect secondary QTLs. WebQTL allows users to control for a single background marker. To select this marker, first run the Marker Regression analysis (and if necessary, check the box labeled display all LRS, select the appropriate locus, and the click on either Composite Interval Mapping or Composite Regression. A more powerful and effective alternative to composite interval mapping is pair-scan analysis. This latter method takes into accounts (models) both the independent effects of two loci and possible two-locus epistatic interactions. [Williams RW, Dec 20, 2004] +Composite interval mapping is a method of mapping chromosomal regions that controls for some fraction of the genetic variability in a quantitative trait. Unlike simple interval mapping, composite interval mapping usually controls for variation produced at one or more background marker loci. These background markers are generally chosen because they are already known to be close to the location of a significant QTL. By factoring out a portion of the genetic variance produced by a major QTL, one can occasionally detect secondary QTLs. WebQTL allows users to control for a single background marker. To select this marker, first run the **Marker Regression** analysis (and if necessary, check the box labeled display all LRS, select the appropriate locus, and the click on either **Composite Interval Mapping** or **Composite Regression**. A more powerful and effective alternative to composite interval mapping is pair-scan analysis. This latter method takes into accounts (models) both the independent effects of two loci and possible two-locus epistatic interactions. [Williams RW, Dec 20, 2004]
    #### Correlations: Pearson and Spearman: -GeneNetwork provides tools to compute both Pearson product-moment correlations (the standard type of correlation), Spearman rank order correlations. Wikipedia and introductory statistics text will have a discussion of these major types of correlation. The quick advice is to use the more robust Spearman rank order correlation if the number of pairs of observations in a data set is less than about 30 and to use the more powerful but much more sensitive Pearson product-moment correlation when the number of observations is greater than 30 AND after you have dealt with any outliers. GeneNetwork automatically flags outliers for you in the Trait Data and Analysis form. GeneNetwork also allows you to modify values by either deleting or winsorising them. That means that you can use Pearson correlations even with smaller sample sizes after making sure that data are well distributed. Be sure to view the scatterplots associated with correlation values (just click on the value to generate a plot). Look for bivariate outliers. +GeneNetwork provides tools to compute both Pearson product-moment correlations (the standard type of correlation), Spearman rank order correlations. [Wikipedia](http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient) and introductory statistics text will have a discussion of these major types of correlation. The quick advice is to use the more robust Spearman rank order correlation if the number of pairs of observations in a data set is less than about 30 and to use the more powerful but much more sensitive Pearson product-moment correlation when the number of observations is greater than 30 AND after you have dealt with any outliers. GeneNetwork automatically flags outliers for you in the Trait Data and Analysis form. GeneNetwork also allows you to modify values by either deleting or winsorising them. That means that you can use Pearson correlations even with smaller sample sizes after making sure that data are well distributed. Be sure to view the scatterplots associated with correlation values (just click on the value to generate a plot). Look for bivariate outliers. #### Cross: @@ -97,7 +97,7 @@ Note that dominance deviations cannot be computed from a set of recombinant inbr #### Epistasis: -Epistasis means that combined effects of two or more different loci or polymorphic genes are not what one would expect given the addition of their individual effects. There is, in other words, evidence for non-linear interactions among two or more loci. This is similar to the dominance effects mentioned above, but now generalized to two or more distinct loci, rather than to two or more alleles at a single locus. For example, if QTL 1 has an A allele that has an additive effects of +5 and QTL 2 has an A alleles that has an additive effect of +2, then the two locus genotype combination A/A would be expected to boost the mean by +7 units. But if the value of these A/A individuals was actually -7 we would be quite surprised and would refer to this as an epistatic interaction between QTL 1 and QTL 2. WebQTL will search for all possible epistatic interactions between pairs of loci in the genome. This function is called a Pair Scan becasue the software analyzes the LRS score for all possible pairs of loci. Instead of viewing an LRS plot along a single dimension, we now view a two-dimensional plot that shows a field of LRS scores computed for pairs of loci. Pair scan plots are extremely sensitive to outlier data. Be sure to review the primary data carefully using Basic Statistics. Also note that this more sophisiticated method also demands a significantly larger sample size. While 25 to 50 cases may be adequate for a conventional LRS plot (sometimes called a "main scan"), a Pair-Scan is hard to apply safely with fewer than 60 cases. [Williams RW, Dec 21, 2004; Dec 5, 2005] +Epistasis means that combined effects of two or more different loci or polymorphic genes are not what one would expect given the addition of their individual effects. There is, in other words, evidence for non-linear interactions among two or more loci. This is similar to the dominance effects mentioned above, but now generalized to two or more distinct loci, rather than to two or more alleles at a single locus. For example, if QTL 1 has an A allele that has an additive effects of +5 and QTL 2 has an A alleles that has an additive effect of +2, then the two locus genotype combination A/A would be expected to boost the mean by +7 units. But if the value of these A/A individuals was actually -7 we would be quite surprised and would refer to this as an epistatic interaction between QTL 1 and QTL 2. WebQTL will search for all possible epistatic interactions between pairs of loci in the genome. This function is called a **Pair Scan** becasue the software analyzes the LRS score for all possible pairs of loci. Instead of viewing an LRS plot along a single dimension, we now view a two-dimensional plot that shows a field of LRS scores computed for pairs of loci. Pair scan plots are extremely sensitive to outlier data. Be sure to review the primary data carefully using **Basic Statistics**. Also note that this more sophisiticated method also demands a significantly larger sample size. While 25 to 50 cases may be adequate for a conventional LRS plot (sometimes called a "main scan"), a **Pair-Scan** is hard to apply safely with fewer than 60 cases. [Williams RW, Dec 21, 2004; Dec 5, 2005] #### Effect Size of a QTL: @@ -108,19 +108,19 @@ Please note that the functional importance of a locus, QTL, or GWAS hit can not Estimates of effect size for families of inbred lines, such as the BXD, HXB, CC, and hybrid diversity panels (e.g. the hybrid mouse diversity panel and the hybrid rat diversity panel) are typically (and correctly) much higher than those measured in otherwise similar analysis of intercrosses, heterogeneous stock (HS), or diversity outbred stock. Two factors contribute to the much higher level of explained variance of QTLs when using inbred strain panels. -1. *Replication Rate:* The variance that can be explained by a locus is increased by sampling multiple cases that have identical genomes and by using the strain mean for genetic analysis. Increasing replication rates from 1 to 6 can easily double the apparent heritability of a trait and therefore the effect size of a locus. The reason is simple—resampling decrease the standard error of mean, boosting the effective heritability (see Glossary entry on Heritability and focus on figure 1 from the Belknap 1998 paper reproduced below). Compare the genetically explained variance (labeled h2RI in this figure) of a single case (no replication) on the x-axis with the function at a replication rate of 4 on the y-axis. If the explained variance is 0.1 (10% of all variance explained) then the value is boosted to 0.3 (30% of strain mean variance explained) with n = 4. +1. **Replication Rate:** The variance that can be explained by a locus is increased by sampling multiple cases that have identical genomes and by using the strain mean for genetic analysis. Increasing replication rates from 1 to 6 can easily double the apparent heritability of a trait and therefore the effect size of a locus. The reason is simple—resampling decrease the standard error of mean, boosting the effective heritability (see Glossary entry on *Heritability* and focus on figure 1 from the Belknap [1998](http://gn1.genenetwork.org/images/upload/Belknap_Heritability_1998.pdf) paper reproduced below).
    Compare the genetically explained variance (labeled h2RI in this figure) of a single case (no replication) on the x-axis with the function at a replication rate of 4 on the y-axis. If the explained variance is 0.1 (10% of all variance explained) then the value is boosted to 0.3 (30% of strain mean variance explained) with n = 4. -2. *Homozygosity:* The second factor has to do with the inherent genetic variance of populations. Recombinant inbred lines are homozygous at nearly all loci. This doubles the genetic variance in a family of recombinant inbred lines compared to a matched number of F2s. This also quadruples the variance compared to a matched number of backcross cases. As a result 40 BXDs sampled just one per genometype will average 2X the genetic variance and 2X the heritability of 40 BDF2 cases. Note that panels made up of isogenic F1 hybrids (so-called diallel crosses, DX) made by crossing recombinant inbred strains (BXD, CC, or HXB) are no longer homozygous at all loci, and while they do expose important new sources of variance associated with dominance, they do not benefit from the 2X gain in genetic variance relative to an F2 intercross. +2. **Homozygosity:** The second factor has to do with the inherent genetic variance of populations. Recombinant inbred lines are homozygous at nearly all loci. This doubles the genetic variance in a family of recombinant inbred lines compared to a matched number of F2s. This also quadruples the variance compared to a matched number of backcross cases. As a result 40 BXDs sampled just one per genometype will average 2X the genetic variance and 2X the heritability of 40 BDF2 cases. Note that panels made up of isogenic F1 hybrids (so-called diallel crosses, DX) made by crossing recombinant inbred strains (BXD, CC, or HXB) are no longer homozygous at all loci, and while they do expose important new sources of variance associated with dominance, they do not benefit from the 2X gain in genetic variance relative to an F2 intercross. Homozygosity For the reasons listed above a QTL effect size of 0.4 detected a panel of BXD lines replicated four times each (160 cases total), corresponds approximately to an effect size of 0.18 in BXDs without replication (40 cases total), and to an effect size of 0.09 in an F2 of 40 cases total. [Williams RW, Dec 23, 2004; updated by RWW July 13, 2019] -eQTL, cis eQTL, trans eQTL +#### eQTL, cis eQTL, trans eQTL -An expression QTL or eQTL. Differences in the expression of mRNA or proteins are often treated as standard phenotypes, much like body height or lung capacity. The variation in these microscopic traits (microtraits) can be mapped using conventional QTL methods. Damerval and colleagues were the first authors to use this kind of nomenclature and in their classic study of 1994 introduced the term PQLs for protein quantitative trait loci. Schadt and colleagues added the acronym eQTL in their early mRNA study of corn, mouse, and humans. We now are "blessed" with all kinds of prefixes to QTLs that highlight the type of trait that has been measured (m for metabolic, b for behavioral, p for physiological or protein). +An expression QTL or eQTL. Differences in the expression of mRNA or proteins are often treated as standard phenotypes, much like body height or lung capacity. The variation in these microscopic traits (microtraits) can be mapped using conventional QTL methods. [Damerval](http://www.genetics.org/cgi/reprint/137/1/289) and colleagues were the first authors to use this kind of nomenclature and in their classic study of 1994 introduced the term PQLs for protein quantitative trait loci. Schadt and colleagues added the acronym eQTL in their early mRNA study of corn, mouse, and humans. We now are "blessed" with all kinds of prefixes to QTLs that highlight the type of trait that has been measured (m for metabolic, b for behavioral, p for physiological or protein). -eQTLs of mRNAs and proteins have the unique property of (usually) having a single parent gene and genetic location. An eQTL that maps to the location of the parent gene that produces the mRNA or protein is referred to as a cis eQTL or local eQTL. In contrast, an eQTL that maps far away from its parent gene is referred to as a trans eQTL. You can use special search commands in GeneNetwork to find cis and trans eQTLs. [Williams RW, Nov 23, 2009, Dec 2009] +eQTLs of mRNAs and proteins have the unique property of (usually) having a single parent gene and genetic location. An eQTL that maps to the location of the parent gene that produces the mRNA or protein is referred to as a **cis eQTL** or local eQTL. In contrast, an eQTL that maps far away from its parent gene is referred to as a **trans eQTL**. You can use special search commands in GeneNetwork to find cis and trans eQTLs. [Williams RW, Nov 23, 2009, Dec 2009] [Go back to index](#index) @@ -134,7 +134,7 @@ The height of the yellow bars in some of the Map View windows provides a measure #### False Discovery Rate (FDR): -A false discovery is an apparently significant finding--usually determined using a particular P value alpha criterion--that given is known to be insignificant or false given other information. When performing a single statistical test we often accept a false discovery rate of 1 in 20 (p = .05). False discovery rates can climb to high levels in large genomic and genetic studies in which hundreds to millions of tests are run and summarized using standard "single test" p values. There are various statistical methods to estimate and control false discovery rate and to compute genome-wide p values that correct for large numbers of implicit or explicit statistical test. The Permutation test in GeneNetwork is one method that is used to prevent and excessive number of false QTL discoveries. Methods used to correct the FDR are approximations and may depend on a set of assumptions about data and sample structure. [Williams RW, April 5, 2008] +A [false discovery](http://en.wikipedia.org/wiki/False_discovery_rate) is an apparently significant finding--usually determined using a particular P value alpha criterion--that given is known to be insignificant or false given other information. When performing a single statistical test we often accept a false discovery rate of 1 in 20 (p = .05). False discovery rates can climb to high levels in large genomic and genetic studies in which hundreds to millions of tests are run and summarized using standard "single test" p values. There are various statistical methods to estimate and control false discovery rate and to compute genome-wide p values that correct for large numbers of implicit or explicit statistical test. The Permutation test in GeneNetwork is one method that is used to prevent and excessive number of false QTL discoveries. Methods used to correct the FDR are approximations and may depend on a set of assumptions about data and sample structure. [Williams RW, April 5, 2008] [Go back to index](#index) @@ -146,34 +146,36 @@ A false discovery is an apparently significant finding--usually determined using GeneNetwork provides summary information on most of the genes and their transcripts. Genes and their alternative splice variants are often are poorly annotated and may not have proper names or symbols. However, almost all entries have a valid GenBank accession identifier. This is a unique code associated with a single sequence deposited in GenBank (Entrez Nucleotide). A single gene may have hundreds of GenBank entries. GenBank entries that share a genomic location and possibly a single gene are generally combined into a single UniGene entry. For mouse, these always begin with "Mm" (Mus musculus) and are followed by a period and then a number. More than half of all mouse UniGene identifiers are associated with a reputable gene, and these genes will have gene identifiers (GeneID). GeneIDs are identical to LocusLink identifiers (LocusID). Even a 10 megabase locus such as human Myopia 4 (MYP4) that is not yet associated with a specific gene is assigned a GeneID--a minor misnomer and one reason to prefer the term LocusID. -See the related FAQ on "How many genes and transcripts are in your databases and what fraction of the genome is being surveyed?" [Williams RW, Dec 23, 2004, updated Jan 2, 2005] +See the related [FAQ](http://gn1.genenetwork.org/faq.html#Q-6) on "How many genes and transcripts are in your databases and what fraction of the genome is being surveyed?" [Williams RW, Dec 23, 2004, updated Jan 2, 2005] #### Genetic Reference Population (GRP): A genetic reference population consists of a set of genetically well characterized lines that are often used over a long period of time to study a multitude of different phenotypes. Once a GRP has been genotyped, subsequent studies can focus on the analysis of interesting and important phenotypes and their joint and independent relations. Most of the mouse GRPs, such as the BXDs used in the GeneNetwork, have been typed using a common set of over 14,000 makers (SNPs and microsatellites). Many of these same GRPs have been phenotyped extensively for more than 25 years, resulting in rich sets of phenotypes. A GRP is an ideal long-term resource for systems genetics because of the relative ease with which vast amounts of diverse data can be accumulated, analyzed, and combined. -The power of GRPs and their compelling scientific advantages derive from the ability to study multiple phenotypes and substantial numbers of genetically defined individuals under one or more environmental conditions. When accurate phenotypes from 20 or more lines in a GRP have been acquired it becomes practical to explore and test the genetic correlations between that trait and any previously measured trait in the same GRP. This fact underlies the use of the term reference in GRP. Since each genetic individual is represented by an entire isogenic line--usually an inbred strain or an isogenic F1 hybrid--it is possible to obtain accurate mean phenotypes associated with each line simply by typing several individuals. GRPs are also ideal for developmental and aging studies because the same genetic individual can be phenotyped at multiple stages. +The power of GRPs and their compelling scientific advantages derive from the ability to study multiple phenotypes and substantial numbers of genetically defined individuals under one or more environmental conditions. When accurate phenotypes from 20 or more lines in a GRP have been acquired it becomes practical to explore and test the genetic correlations between that trait and any previously measured trait in the same GRP. This fact underlies the use of the term **reference** in GRP. Since each genetic individual is represented by an entire isogenic line--usually an inbred strain or an isogenic F1 hybrid--it is possible to obtain accurate mean phenotypes associated with each line simply by typing several individuals. GRPs are also ideal for developmental and aging studies because the same genetic individual can be phenotyped at multiple stages. A GRP can also be used a conventional mapping panel. But unlike most other mapping panel, a GRP can be easily adapted to jointly map sets of functionally related traits (multitrait mapping); a more powerful method to extract causal relations from networks of genetic correlations. -The largest GRPs now consist of more than 400 recombinant inbred lines of Arabidopsis and maize. The BayxSha Arabidopsis set in the GeneNetwork consists of 420 lines. Pioneer Hi-Bred International is rumored to have as many as 4000 maize RI lines. The largest mammalian GRPs are the LXS and BXD RI sets in the GeneNetwork. The Collaborative Cross is the largest mammalian GRP, and over 600 of these strains are now being bred by members of the Complex Trait Consortium. +The largest GRPs now consist of more than 400 recombinant inbred lines of *Arabidopsis* and [maize](http://www.maizegdb.org/cgi-bin/stockadvquery.cgi?check=true&name=&typebox=true&type=701&linkage_group=0&genvar1=&genvar2=&genvar3=&karyovar=0&phenotype=0&attribution=&avail_from=0&parent=0). The BayxSha Arabidopsis set in the GeneNetwork consists of 420 lines. Pioneer Hi-Bred International is rumored to have as many as 4000 maize RI lines. The largest mammalian GRPs are the LXS and BXD RI sets in the GeneNetwork. The Collaborative Cross is the largest mammalian GRP, and over 600 of these strains are now being bred by members of the Complex Trait Consortium. There are several subtypes of GRPs. In addition to recombinant inbred strains there are -- Recombinant congenic (RCC) strains such as the AcB set Consomic or chromosome substitution strains (CSS) of mice (Matin et al., 1999) and rats (Roman et al., 2002) +- Recombinant congenic ([RCC](http://research.jax.org/grs/type/recombcong.htmll)) strains such as the [AcB](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=11374899&query_hl=4) set Consomic or chromosome substitution strains ([CSS](http://research.jax.org/grs/type/consomic.html)) of mice (Matin et al., [1999](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=10508525&query_hl=11)) and rats (Roman et al., [2002](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=12858554&query_hl=7)) -- Recombinant intercross (RIX) F1 sets made by mating different RI strains to each other to generate large set of R! first generation (F1) progeny (RIX). This is a standard (diallel cross) of RI inbred strains. Genetic analysis of a set of RIX progeny has some advantages over a corresponding analysis of RI strains. The first of these is that while each set of F1 progeny is fully isogenic (AXB1 x AXB2 gives a set of isogenic F1s), these F1s are not inbred but are heterozygous at many loci across the genome. RIX therefore retain the advance of being genetically defined and replicable, but without the disadvantage of being fully inbred. RIX have a genetic architecture more like natural populations. The second correlated advantage is that it is possible to study patterns of dominance of allelic variants using an RIX cross. Almost all loci or genes that differs between the original stock strains (A and B) will be heterozygous among a sufficiently larges set of RIX. A set of RIX progeny can therefore be mapped using the same methods used to map an F2 intercross. Mapping of QTLs may have somewhat more power and precision than when RI strains are used alone. A third advantage is that RIX sets make it possible to expand often limited RI resources to very large sizes to confirm and extend models of genetic or GXE effects. For example a set of 30 AXB strains can be used to generate a full matrix of 30 x 29 unique RIX progeny. The main current disadvantage of RIX panels is the comparative lack of extant phenotype data. +- Recombinant intercross ([RIX](http://www.ncbi.nlm.nih.gov/pubmed/?term=15879512)) F1 sets made by mating different RI strains to each other to generate large set of R! first generation (F1) progeny (RIX). This is a standard ([diallel cross](http://en.wikipedia.org/wiki/Diallel_cross)) of RI inbred strains. Genetic analysis of a set of RIX progeny has some advantages over a corresponding analysis of RI strains. The first of these is that while each set of F1 progeny is fully isogenic (AXB1 x AXB2 gives a set of isogenic F1s), these F1s are not inbred but are heterozygous at many loci across the genome. RIX therefore retain the advance of being genetically defined and replicable, but without the disadvantage of being fully inbred. RIX have a genetic architecture more like natural populations. The second correlated advantage is that it is possible to study patterns of dominance of allelic variants using an RIX cross. Almost all loci or genes that differs between the original stock strains (A and B) will be heterozygous among a sufficiently larges set of RIX. A set of RIX progeny can therefore be mapped using the same methods used to map an F2 intercross. Mapping of QTLs may have somewhat more power and precision than when RI strains are used alone. A third advantage is that RIX sets make it possible to expand often limited RI resources to very large sizes to confirm and extend models of genetic or GXE effects. For example a set of 30 AXB strains can be used to generate a full matrix of 30 x 29 unique RIX progeny. The main current disadvantage of RIX panels is the comparative lack of extant phenotype data. - Recombinant F1 line sets can also be made by backcrossing an entire RI sets to a single inbred line that carries an interesting mutation or transgene (RI backcross or RIB). GeneNetwork includes one RI backcross sets generated by Kent Hunter. In this RIB each of 18 AKXD RI strains were crossed to an FVB/N line that carries a tumor susceptibility allele (polyoma middle T). - All of these sets of lines are GRPs since each line is genetically defined and because the set as a whole can in principle be easily regenerated and phenotyped. Finally, each of these resources can be used to track down genetic loci that are causes of variation in phenotype using variants of standard linkage analysis. +All of these sets of lines are GRPs since each line is genetically defined and because the set as a whole can in principle be easily regenerated and phenotyped. Finally, each of these resources can be used to track down genetic loci that are causes of variation in phenotype using variants of standard linkage analysis. - A Diversity Panel such as that used by the Mouse Phenome Project is not a standard GRPs, although its also shares the ability to accumulate and study networks of phenotypes. The main difference is that a Diversity Panel cannot be used for conventional linkage analysis. A sufficiently large Diversity Panel could in principle be used for the equivalent of an assocation study. However, these are definitely NOT in silico studies, because hundreds of individuals need to be phenotyped for every trait. Surveys of many diverse isogenic lines (inbred or F1 hybrids) is statistically the equivalent of a human association study (the main difference is the ability to replicate measurements and study sets of traits) and therefore, like human association studies, does require very high sample size to map polygenic traits. Like human association studies there is also a high risk of false positive results due to population stratification and non-syntenic marker association. +A Diversity Panel such as that used by the Mouse Phenome Project is not a standard GRPs, although its also shares the ability to accumulate and study networks of phenotypes. The main difference is that a Diversity Panel cannot be used for conventional linkage analysis. A sufficiently large Diversity Panel could in principle be used for the equivalent of an assocation study. However, these are definitely NOT in silico studies, because hundreds of individuals need to be phenotyped for every trait. Surveys of many diverse isogenic lines (inbred or F1 hybrids) is statistically the equivalent of a human association study (the main difference is the ability to replicate measurements and study sets of traits) and therefore, like human association studies, does require very high sample size to map polygenic traits. Like human association studies there is also a high risk of false positive results due to population stratification and non-syntenic marker association. - A good use of a Diversity Panel is as a fine-mapping resource with which to dissect chromosomal intervals already mapped using a conventional cross or GRP. GeneNetwork now includes Mouse Diversity Panel (MDP) data for several data sets. We now typically include all 16 sequenced strains of mice, and add PWK/PhJ, NZO/HiLtJ (two of the eight members of the Collaborative Cross), and several F1 hybrids. The MDP data is often appended at the bottom of the GRP data set with which is was acquired (e.g., BXD hippocampal and BXD eye data sets). [Williams RW, June 19, 2005; Dec 4, 2005] +A good use of a Diversity Panel is as a fine-mapping resource with which to dissect chromosomal intervals already mapped using a conventional cross or GRP. GeneNetwork now includes Mouse Diversity Panel (MDP) data for several data sets. We now typically include all 16 sequenced strains of mice, and add PWK/PhJ, NZO/HiLtJ (two of the eight members of the Collaborative Cross), and several F1 hybrids. The MDP data is often appended at the bottom of the GRP data set with which is was acquired (e.g., BXD hippocampal and BXD eye data sets). [Williams RW, June 19, 2005; Dec 4, 2005] - Genotype: The state of a gene or DNA sequence, usually used to describe a contrast between two or more states, such as that between the normal state (wildtype) and a mutant state (mutation) or between the alleles inherited from two parents. All species that are included in GeneNetwork are diploid (derived from two parents) and have two copies of most genes (genes located on the X and Y chromosomes are exceptions). As a result the genotype of a particular diploid individual is actually a pair of genotypes, one from each parents. For example, the offspring of a mating between strain A and strain B will have one copy of the A genotype and one copy of the B genotype and therefore have an A/B genotype. In contrast, offspring of a mating between a female strain A and a male strain A will inherit only A genotypes and have an A/A genotype. +#### Genotype + +The state of a gene or DNA sequence, usually used to describe a contrast between two or more states, such as that between the normal state (wildtype) and a mutant state (mutation) or between the alleles inherited from two parents. All species that are included in GeneNetwork are diploid (derived from two parents) and have two copies of most genes (genes located on the X and Y chromosomes are exceptions). As a result the genotype of a particular diploid individual is actually a pair of genotypes, one from each parents. For example, the offspring of a mating between strain A and strain B will have one copy of the A genotype and one copy of the B genotype and therefore have an A/B genotype. In contrast, offspring of a mating between a female strain A and a male strain A will inherit only A genotypes and have an A/A genotype. Genotypes can be measured or inferred in many different ways, even by visual inspection of animals (e.g. as Gregor Mendel did long before DNA was discovered). But now the typical method is to directly test DNA that has a well define chromosomal location that has been obtained from one or usually many cases using molecular tests that often rely on polymerase chain reaction steps and sequence analysis. Each case is genotyped at many chromosomal locations (loci, markers, or genes). The entire collection of genotypes (as many a 1 million for a single case) is also sometimes referred to as the cases genotype, but the word "genometype" might be more appropriate to highlight the fact that we are now dealing with a set of genotypes spanning the entire genome (all chromosomes) of the case. @@ -187,13 +189,13 @@ Text here [Williams RW, July 15, 2010] ## H -#### Heritability, h2: +#### Heritability, h2: Heritability is a rough measure of the ability to use genetic information to predict the level of variation in phenotypes among progeny. Values range from 0 to 1 (or 0 to 100%). A value of 1 or 100% means that a trait is entirely predictable based on paternal/materinal and genetic data (in other words, a Mendelian trait), whereas a value of 0 means that a trait is not at all predictable from information on gene variants. Estimates of heritability are highly dependent on the environment, stage, and age. Important traits that affect fitness often have low heritabilities because stabilizing selection reduces the frequency of DNA variants that produce suboptimal phenotypes. Conversely, less critical traits for which substantial phenotypic variation is well tolerated, may have high heritability. The environment of laboratory rodents is unnatural, and this allows the accumulation of somewhat deleterious mutations (for example, mutations that lead to albinism). This leads to an upward trend in heritability of unselected traits in laboratory populations--a desirable feature from the point of view of the biomedical analysis of the genetic basis of trait variance. Heritability is a useful parameter to measure at an early stage of a genetic analysis, because it provides a rough gauge of the likelihood of successfully understanding the allelic sources of variation. Highly heritable traits are more amenable to mapping studies. There are numerous ways to estimate heritability, a few of which are described below. [Williams RW, Dec 23, 2004] -#### h2 Estimated by Intraclass Correlation: +#### h2 Estimated by Intraclass Correlation: Heritability can be estimated using the intraclass correlation coefficient. This is essentially a one-way repeated measures analysis of variance (ANOVA) of the reliability of trait data. Difference among strains are considered due to a random effect, whereas variation among samples within a single strain are considered due to measurement error. One can use the method implemented by SAS (PROC VARCOMP) that exploits a restricted maximum likelihood (REML) approach to estimate the intraclass correlation coefficient instead of an ordinary least squares method. The general equation for the intraclass correlation is: @@ -201,36 +203,36 @@ Heritability can be estimated using the intraclass correlation coefficient. This where n is the average number of cases per strain. The intraclass correlation approaches 1 when there is minimal variation within strains, and strain means differ greatly. In contrast, if difference between strains are less than what would be predicted from the differences within strain, then the intraclass correlation will produce negative estimates of heritability. Negative heritability is usually a clue that the design of the experiment has injected excessive within-strain variance. It is easy for this to happen inadvertently by failing to correct for a batch effect. For example, if one collects the first batch of data for strains 1 through 20 during a full moon, and a second batch of data for these same strains during a rare blue moon, then the apparent variation within strain may greatly exceed the among strain variance. A technical batch effect has been confounded with the within-strain variation and has swamped any among-strain variance. What to do? Fix the batch effect, sex effect, age effect, etc., first! [Williams RW, Chesler EJ, Dec 23, 2004] -#### h2 Estimated using Hegmann and Possidente's Method (Adjusted Heritability in the Basic Statisics): +#### h2 Estimated using Hegmann and Possidente's Method (Adjusted Heritability in the Basic Statisics): A simple estimate of heritability for inbred lines involves comparing the variance between strain means (Va) to the total variance (Vt) of the phenotype, where Va is the a rough estimate of the additive genetic variance and Vt is the equal to Va and the average environmental variance, Ve. For example, if we study 10 cases of each of 20 strains, we have a total variance of the phenotype across 200 samples, and a strain mean variance across 20 strain averages. We can use this simple equation to estimate the heritability: -h2 = Va / Vt +h2 = Va / Vt -This estimate of heritability will be an overestimate, and the severity of this bias will be a function of the within-strain standard error of the mean. Even a random data set of 10 each of 20 strains that should have an h2 of 0, will often give h2 values of 0.10 to 0.20. (Try this in a spreadsheet program using random numbers.) +This estimate of heritability will be an **overestimate**, and the severity of this bias will be a function of the within-strain standard error of the mean. Even a random data set of 10 each of 20 strains that should have an h2 of 0, will often give h2 values of 0.10 to 0.20. (Try this in a spreadsheet program using random numbers.) However, this estimate of h2 cannot be compared directly to those calculated using standard intercrosses and backcrosses. The reason is that all cases above are fully inbred and no genotypes are heterozygous. As a result the estimate of Va will be inflated two-fold. Hegmann and Possidente (1981 suggested a simple solution; adjust the equation as follows: -h2 = 0.5Va / (0.5Va+Ve) +h2 = 0.5Va / (0.5Va+Ve) -The factor 0.5 is applied to Va to adjust for the overestimation of additive genetic variance among inbred strains. This estimate of heritability also does not make allowances for the within-strain error term. The 0.5 adjustment factor is not recommended any more because h2 is severely underestimated. This adjustment is really only needed if the goal is to compare h2 between intercrosses and those generated using panels of inbred strains. +The factor 0.5 is applied to Va to adjust for the overestimation of additive genetic variance among inbred strains. This estimate of heritability also does not make allowances for the within-strain error term. The 0.5 adjustment factor is not recommended any more because h2 is severely **underestimated**. This adjustment is really only needed if the goal is to compare h2 between intercrosses and those generated using panels of inbred strains. -#### h2RIx̅ +#### h2RIx̅ -Finally, heritability calculations using strain means, such as those listed above, do not provide estimates of the effective heritability achieved by resampling a given line, strain, or genometype many times. Belknap (1998) provides corrected estimates of the effective heritability. Figure 1 from his paper (reproduced below) illustrates how resampling helps a great deal. Simply resampling each strain 8 times can boost the effective heritability from 0.2 to 0.8. The graph also illustrates why it often does not make sense to resample much beyond 4 to 8, depending on heritability. Belknap used the term h2RIx̅ in this figure and paper, since he was focused on data generated using recombinant inbred (RI) strains, but the logic applies equally well to any panel of genomes for which replication of individual genometypes is practical. This h2RIx̅ can be calculated simply by: -h2RIx̅ = Va / (Va+(Ve/n)) where Va is the genetic variability (variability between strains), Ve is the environmental variability (variability within strains), and n is the number of within strain replicates. Of course, with many studies the number of within strain replicates will vary between strains, and this needs to be dealt with. A reasonable approach is to use the harmonic mean of n across all strains. +Finally, heritability calculations using strain means, such as those listed above, do not provide estimates of the effective heritability achieved by resampling a given line, strain, or genometype many times. Belknap ([1998](http://gn1.genenetwork.org/images/upload/Belknap_Heritability_1998.pdf)) provides corrected estimates of the effective heritability. Figure 1 from his paper (reproduced below) illustrates how resampling helps a great deal. Simply resampling each strain 8 times can boost the effective heritability from 0.2 to 0.8. The graph also illustrates why it often does not make sense to resample much beyond 4 to 8, depending on heritability. Belknap used the term h2RIx̅ in this figure and paper, since he was focused on data generated using recombinant inbred (RI) strains, but the logic applies equally well to any panel of genomes for which replication of individual genometypes is practical. This h2RIx̅ can be calculated simply by: +h2RIx̅ = Va / (Va+(Ve/n)) where Va is the genetic variability (variability between strains), Ve is the environmental variability (variability within strains), and n is the number of within strain replicates. Of course, with many studies the number of within strain replicates will vary between strains, and this needs to be dealt with. A reasonable approach is to use the harmonic mean of n across all strains. Homozygosity An analysis of statistical power is useful to estimate numbers of replicates and strains needed to detect and resolve major sources of trait variance and covariance. A versatile method has been developed by Sen and colleagues (Sen et al., 2007) and implemented in the R program. qtlDesign. David Ashbrook implemented a version of this within Shiny that can help you estimate power for different heritability values QTL effect sizes, cohort sizes, and replication rates: -### Power Calculator (D. Ashbrook) +**[Power Calculator (D. Ashbrook)](https://dashbrook1.shinyapps.io/bxd_power_calculator_app/)** We can see that in all situations power is increased more by increasing the number of lines than by increasing the number of biological replicates. Dependent upon the heritability of the trait, there is little gain in power when going above 4-6 biological replicates. [DGA, Feb 1, 2019] [Chesler EJ, Dec 20, 2004; RWW updated March 7, 2018; Ashbrook DG, updated Feb 1, 2019] #### Hitchhiking Effect: -Conventional knockout lines (KOs) of mice are often mixtures of the genomes of two strains of mice. One important consequence of this fact is that a conventional comparison of wildtype and KO litter mates does not only test of the effects of the KO gene itself but also tests the effects of thousands of "hitchhiking" sequence polymorphisms in genes that flank the KO gene. This experimental confound can be difficult to resolve (but see below). This problem was first highlighted by Robert Gerlai (1996). +Conventional knockout lines (KOs) of mice are often mixtures of the genomes of two strains of mice. One important consequence of this fact is that a conventional comparison of wildtype and KO litter mates does not only test of the effects of the KO gene itself but also tests the effects of thousands of "hitchhiking" sequence polymorphisms in genes that flank the KO gene. This experimental confound can be difficult to resolve (but see below). This problem was first highlighted by Robert Gerlai ([1996](http://gn1.genenetwork.org/images/upload/Gerlai_TINS_1996.pdf)). **Genetics of KO Lines**. The embryonic stem cells used to make KOs are usually derived from a 129 strain of mouse (e.g., 129/OlaHsd). Mutated stem cells are then added to a C57BL/6J blastocyst to generate B6x129 chimeric mice. Germline transmission of the KO allele is tested and carriers are then used to establish heterozygous +/- B6.129 KO stock. This stock is often crossed back to wildtype C57BL/6J strains for several generations. At each generation the transmission of the KO is checked by genotyping the gene or closely flanking markers in each litter of mice. Carriers are again selected for breeding. The end result of this process is a KO congenic line in which the genetic background is primarily C57BL/6J except for the region around the KO gene. @@ -238,13 +240,13 @@ It is often thought that 10 generations of backcrossing will result in a pure ge Congenic -After 20 generations of backcrossing nearly +/-5 cM on either side of the KO will still usually be derived from 129 (see Figure 3.6) This amounts to an average of +/- 10 megabases of DNA around the KO. The wildtype littermates do NOT have this flanking DNA from 129 and they will be like a true C57BL/6J. The +/- 10 megabases to either side of the KO is known as the "hitchhiking" chromosomal interval. Any polymorphism between 129 and B6 in this interval has the potential to have significant downstream effects on gene expression, protein expression, and higher order traits such as anxiety, activity, and maternal behavior. Much of the conventional KO literature is highly suspect due to this hitchhiker effect (see Gerlai R, Trends in Neurosci 1996 19:177). +After 20 generations of backcrossing nearly +/-5 cM on either side of the KO will still usually be derived from 129 (see [Figure 3.6](http://www.informatics.jax.org/silverbook/frames/frame3-3.shtml)) This amounts to an average of +/- 10 megabases of DNA around the KO. The wildtype littermates do NOT have this flanking DNA from 129 and they will be like a true C57BL/6J. The +/- 10 megabases to either side of the KO is known as the "hitchhiking" chromosomal interval. Any polymorphism between 129 and B6 in this interval has the potential to have significant downstream effects on gene expression, protein expression, and higher order traits such as anxiety, activity, and maternal behavior. Much of the conventional KO literature is highly suspect due to this hitchhiker effect (see Gerlai R, [Trends in Neurosci 1996 19:177](http://gn1.genenetwork.org/images/upload/Gerlai_TINS_1996.pdf)). -As one example, consider the thyroid alpha receptor hormone gene Thra and its KO. Thra maps to Chr 11 at about 99 Mb. A conventional KO made as described above will have a hitchhiking 129 chromosomal interval extending from about 89 Mb to 109 Mb even after 20 generations of backcrossing to B6. Since the mouse genome is about 2.6 billion base pairs and contains about 26,000 genes, this 20 Mb region will typically contain about 200 genes. The particular region of Chr 11 around Thra has an unusually high density of genes (2-3X) and includes many highly expressed and polymorphic genes, including Nog, Car10, Cdc34, Col1a1, Dlx4, Myst2, Ngfr, Igf2bp1, Gip, the entire Hoxb complex, Sp6, Socs7, Lasp1, Cacnb1, Pparbp, Pnmt, Erbb2, Grb7, Nr1d1, Casc3, Igfbp4, and the entire Krt1 complex. Of these gene roughly half will be polymorphic between B6 and 129. It is like having a busload of noisy and possibly dangerous hitchhikers. Putative KO effects may be generated by a complex subset of these 100 polymorphic genes. +As one example, consider the thyroid alpha receptor hormone gene Thra and its KO. Thra maps to Chr 11 at about 99 Mb. A conventional KO made as described above will have a hitchhiking 129 chromosomal interval extending from about 89 Mb to 109 Mb even after 20 generations of backcrossing to B6. Since the mouse genome is about 2.6 billion base pairs and contains about 26,000 genes, this 20 Mb region will typically contain about 200 genes. The particular region of Chr 11 around Thra has an unusually high density of genes (2-3X) and includes many highly expressed and polymorphic genes, including *Nog*, *Car10*, *Cdc34*, *Col1a1*, *Dlx4*, *Myst2*, *Ngfr*, *Igf2bp1*, *Gip*, the entire *Hoxb* complex, *Sp6*, *Socs7*, *Lasp1*, *Cacnb1*, *Pparbp*, *Pnmt*, *Erbb2*, *Grb7*, *Nr1d1*, *Casc3*, *Igfbp4*, and the entire *Krt1* complex. Of these gene roughly half will be polymorphic between B6 and 129. It is like having a busload of noisy and possibly dangerous hitchhikers. Putative KO effects may be generated by a complex subset of these 100 polymorphic genes. What is the solution? -1. Do not use litter mates as controls without great care. They are not really the correct genetic control. The correct genetic control is a congenic strain of the same general type without the KO or with a different KO in a nearby gene. These are often available as KOs in neighboring genes that are not of interest. For example, the gene Casc3 is located next to Thra. If a KO in Casc3 is available, then compare the two KOs and see if phenotypes of the two KOs differ ways predicted given the known molecular functions of the gene. +1. Do not use litter mates as controls without great care. They are not really the correct genetic control. The correct genetic control is a congenic strain of the same general type without the KO or with a different KO in a nearby gene. These are often available as KOs in neighboring genes that are not of interest. For example, the gene *Casc3* is located next to Thra. If a KO in Casc3 is available, then compare the two KOs and see if phenotypes of the two KOs differ ways predicted given the known molecular functions of the gene. 2. Use a KO in which the KO has been backcrossed to a 129 strain--ideally the same strain from which ES cells were obtained. This eliminates the hitchhiker effect entirely and the KO, HET, and WT littermates really can be compared. @@ -254,7 +256,7 @@ What is the solution? Homozygosity -Legend:from Silver, L. (1995) Oxford University Press +Legend:from [Silver, L. (1995) Oxford University Press](http://www.informatics.jax.org/silver/index.shtml) [Go back to index](#index) @@ -268,23 +270,23 @@ The interquartile range is the difference between the 75% and 25% percentiles of #### Interval Mapping: -Interval mapping is a process in which the statistical significance of a hypothetical QTL is evaluated at regular points across a chromosome, even in the absence of explicit genotype data at those points. In the case of WebQTL, significance is calculated using an efficient and very rapid regression method, the Haley-Knott regression equations (Haley CS, Knott SA. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers; Heredity 69:315–324), in which trait values are compared to the known genotype at a marker or to the probability of a specific genotype at a test location between two flanking markers. (The three genotypes are coded as -1, 0, and +1 at known markers, but often have fractional values in the intervals between markers.) The inferred probability of the genotypes in regions that have not been genotyped can be estimated from genotypes of the closest flanking markers. GeneNetwork/WebQTL compute linkage at intervals of 1 cM or less. As a consequence of this approach to computing linkage statistics, interval maps often have a characteristic shape in which the markers appear as sharply defined inflection points, and the intervals between nodes are smooth curves. [Chesler EJ, Dec 20, 2004; RWW April 2005; RWW Man 2014] +Interval mapping is a process in which the statistical significance of a hypothetical QTL is evaluated at regular points across a chromosome, even in the absence of explicit genotype data at those points. In the case of WebQTL, significance is calculated using an efficient and very rapid regression method, the Haley-Knott regression equations ([Haley CS, Knott SA. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers; Heredity 69:315–324](http://www.ncbi.nlm.nih.gov/pubmed/16718932)), in which trait values are compared to the known genotype at a marker or to the probability of a specific genotype at a test location between two flanking markers. (The three genotypes are coded as -1, 0, and +1 at known markers, but often have fractional values in the intervals between markers.) The inferred probability of the genotypes in regions that have not been genotyped can be estimated from genotypes of the closest flanking markers. GeneNetwork/WebQTL compute linkage at intervals of 1 cM or less. As a consequence of this approach to computing linkage statistics, interval maps often have a characteristic shape in which the markers appear as sharply defined inflection points, and the intervals between nodes are smooth curves. [Chesler EJ, Dec 20, 2004; RWW April 2005; RWW Man 2014] #### Interval Mapping Options: -- Permutation Test: Select this option to determine the approximate LRS value that matches a genome-wide p-value of .05. +- _Permutation Test_: Select this option to determine the approximate LRS value that matches a genome-wide p-value of .05. -- Bootstrap Test: Select this option to evaluate the consistency with which peak LRS scores cluster around a putative QTL. Deselect this option if it obscures the SNP track or the additive effect track. +- _Bootstrap Test_: Select this option to evaluate the consistency with which peak LRS scores cluster around a putative QTL. Deselect this option if it obscures the SNP track or the additive effect track. -- Additive Effect: The additive effect (shown by the red lines in these plots) provide an estimate of the change in the average phenotype that is brought about by substituting a single allele of one type with that of another type. +- _Additive Effect_: The additive effect (shown by the red lines in these plots) provide an estimate of the change in the average phenotype that is brought about by substituting a single allele of one type with that of another type. -- SNP Track: The SNP Seismograph Track provides information on the regional density of segregating variants in the cross that may generate trait variants. It is plotted along the X axis. If a locus spans a region with both high and low SNP density, then the causal variant has a higher prior probability to be located in the region with high density than in the region with low density. +- _SNP Track_: The SNP Seismograph Track provides information on the regional density of segregating variants in the cross that may generate trait variants. It is plotted along the X axis. If a locus spans a region with both high and low SNP density, then the causal variant has a higher prior probability to be located in the region with high density than in the region with low density. -- Gene Track: This track overlays the positions of known genes on the physical Interval Map Viewer. If you hover the cursor over genes on this track, minimal information (symbol, position, and exon number) will appear. +- _Gene Track_: This track overlays the positions of known genes on the physical Interval Map Viewer. If you hover the cursor over genes on this track, minimal information (symbol, position, and exon number) will appear. -- Display from X Mb to Y Mb: Enter values in megabases to regenerate a smaller or large map view. +- _Display from X Mb to Y Mb_: Enter values in megabases to regenerate a smaller or large map view. -- Graph width (in pixels): Adjust this value to obtain larger or smaller map views (x axis only). +- _Graph width (in pixels)_: Adjust this value to obtain larger or smaller map views (x axis only). [Go back to index](#index) @@ -308,15 +310,15 @@ Interval mapping is a process in which the statistical significance of a hypothe #### Literature Correlation: -The literature correlation is a unique feature in GeneNetwork that quantifies the similarity of words used to describe genes and their functions. Sets of words associated with genes were extracted from MEDLINE/PubMed abstracts (Jan 2017 by Ramin Homayouni, Diem-Trang Pham, and Sujoy Roy). For example, about 2500 PubMed abstracts contain reference to the gene "Sonic hedgehog" (Shh) in mouse, human, or rat. The words in all of these abstracts were extracted and categorize by their information content. A word such as "the" is not interesting, but words such as "dopamine" or "development" are useful in quantifying similarity. Sets of informative words are then compared—one gene's word set is compared the word set for all other genes. Similarity values are computed for a matrix of about 20,000 genes using latent semantic indexing (see Xu et al., 2011). Similarity values are also known as literature correlations. These values are always positive and range from 0 to 1. Values between 0.5 and 1.0 indicate moderate-to-high levels of overlap of vocabularies. +The literature correlation is a unique feature in GeneNetwork that quantifies the similarity of words used to describe genes and their functions. Sets of words associated with genes were extracted from MEDLINE/PubMed abstracts (Jan 2017 by Ramin Homayouni, Diem-Trang Pham, and Sujoy Roy). For example, about 2500 PubMed abstracts contain reference to the gene "Sonic hedgehog" (Shh) in mouse, human, or rat. The words in all of these abstracts were extracted and categorize by their information content. A word such as "the" is not interesting, but words such as "dopamine" or "development" are useful in quantifying similarity. Sets of informative words are then compared—one gene's word set is compared the word set for all other genes. Similarity values are computed for a matrix of about 20,000 genes using latent semantic indexing [(see Xu et al., 2011)](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0018851). Similarity values are also known as literature correlations. These values are always positive and range from 0 to 1. Values between 0.5 and 1.0 indicate moderate-to-high levels of overlap of vocabularies. -The literature correlation can be used to compare the "semantic" signal-to-noise of different measurements of gene, mRNA, and protein expression. Consider this common situation:There are three probe sets that measure Kit gene expression (1459588\_at, 1415900\_a\_at, and 1452514\_a\_at) in the Mouse BXD Lung mRNA data set (HZI Lung M430v2 (Apr08) RMA). Which one of these three gives the best measurement of Kit expression? It is impractical to perform quantitative rtPCR studies to answer this question, but there is a solid statistical answer that relies on Literature Correlation. Do the following: For each of the three probe sets, generate the top 1000 literature correlates. This will generate three apparently identical lists of genes that are known from the PubMed literature to be associated with the Kit oncogene. But the three lists are NOT actually identical when we look at the Sample Correlation column. To answer the question "which of the three probe sets is best", review the actual performance of the probe sets against this set of 1000 "friends of Kit". Do this by sorting all three lists by their Sample Correlation column (high to low). The clear winner is probe set 1415900_a_at. The 100th row in this probe set's list has a Sample Correlation of 0.620 (absolute value). In comparison, the 100th row for probe set 1452514_a_at has a Sample Correlation of 0.289. The probe set that targets the intron comes in last at 0.275. In conclusion, the probe set that targets the proximal half of the 3' UTR (1415900_a_at) has the highest "agreement" between Literature Correlation and Sample Correlation, and is our preferred measurement of Kit expression in the lung in this data set. (Updated by RWW and Ramin Homayouni, April 2017.) +The literature correlation can be used to compare the "semantic" signal-to-noise of different measurements of gene, mRNA, and protein expression. Consider this common situation:There are three probe sets that measure Kit gene expression (1459588\_at, 1415900\_a\_at, and 1452514\_a\_at) in the Mouse BXD Lung mRNA data set (HZI Lung M430v2 (Apr08) RMA). Which one of these three gives the best measurement of Kit expression? It is impractical to perform quantitative rtPCR studies to answer this question, but there is a solid statistical answer that relies on **Literature Correlation**. Do the following: For each of the three probe sets, generate the top 1000 literature correlates. This will generate three apparently identical lists of genes that are known from the PubMed literature to be associated with the Kit oncogene. But the three lists are NOT actually identical when we look at the **Sample Correlation** column. To answer the question "which of the three probe sets is best", review the actual performance of the probe sets against this set of 1000 "friends of Kit". Do this by sorting all three lists by their Sample Correlation column (high to low). The clear winner is probe set 1415900_a_at. The 100th row in this probe set's list has a Sample Correlation of 0.620 (absolute value). In comparison, the 100th row for probe set 1452514_a_at has a Sample Correlation of 0.289. The probe set that targets the intron comes in last at 0.275. In conclusion, the probe set that targets the proximal half of the 3' UTR (1415900_a_at) has the highest "agreement" between Literature Correlation and Sample Correlation, and is our preferred measurement of Kit expression in the lung in this data set. (Updated by RWW and Ramin Homayouni, April 2017.)
    #### LOD: -The logarithm of the odds (LOD) provides a measure of the association between variation in a phenotype and genetic differences (alleles) at a particular chromosomal locus (see Nyholt 2000 for a lovely review of LOD scores). +The logarithm of the odds (LOD) provides a measure of the association between variation in a phenotype and genetic differences (alleles) at a particular chromosomal locus (see Nyholt [2000](http://www.sciencedirect.com/science/article/pii/S0002929707626391) for a lovely review of LOD scores). A LOD score is defined as the logarithm of the ratio of two likelihoods: (1) in the numerator the likelihood for the alternative hypothesis, namely that there is linkage at the chromosomal marker, and (2) the likelihood of the null hypothesis that there is no linkage. Likelihoods are probabilities, but they are not Pr(hypothesis | data) but rather Pr(data | two alternative hypotheses). That's why they are called likelihoods rather than probabilities. (The "|" symbol above translates to "given the"). Since LOD and LRS scores are associated with two particular hypotheses or models, they are also associated with the degrees of freedom of those two alternative models. When the model only has one degree of freedom this conversion between LOD to p value will work: 
    @@ -367,11 +369,11 @@ To compute the marker regression (or correlation) we just compare values in Rows
 
 #### Normal Probability Plot:
 
-A normal probability plot is a powerful tool to evaluate the extent to which a distribution of values conforms to (or deviates from) a normal Gaussian distribution. The Basic Statistics tools in GeneNetwork provides these plots for any trait. If a distribution of numbers is normal then the actual values and the predicted values based on a z score (units of deviation from the mean measured in standard deviation units) will form a nearly straight line. These plots can also be used to efficiently flag outlier samples in either tail of the distribution.
+A [normal probability plot](http://en.wikipedia.org/wiki/Normal_probability_plot) is a powerful tool to evaluate the extent to which a distribution of values conforms to (or deviates from) a normal Gaussian distribution. The Basic Statistics tools in GeneNetwork provides these plots for any trait. If a distribution of numbers is normal then the actual values and the predicted values based on a z score (units of deviation from the mean measured in standard deviation units) will form a nearly straight line. These plots can also be used to efficiently flag outlier samples in either tail of the distribution.
 
 In genetic studies, the probability plot can be used to detect the effects of major effect loci. A classical Mendelian locus will typically be associated with either a bimodal or trimodal distribution. In the plot below based on 99 samples, the points definitely do not fall on a single line. Three samples (green squares) have unusually high values; the majority of samples fall on a straight line between z = -0.8 to z = 2; and 16 values have much lower trait values than would be predicted based on a single normal distribution (a low mode group). The abrupt discontinuity in the distribution at -0.8 z is due to the effect of a single major Mendelian effect.
 
-Deviations from normality of the sort in the figure below should be considered good news from the point of view of likely success of tracking down the locations of QTLs. However, small numbers of outliers may require special statistical handling, such as their exclusion or winsorising (see more below on "Winsorizing"). [RWW June 2011] 
+Deviations from normality of the sort in the figure below should be considered good news from the point of view of likely success of tracking down the locations of QTLs. However, small numbers of outliers may require special statistical handling, such as their exclusion or [winsorising](http://en.wikipedia.org/wiki/Winsorising) (see more below on "Winsorizing"). [RWW June 2011] 
 
 Homozygosity
 
@@ -381,11 +383,11 @@ Deviations from normality of the sort in the figure below should be considered g
 
 ## O
 
-#### Outliers: (also see Wikipedia)
+#### Outliers: (also see [Wikipedia](http://en.wikipedia.org/wiki/Outlier))
 
 Statistical methods often assume that the distribution of trait values is close to a Gaussian normal bell-shaped curve and that there are no outlier values that are extremely high or low compared to the average. Some traits can be clearly split into two or more groups (affected cases and unaffected cases) and this is not a problem as long as the number of cases in each group is close to the number that you expected by chance and that your sample size is reasonable high (40 or more for recombinant inbred strains). Mapping functions and most statistical procedure in GeneNetwork should work reasonable well (the pair scan function for epistatic interactions is one possible exception).
 
-However, correlations and QTL mapping methods can be highly sensitive to outlier values. Make sure you review your data for outliers before mapping. GeneNetwork flags all outliers for you in the Trait Data and Analysis window and gives you the option of zapping these extreme values. Options include (1) do nothing, (2) delete the outliers and see what happens to your maps, (3) Winsorize the values of the outliers. You might try all three options and determine if your main results are stable or not. With small samples or extreme outliers, you may find the correlation and mapping results to be volatile. In general, if results (correlations, QTL positions or QTL LRS score) depend highly on one or two outliers (5-10% of the samples) then you should probably delete or winsorize the outliers.
+However, correlations and QTL mapping methods can be highly sensitive to outlier values. Make sure you review your data for outliers before mapping. GeneNetwork flags all outliers for you in the Trait Data and Analysis window and gives you the option of zapping these extreme values. Options include (1) do nothing, (2) delete the outliers and see what happens to your maps, (3) [Winsorize](http://en.wikipedia.org/wiki/Winsorising) the values of the outliers. You might try all three options and determine if your main results are stable or not. With small samples or extreme outliers, you may find the correlation and mapping results to be volatile. In general, if results (correlations, QTL positions or QTL LRS score) depend highly on one or two outliers (5-10% of the samples) then you should probably delete or winsorize the outliers.
 
 In order to calculate outliers, we first determine the Q1(25%) and Q3(75%) values and then multiply by a constant (in our case 1.5; a higher constant is less sensitive to outliers). This value is then subtracted from the Q1 value and added to the Q3 value in order to determine the lower and upper bounds. Values that fall above the upper bound or below the lower bound are considered outliers.
 
@@ -409,15 +411,15 @@ The output table in GeneNetwork list the the two intervals at the top of the tab
 
 CAUTIONS and LIMITATIONS: Pair-scan is only implemented for recombinant inbred strains. We do not recommend the use of this function with sample sizes of less than 60 recombinant inbred strains. Pair-scan procedures need careful diagnostics and an be very sensitive to outliers and to the balance among the four possible two-locus genotype classes among a set of RI strains. Pair-scan is not yet implemented for F2 progeny.
 
-GeneNetwork implements a rapid but non-exhaustive DIRECT algorithm (Lundberg et al., 2004) that efficiently searches for epistatic interactions. This method is so fast that it is possible to compute 500 permutations to evaluate non-parametric significance of the joint LRS value within a minute. This makes DIRECT ideal for an interactive web service. Karl Broman's R/qtl implements an exhaustive search using the "scantwo" function. [RWW, May 2011]
+GeneNetwork implements a rapid but non-exhaustive DIRECT algorithm (Lundberg et al., [2004](http://bioinformatics.oxfordjournals.org/content/20/12/1887.full.pdf)) that efficiently searches for epistatic interactions. This method is so fast that it is possible to compute 500 permutations to evaluate non-parametric significance of the joint LRS value within a minute. This makes DIRECT ideal for an interactive web service. Karl Broman's [R/qtl](http://www.rqtl.org/tutorials/rqtltour.pdf) implements an exhaustive search using the "scantwo" function. [RWW, May 2011]
 
 #### Partial Correlation:
 
-Partial correlation is the correlation between two variables that remains after controlling for one or more other variables. Idea and techniques used to compute partial correlations are important in testing causal models (Cause and Correlation in Biology, Bill Shipley, 2000). For instance, r1,2||3,4 is the partial correlation between variables 1 and 2, while controlling for variables 3 and 4 (the || symbol is equivalent to "while controlling for"). We can compare partial correlations (e.g., r1,2||3,4) with original correlations (e.g., r1,2). If there is an insignificant difference, we infer that the controlled variables have minimal effect and may not influence the variables or even be part of the model. In contrast, if the partial correlations change significantly, the inference is that the causal link between the two variables is dependent to some degree on the controlled variables. These control variables are either anteceding causes or intervening variables. (text adapted from D Garson's original by RWW).
+Partial correlation is the correlation between two variables that remains after controlling for one or more other variables. Idea and techniques used to compute partial correlations are important in testing causal models ([Cause and Correlation in Biology](http://www.amazon.com/Cause-Correlation-Biology-Structural-Equations/dp/0521529212), Bill Shipley, 2000). For instance, r1,2||3,4 is the partial correlation between variables 1 and 2, while controlling for variables 3 and 4 (the || symbol is equivalent to "while controlling for"). We can compare partial correlations (e.g., r1,2||3,4) with original correlations (e.g., r1,2). If there is an insignificant difference, we infer that the controlled variables have minimal effect and may not influence the variables or even be part of the model. In contrast, if the partial correlations change significantly, the inference is that the causal link between the two variables is dependent to some degree on the controlled variables. These control variables are either anteceding causes or intervening variables. (text adapted from D Garson's original by RWW).
 
-For more on partial correlation please link to this great site by David Garson at NC State.
+For more on [partial correlation](http://faculty.chass.ncsu.edu/garson/PA765/partialr.htm) please link to this great site by David Garson at NC State.
 
-For more on dependence separation ( d-separation) and constructing causal models see Richard Scheines' site.
+For more on dependence separation ([d-separation](http://www.andrew.cmu.edu/user/scheines/tutor/d-sep.html)) and constructing causal models see Richard Scheines' site.
 
 Why would you use of need partial correlations in GeneNetwork? It is often useful to compute correlations among traits while controlling for additional variables. Partial correlations may reveal more about the causality of relations. In a genetic context, partial correlations can be used to remove much of the variance associated with linkage and linkage disequilibrium. You can also control for age, age, and other common cofactors.
 
@@ -425,19 +427,19 @@ Please see the related Glossary terms "Tissue Correlation". [RWW, Aug 21, 2009;
 
 #### PCA Trait or Eigentrait:
 
-If you place a number of traits in a Trait Collection you can carry out some of the key steps of a principal component analysis, including defining the variance directed along specific principal component eigenvectors. You can also plot the positions of cases against the first two eigenvectors; in essence a type of scatterplot. Finally, GeneNetwork allows you to exploit PCA methods to make new "synthetic" eigentraits from collections of correlated traits. These synthetic traits are the values of cases along specific eigenvectors and they may be less noisy than single traits. If this seems puzzling, then have a look at these useful PCA explanation by G. Dallas and by Powell and Lehe. How to do it: You can select and assemble many different traits into a single Trait Collection window using the check boxes and Add To Collection buttons. One of the most important function buttons in the Collection window is labeled Correlation Matrix. This function computes Pearson product moment correlations and Spearman rank order correlations for all possible pairs of traits in the Collection window. It also perfoms a principal component or factor analysis. For example, if you have 20 traits in the Collection window, the correlation matrix will consist of 20*19 or 190 correlations and the identity diagonal. Principal components analysis is a linear algebraic procedure that finds a small number of independent factors or principal components that efficiently explain variation in the original 20 traits. It is a effective method to reduce the dimensionality of a group of traits. If the 20 traits share a great deal of variation, then only two or three factors may explain variation among the traits. Instead of analyzing 20 traits as if they were independent, we can now analyze the main principal components labeled PC01, PC02, etc. PC01 and PC02 can be treated as new synthetic traits that represent the main sources of variation among original traits. You can treat a PC trait like any other trait except that it is not stored permanently in a database table. You can put a PC trait in your Collection window and see how well correlated each of the 20 original traits is with this new synthetic trait. You can also map a PC trait. [RWW, Aug 23, 2005]
+If you place a number of traits in a Trait Collection you can carry out some of the key steps of a principal component analysis, including defining the variance directed along specific principal component eigenvectors. You can also plot the positions of cases against the first two eigenvectors; in essence a type of scatterplot. Finally, GeneNetwork allows you to exploit PCA methods to make new "synthetic" eigentraits from collections of correlated traits. These synthetic traits are the values of cases along specific eigenvectors and they may be less noisy than single traits. If this seems puzzling, then have a look at these useful PCA explanation by [G. Dallas](http://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/) and by [Powell and Lehe](http://setosa.io/ev/principal-component-analysis/). **How to do it:** You can select and assemble many different traits into a single **Trait Collection** window using the check boxes and **Add To Collection** buttons. One of the most important function buttons in the **Collection** window is labeled **Correlation Matrix**. This function computes Pearson product moment correlations and Spearman rank order correlations for all possible pairs of traits in the Collection window. It also perfoms a principal component or factor analysis. For example, if you have 20 traits in the Collection window, the correlation matrix will consist of 20*19 or 190 correlations and the identity diagonal. Principal components analysis is a linear algebraic procedure that finds a small number of independent factors or principal components that efficiently explain variation in the original 20 traits. It is a effective method to reduce the dimensionality of a group of traits. If the 20 traits share a great deal of variation, then only two or three factors may explain variation among the traits. Instead of analyzing 20 traits as if they were independent, we can now analyze the main principal components labeled PC01, PC02, etc. PC01 and PC02 can be treated as new synthetic traits that represent the main sources of variation among original traits. You can treat a PC trait like any other trait except that it is not stored permanently in a database table. You can put a PC trait in your Collection window and see how well correlated each of the 20 original traits is with this new synthetic trait. You can also map a PC trait. [RWW, Aug 23, 2005]
 
 
    
 
 #### Permutation Test:
 
-A permutation test is a computationally intensive but conceptually simple method used to evaluate the statisical significance of findings. Permutation tests are often used to evaluate QTL significance. Some background: In order to detect parts of chromosomes that apparently harbor genes that contribute to differences in a trait's value, it is common to search for associations (linkage) across the entire genome. This is referred to as a "whole genome" scan, and it usually involves testing hundreds of independently segregating regions of the genome using hundreds, or even thousands of genetic markers (SNPs and microsatellites). A parametric test such as a conventional t test of F test can be used to estimate the probability of the null hypothesis at any single location in the genome (the null hypothesis is that there is no QTL at this particular location). But a parametric test of this type makes assumptions about the distribution of the trait (its normality), and also does not provide a way to correct for the large number of independent tests that are performed while scanning the whole genome. We need protection against many false discoveries as well as some assurance that we are not neglecting truly interesting locations. A permutation test is an elegant solution to both problems. The procedure involves randomly reassigning (permuting) traits values and genotypes of all cases used in the analysis. The permuted data sets have the same set of phenotypes and genotypes (in other words, distributions are the same), but obviously the permutation procedure almost invariably obliterates genuine gene-to-phenotype relation in large data sets. We typically generate several thousand permutations of the data. Each of these is analyzed using precisely the same method that was used to analyze the correctly ordered data set. We then compare statistical results of the original data set with the collection of values generated by the many permuted data sets. The hope is that the correctly ordered data are associated with larger LRS and LOD values than more than 95% of the permuted data sets. This is how we define the p = .05 whole genome significance threshold for a QTL. Please see the related Glossary terms "Significant threshold" and "Suggestive threshold". [RWW, July 15, 2005]
+A permutation test is a computationally intensive but conceptually simple method used to evaluate the statisical significance of findings. Permutation tests are often used to evaluate QTL significance. _Some background_: In order to detect parts of chromosomes that apparently harbor genes that contribute to differences in a trait's value, it is common to search for associations (linkage) across the entire genome. This is referred to as a "whole genome" scan, and it usually involves testing hundreds of independently segregating regions of the genome using hundreds, or even thousands of genetic markers (SNPs and microsatellites). A parametric test such as a conventional t test of F test can be used to estimate the probability of the null hypothesis at any single location in the genome (the null hypothesis is that there is no QTL at this particular location). But a parametric test of this type makes assumptions about the distribution of the trait (its normality), and also does not provide a way to correct for the large number of independent tests that are performed while scanning the whole genome. We need protection against many false discoveries as well as some assurance that we are not neglecting truly interesting locations. A permutation test is an elegant solution to both problems. The procedure involves randomly reassigning (permuting) traits values and genotypes of all cases used in the analysis. The permuted data sets have the same set of phenotypes and genotypes (in other words, distributions are the same), but obviously the permutation procedure almost invariably obliterates genuine gene-to-phenotype relation in large data sets. We typically generate several thousand permutations of the data. Each of these is analyzed using precisely the same method that was used to analyze the correctly ordered data set. We then compare statistical results of the original data set with the collection of values generated by the many permuted data sets. The hope is that the correctly ordered data are associated with larger LRS and LOD values than more than 95% of the permuted data sets. This is how we define the p = .05 whole genome significance threshold for a QTL. Please see the related Glossary terms "Significant threshold" and "Suggestive threshold". [RWW, July 15, 2005]
 
 #### Power to detect QTLs:
 
 An analysis of statistical power is useful to estimate numbers of replicates and strains needed to detect and resolve major sources of trait variance and covariance. A versatile method has been developed by Sen and colleagues (Sen et al., 2007) and implemented in the R program. qtlDesign. David Ashbrook implemented a version of this within Shiny that can help you estimate power for different QTL effect sizes, cohort sizes, and replication rates:
 
-#### Power Calculator (D. Ashbrook)
+#### [Power Calculator (D. Ashbrook)](https://dashbrook1.shinyapps.io/bxd_power_calculator_app/)
 
 We can see that in all situations power is increased more by increasing the number of lines than by increasing the number of biological replicates. Dependent upon the heritability of the trait, there is little gain in power when going above 4-6 biological replicates. [DGA, Mar 3, 2018] 
 
@@ -466,34 +468,38 @@ A quantitative trait locus is a chromosome region that contains one or more sequ
 
 An inbred strain whose chromosomes incorporate a fixed and permanent set of recombinations of chromosomes originally descended from two or more parental strains. Sets of RI strains (from 10 to 5000) are often used to map the chromosomal positions of polymorphic loci that control variance in phenotypes.
 
-For a terrific short summary of the uses of RI strains see 2007).
+For a terrific short summary of the uses of RI strains see [2007](http://www.informatics.jax.org/silverbook/chapters/9-2.shtml)).
 
 Chromosomes of RI strains typically consist of alternating haplotypes of highly variable length that are inherited intact from the parental strains. In the case of a typical rodent RI strain made by crossing maternal strain C with paternal strain B (called a CXB RI strain), a chromosome will typically incorporate 3 to 5 alternating haplotype blocks with a structure such as BBBBBCCCCBBBCCCCCCCC, where each letter represents a genotype, series of similar genotype represent haplotypes, and where a transition between haplotypes represents a recombination. Both pairs of each chromosome will have the same alternating pattern, and all markers will be homozygous. Each of the different chromosomes (Chr 1, Chr 2, etc.) will have a different pattern of haplotypes and recombinations. The only exception is that the Y chromosome and the mitochondial genome, both of which are inherited intact from the paternal and maternal strain, respectively. For an RI strain to be useful for mapping purposes, the approximate position of recombinations along each chromsome need to be well defined either in terms of centimorgan or DNA basepair position. The precision with which these recombinations are mapped is a function of the number and position of the genotypes used to type the chromosomes--20 in the example above. Because markers and genotypes are often space quite far apart, often more than 500 Kb, the actual data entered into GeneNetwork will have some ambiguity at each recombination locus. The haplotype block BBBBBCCCCBBBCCCCCCCC will be entered as BBBBB?CCCC?BBB?CCCCCCCC where the ? mark indicates incomplete information over some (we hope) short interval.
 
 RI strains are almost always studied in sets or panels. All else being equal, the larger the set of RI strains, the greater the power and precision with which phenotypes can be mapped to chromosomal locations. The first set of eight RIs, the CXB RIs, were generated by Donald Bailey (By) from an intercross between a female BALB/cBy mouse (abbreviated C) and a male C57BL/6By mouse in the 1960s. The small panel of 8 CXB strains was originally used to determine if the major histocompatibility (MHC) locus on proximal Chr 17 was a key factor accounting for different immune responses such as tissue rejection. The methods used to determine the locations of recombinations relied on visible markers (coat color phenotypes such as the C and B loci) and the electrophoretic mobility of proteins. Somewhat larger RI sets were generated by Benjamin Taylor to map Mendelian and other major effect loci. In the 1990s the utility of RI sets for mapping was significantly improved thanks to higher density genotypes made possible by the use of microsatellite markers. Between 2005 and 2017, virtually all extant mouse and rat RI strains were regenotyped at many thousands of SNP markers, providing highly accurate maps of recombinations.
 
-While the potential utility of RI strains in mapping complex polygenic traits was obvious from the outset, the small number of strains only made it feasible to map quantitative traits with large effects. The first large RI sets were generated by plant geneticists (Burr et al. 2000) and this the plant genetics community holds a strong lead in the production of very large RI sets to study multigenic and polygenic traits and trait covariance and pleiotropy.
+While the potential utility of RI strains in mapping complex polygenic traits was obvious from the outset, the small number of strains only made it feasible to map quantitative traits with large effects. The first large RI sets were generated by plant geneticists (Burr et al. [2000](http://demeter.bio.bnl.gov/RIchap_rev.pdf)) and this the plant genetics community holds a strong lead in the production of very large RI sets to study multigenic and polygenic traits and trait covariance and pleiotropy.
 
 By 2010 the number of mouse RI strains had increased to the point where defining causal gene and sequence variant was more practical. As of 2018 there are about 150 BXD strains (152 have been fully sequenced), ~100 Collaborative Cross strains (also all fully sequenced), and at least another 100 RI strains belonging to smaller sets that have been extremely well genotyped.
 
-Making RI strains: The usual procedure typically involves sib mating of the progeny of an F1 intercross for more than 20 generations. Even by the 5th filial (F) generation of successive matings, the RI lines are homozygous at 50% of loci and by F13, the value is above 90%. At F20 the lines are nearly fully inbred (~98%) and by convention are now referred to as inbred strains rather than inbred lines. 
+**Making RI strains**: The usual procedure typically involves sib mating of the progeny of an F1 intercross for more than 20 generations. Even by the 5th filial (F) generation of successive matings, the RI lines are homozygous at 50% of loci and by F13, the value is above 90%. At F20 the lines are nearly fully inbred (~98%) and by convention are now referred to as inbred strains rather than inbred lines. 
 
 
 [Go back to index](#index)
 
+Legend:from [Silver, L. (1995) Oxford University Press](http://www.informatics.jax.org/silverbook/frames/frame3-3.shtml)
+
+[Williams RW, June 20, 2005; significant extension, Sept 21, 2007, added Crow ref, Oct 2009]
+
 
    
 
 ## S
 
 #### Scree Plots:
 
-GeneNetwork will often automatically generate a Scree Plot and the associated principal components (PCs) when you compute a Correlation Matrix for a group of traits that you have placed in your Trait Collection (a set of phenotypes and/or expression data for a specific population). Here is a nice definition of what a Scree plot is trying to tell you adopted and adapted from IOS (www.improvedoutcomes.com).
+GeneNetwork will often automatically generate a [Scree Plot](http://www.improvedoutcomes.com/docs/WebSiteDocs/PCA/Creating_a_Scree_Plot.htm) and the associated principal components (PCs) when you compute a Correlation Matrix for a group of traits that you have placed in your Trait Collection (a set of phenotypes and/or expression data for a specific population). Here is a nice definition of what a Scree plot is trying to tell you adopted and adapted from IOS (www.improvedoutcomes.com).
 
 A Scree Plot is a simple line segment plot that shows the fraction of total variance in the data as explained or represented by each PC. The PCs are ordered, and by definition are therefore assigned a number label, by decreasing order of contribution to total variance. The PC with the largest fraction contribution is labeled PC01. Such a plot when read left-to-right across the abscissa can often show a clear separation in fraction of total variance where the 'most important' components cease and the 'least important' components begin. The point of separation is often called the 'elbow'. (In the PCA literature, the plot is called a 'Scree' Plot because it often looks like a 'scree' slope, where rocks have fallen down and accumulated on the side of a mountain.) [Williams RW, Dec 20, 2008]
 
 #### Significant threshold:
 
-The significant threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.05, or a 5% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This threshold is computed by evaluating the distribution of highest LRS scores generated by a set of 2000 random permutations of strain means. For example, a random permutation of the correctly ordered data may give a peak LRS score of 10 somewhere across the genome. The set of 1000 or more of these highest LRS scores is then compared to the actual LRS obtained for the correctly ordered (real) data at any location in the genome. If fewer than 50 (5%) of the 1000 permutations have peak LRS scores anywhere in the genome that exceed that obtained at a particular locus using the correctly ordered data, then one can usually claim that a QTL has been defined at a genome-wide p-value of .05. The threshold will vary slightly each time it is recomputed due to the random generation of the permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the Analysis Tools area of the Trait Data and Editing Form. WebQTL does make it possible to search through hundreds of traits for those that may have significant linkage somewhere in the genome. Keep in mind that this introduces a second tier of multiple testing problems for which the permutation test will not usually provide adequate protection. If you anticipate mapping many independent traits, then you will need to correct for the number of traits you have tested. [Williams RW, Nov 14, 2004]
+The significant threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.05, or a 5% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This threshold is computed by evaluating the distribution of highest LRS scores generated by a set of 2000 random permutations of strain means. For example, a random permutation of the correctly ordered data may give a peak LRS score of 10 somewhere across the genome. The set of 1000 or more of these highest LRS scores is then compared to the actual LRS obtained for the correctly ordered (real) data at any location in the genome. If fewer than 50 (5%) of the 1000 permutations have peak LRS scores anywhere in the genome that exceed that obtained at a particular locus using the correctly ordered data, then one can usually claim that a QTL has been defined at a genome-wide p-value of .05. The threshold will vary slightly each time it is recomputed due to the random generation of the permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the **Analysis Tools** area of the **Trait Data and Editing Form**. WebQTL does make it possible to search through hundreds of traits for those that may have significant linkage somewhere in the genome. Keep in mind that this introduces a second tier of multiple testing problems for which the permutation test will not usually provide adequate protection. If you anticipate mapping many independent traits, then you will need to correct for the number of traits you have tested. [Williams RW, Nov 14, 2004]
 
 
    
 
@@ -513,11 +519,11 @@ where n is the number of independent biological samples used to estimate the pop
 
 #### Strain Distribution Pattern:
 
-A marker such as a SNP or microsatellite is genotyped using DNA obtained from each member of the mapping population. In the case of a genetic reference population, such as the BXD strains or the BayXSha Arabadopsis lines, this results in a text string of genotypes (e.g., BDDDBDBBBBDDBDDDBBBB... for BXD1 through BXD100). Each marker is associated with its own particular text string of genotypes that is often called the strain distribution pattern of the marker. (A more appropriate term would be the marker genotype string.) This string is converted to a numerical version, a genotype vector: -1111-11-1-1-1-111-1111-1-1-1-1..., where D=1, B=-1, H=0. Mapping a trait boils down to performing correlations between each trait and all of the genotype vectors. The genotype vector with the highest correlation (absolute value) is a good candidate for a QTL. [Williams RW, June 18, 2005] 
+A marker such as a SNP or microsatellite is genotyped using DNA obtained from each member of the mapping population. In the case of a genetic reference population, such as the BXD strains or the BayXSha Arabadopsis lines, this results in a text string of genotypes (e.g., BDDDBDBBBBDDBDDDBBBB... for BXD1 through BXD100). Each marker is associated with its own particular text string of genotypes that is often called the **strain distribution pattern** of the marker. (A more appropriate term would be the **marker genotype string**.) This string is converted to a numerical version, a genotype vector: -1111-11-1-1-1-111-1111-1-1-1-1..., where D=1, B=-1, H=0. Mapping a trait boils down to performing correlations between each trait and all of the genotype vectors. The genotype vector with the highest correlation (absolute value) is a good candidate for a QTL. [Williams RW, June 18, 2005] 
 
 #### Suggestive Threshold:
 
-The suggestive threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.63, or a 63% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This is not a typographical error. The Suggestive LRS threshold is defined as that which yields, on average, one false positive per genome scan. That is, roughly one-third of scans at this threshold will yield no false positive, one-third will yield one false positive, and one-third will yield two or more false positives. This is a very permissive threshold, but it is useful because it calls attention to loci that may be worth follow-up. Regions of the genome in which the LRS exceeds the suggestive threshold are often worth tracking and screening. They are particularly useful in combined multicross metaanalysis of traits. If two crosses pick up the same suggestive locus, then that locus may be significant when the joint probability is computed. The suggestive threshold may vary slightly each time it is recomputed due to the random generation of permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the Analysis Tools area of the Trait Data and Editing Form. [Williams RW and Manly KF, Nov 15, 2004] 
+The suggestive threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.63, or a 63% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This is not a typographical error. The Suggestive LRS threshold is defined as that which yields, on average, one false positive per genome scan. That is, roughly one-third of scans at this threshold will yield no false positive, one-third will yield one false positive, and one-third will yield two or more false positives. This is a very permissive threshold, but it is useful because it calls attention to loci that may be worth follow-up. Regions of the genome in which the LRS exceeds the suggestive threshold are often worth tracking and screening. They are particularly useful in combined multicross metaanalysis of traits. If two crosses pick up the same suggestive locus, then that locus may be significant when the joint probability is computed. The suggestive threshold may vary slightly each time it is recomputed due to the random generation of permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the **Analysis Tools** area of the **Trait Data and Editing Form**. [Williams RW and Manly KF, Nov 15, 2004] 
 
 #### Systems Genetics:
 
@@ -525,7 +531,7 @@ Systems genetics or "network genetics" is an emerging new branch of genetics tha
 
 A hallmark of systems genetics is the simultaneous consideration of groups (systems) of phenotypes from the primary level of molecular and cellular interactions that ultimately modulate global phenotypes such as blood pressure, behavior, or disease resistance. Changes in environment are also often important determinants of multiscalar phenotypes; reversing the standard notion of causality as flowing inexorably upward from the genome. Scientists who use a systems genetics approach often have a broad interest in modules of linked phenotypes. Causality in these complex dynamic systems is often contingent on environmental or temporal context, and often will involve feedback modulation. A systems genetics approach can be unusually powerful, but does require the use of large numbers of observations (large sample size), and more advanced statistical and computational models.
 
-Systems genetics is not really a new field and traces back to Sewall Wright's classical paper (Wright, 1921, "Correlation and Causation") that introduced path analysis to study systems of related phenotypes. Two factors have invigorated this field. The first factor is the advent of more sophisticated statistical methods including Structural Equation Modeling (SEM), System Dynamics Modeling, and Bayesian Network Modeling combined with powerful computer systems and efficient algorithms. The second factor is the relative ease with which it is now possible to acquire extensive and diverse phenotype data sets across genetic reference populations such as the BXD set of mice, the HXB set of rats, and the BayXSha lines of Arabidopsis (data are incorporated in the GeneNetwork). In the case of the BXD strains, a large research community has collectively generated hundreds of thousands of transcript phenotypes in different tissues and cells (level of expression), as well as hundreds of protein, cellular, pharmacological, and behavioral data types across a single genetic reference panel. Evaluating and modeling the associative and causal relations among these phenotypes is a major, and still relatively new area of research. Complex trait analysis and QTL mapping are both part of systems genetics in which causality is inferred using conventional genetic linkage (Li et al., 2005). One can often assert with confidence that a particular module of phenotypes (component of the variance and covariance) is modulated by sequence variants at a common locus. This provides a causal constraint that can be extremely helpful in more accurately modeling network architecture. Most models are currently static, but as the field matures, more sophisticated dynamic models will supplant steady-state models.
+Systems genetics is not really a new field and traces back to [Sewall Wright's](http://www.amphilsoc.org/library/mole/w/wrights.htm) classical paper (Wright, 1921, "Correlation and Causation") that introduced path analysis to study systems of related phenotypes. Two factors have invigorated this field. The first factor is the advent of more sophisticated statistical methods including Structural [Equation Modeling](http://userwww.sfsu.edu/~efc/classes/biol710/path/SEMwebpage.htm) (SEM), [System Dynamics Modeling](http://www.public.asu.edu/~kirkwood/sysdyn/SDIntro/SDIntro.htm), and [Bayesian Network Modeling](http://bnj.sourceforge.net/) combined with powerful computer systems and efficient algorithms. The second factor is the relative ease with which it is now possible to acquire extensive and diverse phenotype data sets across genetic reference populations such as the BXD set of mice, the HXB set of rats, and the BayXSha lines of Arabidopsis (data are incorporated in the GeneNetwork). In the case of the BXD strains, a large research community has collectively generated hundreds of thousands of transcript phenotypes in different tissues and cells (level of expression), as well as hundreds of protein, cellular, pharmacological, and behavioral data types across a single genetic reference panel. Evaluating and modeling the associative and causal relations among these phenotypes is a major, and still relatively new area of research. Complex trait analysis and QTL mapping are both part of systems genetics in which causality is inferred using conventional genetic linkage (Li et al., [2005](http://hmg.oupjournals.org/cgi/content/abstract/ddi124v1)). One can often assert with confidence that a particular module of phenotypes (component of the variance and covariance) is modulated by sequence variants at a common locus. This provides a causal constraint that can be extremely helpful in more accurately modeling network architecture. Most models are currently static, but as the field matures, more sophisticated dynamic models will supplant steady-state models.
 
 The term "systems genetics" was coined by Grant Morahan, October 2004, during a visit to Memphis, as a more general and appropriate term to use instead of "genetical genomics." [Williams RW, April 11, 2005, revised Oct 22, 2005, April, 2008]
 
@@ -579,13 +585,13 @@ Transgression means that you can rarely predict the distribution of phenotypes a
 
 #### Winsorize, Winsorise:
 
-QTL mapping results can be greatly affected by inclusion of outlier data. GeneNetwork will do its best to flag outliers for you in the Trait Data and Analysis pages (yellow highlighting). Before mapping, review the data, and if necessary, change values. Options for handling outliers include: (1) do nothing, (2) delete the outliers (trimming), (3) transform the data (e.g., logarithmic, arcsine, or logistic regression transforms), or (4) winsorize the distribution of values. Winsorizing is usually the easiest method to implement directly in GeneNetwork.
+QTL mapping results can be greatly affected by inclusion of outlier data. GeneNetwork will do its best to flag outliers for you in the **Trait Data and Analysis** pages (yellow highlighting). Before mapping, review the data, and if necessary, change values. Options for handling outliers include: (1) do nothing, (2) delete the outliers (trimming), (3) transform the data (e.g., logarithmic, arcsine, or logistic regression transforms), or (4) [winsorize](http://en.wikipedia.org/wiki/Winsorising) the distribution of values. Winsorizing is usually the easiest method to implement directly in GeneNetwork.
 
-How to winsorize: First review the distribution of values and define outliers. You should only do this one time, so think before you leap. Look at the Probability Plot of the trait by going to Trait Data and Analysis page and selecting Basic Statistics). For example, the figure below from GeneNetwork shows that at many as seven cases have relatively high values and as many as three have relatively low values (this trait is taken from Species = Mouse, Group = LXS, Type = Phenotype, Trait 10182). GeneNetwork code only declares the highest two values to be outliers, but you can use a more liberal definition and give all seven high values a haircut. It is advisable to winsorizes equal numbers of cases on each side of the distribution (high and low cases). In this case, the seven highest values were changed to match that of the 8th highest value (0.860). To retain the original rank order I added an incremental value of 0.01 to each (0.861, 0.862, etc). I did the same thing to the lowest seven values. Adding this increment is not necessary.
+**How to winsorize**: First review the distribution of values and define outliers. You should only do this one time, so think before you leap. Look at the **Probability Plot** of the trait by going to **Trait Data and Analysis** page and selecting **Basic Statistics**). For example, the figure below from GeneNetwork shows that at many as seven cases have relatively high values and as many as three have relatively low values (this trait is taken from Species = Mouse, Group = LXS, Type = Phenotype, Trait 10182). GeneNetwork code only declares the highest two values to be outliers, but you can use a more liberal definition and give all seven high values a haircut. It is advisable to winsorizes equal numbers of cases on each side of the distribution (high and low cases). In this case, the seven highest values were changed to match that of the 8th highest value (0.860). To retain the original rank order I added an incremental value of 0.01 to each (0.861, 0.862, etc). I did the same thing to the lowest seven values. Adding this increment is not necessary.
 
 The result in this case: a suggestive QTL on Chr 16 now reaches the significance threshold.
 
-The danger of winsorizing is doing it multiple times in different ways. You should transform or winsorize the data before mapping. And you should ideally only do any transformation/correction one time. If you fool around with different methods of transforming your data then you are asking for trouble by adding yet another level of multiple testing. If you feel compelled to experiment with different transforms, then you should/must report this in publications and explain why you did so. Demonstrating that mapping results are robust even using multiple transforms is one good excuse. [Williams RW, Jan 2, 2014]
+The **danger of winsorizing** is doing it multiple times in different ways. You should transform or winsorize the data before mapping. And you should ideally only do any transformation/correction one time. If you fool around with different methods of transforming your data then you are asking for trouble by adding yet another level of multiple testing. If you feel compelled to experiment with different transforms, then you should/must report this in publications and explain why you did so. Demonstrating that mapping results are robust even using multiple transforms is one good excuse. [Williams RW, Jan 2, 2014]
 
 
 
-- 
cgit v1.2.3


From 96dd0a10f0bfe66afab9701dd028f169d63cdae5 Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Tue, 10 Nov 2020 19:56:07 +0300
Subject: add tests for write input for browser in
 marker_regression/run_mapping.py

---
 .../wqflask/marker_regression/test_run_mapping.py  | 277 +++++++++++----------
 1 file changed, 144 insertions(+), 133 deletions(-)

diff --git a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
index e925ad28..6521c41d 100644
--- a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
@@ -24,9 +24,11 @@ class MockDataSetGroup(AttributeSetter):
 class TestRunMapping(unittest.TestCase):
     def setUp(self):
         self.group = MockDataSetGroup(
-            {"genofile": "~/genofiles/g1_file", "name": "GP1_","species":"Human"})
-        self.dataset = AttributeSetter({"fullname":"dataser_1","group": self.group,"type":"ProbeSet"})
-        self.trait=AttributeSetter({"symbol":"IGFI","chr":"X1","mb":123313})
+            {"genofile": "~/genofiles/g1_file", "name": "GP1_", "species": "Human"})
+        self.dataset = AttributeSetter(
+            {"fullname": "dataser_1", "group": self.group, "type": "ProbeSet"})
+        self.trait = AttributeSetter(
+            {"symbol": "IGFI", "chr": "X1", "mb": 123313})
 
     def tearDown(self):
         self.dataset = AttributeSetter(
@@ -51,137 +53,146 @@ class TestRunMapping(unittest.TestCase):
         self.assertEqual(results_with_error, "False")
         self.assertEqual(results_no_error, "True")
 
-    
     def test_trim_markers_for_figure(self):
 
-
-    	markers=[{
-    	"name":"MK1",
-    	"chr":"C1",
-    	"cM":"1",
-    	"Mb":"12000",
-    	"genotypes":[],
-    	"dominance":"TT",
-    	"additive":"VA",
-    	"lod_score":0.5
-    	},
-    	{
-    	"name":"MK2",
-    	"chr":"C2",
-    	"cM":"15",
-    	"Mb":"10000",
-    	"genotypes":[],
-    	"lod_score":0.7
-    	},
-    	{
-    	"name":"MK1",
-    	"chr":"C3",
-    	"cM":"45",
-    	"Mb":"1",
-    	"genotypes":[],
-    	"dominance":"Tt",
-    	"additive":"VE",
-    	"lod_score":1
-    	}]
-
-    	marker_2=[{
-    	"name":"MK1",
-    	"chr":"C1",
-    	"cM":"1",
-    	"Mb":"12000",
-    	"genotypes":[],
-    	"dominance":"TT",
-    	"additive":"VA",
-    	"p_wald":4.6
-    	}]
-    	results=trim_markers_for_figure(markers)
-    	result_2=trim_markers_for_figure(marker_2)
-    	expected=[
-    	{
-    	"name":"MK1",
-    	"chr":"C1",
-    	"cM":"1",
-    	"Mb":"12000",
-    	"genotypes":[],
-    	"dominance":"TT",
-    	"additive":"VA",
-    	"lod_score":0.5
-    	},
-    	{
-    	"name":"MK1",
-    	"chr":"C3",
-    	"cM":"45",
-    	"Mb":"1",
-    	"genotypes":[],
-    	"dominance":"Tt",
-    	"additive":"VE",
-    	"lod_score":1
-    	}
-
-    	]
-    	self.assertEqual(results,expected)
-    	self.assertEqual(result_2,marker_2)
+        markers = [{
+            "name": "MK1",
+            "chr": "C1",
+            "cM": "1",
+            "Mb": "12000",
+            "genotypes": [],
+            "dominance":"TT",
+            "additive":"VA",
+            "lod_score":0.5
+        },
+            {
+            "name": "MK2",
+            "chr": "C2",
+            "cM": "15",
+            "Mb": "10000",
+            "genotypes": [],
+            "lod_score":0.7
+        },
+            {
+            "name": "MK1",
+            "chr": "C3",
+            "cM": "45",
+            "Mb": "1",
+            "genotypes": [],
+            "dominance":"Tt",
+            "additive":"VE",
+            "lod_score":1
+        }]
+
+        marker_2 = [{
+            "name": "MK1",
+            "chr": "C1",
+            "cM": "1",
+            "Mb": "12000",
+            "genotypes": [],
+            "dominance":"TT",
+            "additive":"VA",
+            "p_wald":4.6
+        }]
+        results = trim_markers_for_figure(markers)
+        result_2 = trim_markers_for_figure(marker_2)
+        expected = [
+            {
+                "name": "MK1",
+                "chr": "C1",
+                "cM": "1",
+                "Mb": "12000",
+                "genotypes": [],
+                "dominance":"TT",
+                "additive":"VA",
+                "lod_score":0.5
+            },
+            {
+                "name": "MK1",
+                "chr": "C3",
+                "cM": "45",
+                "Mb": "1",
+                "genotypes": [],
+                "dominance":"Tt",
+                "additive":"VE",
+                "lod_score":1
+            }
+
+        ]
+        self.assertEqual(results, expected)
+        self.assertEqual(result_2, marker_2)
 
     def test_export_mapping_results(self):
-    	datetime_mock=mock.Mock(wraps=datetime.datetime)
-    	datetime_mock.now.return_value=datetime.datetime(2019,9,1,10,12,12)
-
-    	markers=[{
-    	"name":"MK1",
-    	"chr":"C1",
-    	"cM":"1",
-    	"Mb":"12000",
-    	"genotypes":[],
-    	"dominance":"TT",
-    	"additive":"VA",
-    	"lod_score":3
-    	},
-    	{
-    	"name":"MK2",
-    	"chr":"C2",
-    	"cM":"15",
-    	"Mb":"10000",
-    	"genotypes":[],
-    	"lod_score":7
-    	},
-    	{
-    	"name":"MK1",
-    	"chr":"C3",
-    	"cM":"45",
-    	"Mb":"1",
-    	"genotypes":[],
-    	"dominance":"Tt",
-    	"additive":"VE",
-    	"lod_score":7
-    	}]
-
-    	with mock.patch("builtins.open", mock.mock_open()) as mock_open:
-    		
-    		# mock_open.assert_called_once_with("~/results","w+")
-    		# filehandler=mock_open()
-    		with mock.patch("wqflask.marker_regression.run_mapping.datetime.datetime",new=datetime_mock):
-    			export_mapping_results(dataset=self.dataset,trait=self.trait,markers=markers,results_path="~/results",mapping_scale="physic",score_type="-log(p)")
-
-    		
-	    		write_calls=[
-	    		mock.call('Time/Date: 09/01/19 / 10:12:12\n'),
-	    		 mock.call('Population: Human GP1_\n'),mock.call('Data Set: dataser_1\n'),
-				 mock.call('Gene Symbol: IGFI\n'), mock.call('Location: X1 @ 123313 Mb\n'),
-				 mock.call('\n'), mock.call('Name,Chr,'),
-				 mock.call('Mb,-log(p)'), mock.call('Cm,-log(p)'),
-				 mock.call(',Additive'),mock.call(',Dominance'),
-				 mock.call('\n'),mock.call('MK1,C1,'),
-				 mock.call('12000,'), mock.call('1,'),
-				 mock.call('3'), mock.call(',VA'),
-				 mock.call(',TT'),mock.call('\n'),
-				 mock.call('MK2,C2,'),mock.call('10000,'),
-				 mock.call('15,'), mock.call('7'),
-				 mock.call('\n'), mock.call('MK1,C3,'),
-				 mock.call('1,'),mock.call('45,'),
-				 mock.call('7'), mock.call(',VE'),
-				 mock.call(',Tt')
-
-	    		]
-	    		mock_open.assert_called_once_with("~/results","w+")
-	    		filehandler=mock_open()
-	    		filehandler.write.assert_has_calls(write_calls)
\ No newline at end of file
+        datetime_mock = mock.Mock(wraps=datetime.datetime)
+        datetime_mock.now.return_value = datetime.datetime(
+            2019, 9, 1, 10, 12, 12)
+
+        markers = [{
+            "name": "MK1",
+            "chr": "C1",
+            "cM": "1",
+            "Mb": "12000",
+            "genotypes": [],
+            "dominance":"TT",
+            "additive":"VA",
+            "lod_score":3
+        },
+            {
+            "name": "MK2",
+            "chr": "C2",
+            "cM": "15",
+            "Mb": "10000",
+            "genotypes": [],
+            "lod_score":7
+        },
+            {
+            "name": "MK1",
+            "chr": "C3",
+            "cM": "45",
+            "Mb": "1",
+            "genotypes": [],
+            "dominance":"Tt",
+            "additive":"VE",
+            "lod_score":7
+        }]
+
+        with mock.patch("builtins.open", mock.mock_open()) as mock_open:
+
+            with mock.patch("wqflask.marker_regression.run_mapping.datetime.datetime", new=datetime_mock):
+                export_mapping_results(dataset=self.dataset, trait=self.trait, markers=markers,
+                                       results_path="~/results", mapping_scale="physic", score_type="-log(p)")
+
+                write_calls = [
+                    mock.call('Time/Date: 09/01/19 / 10:12:12\n'),
+                    mock.call('Population: Human GP1_\n'), mock.call(
+                        'Data Set: dataser_1\n'),
+                    mock.call('Gene Symbol: IGFI\n'), mock.call(
+                        'Location: X1 @ 123313 Mb\n'),
+                    mock.call('\n'), mock.call('Name,Chr,'),
+                    mock.call('Mb,-log(p)'), mock.call('Cm,-log(p)'),
+                    mock.call(',Additive'), mock.call(',Dominance'),
+                    mock.call('\n'), mock.call('MK1,C1,'),
+                    mock.call('12000,'), mock.call('1,'),
+                    mock.call('3'), mock.call(',VA'),
+                    mock.call(',TT'), mock.call('\n'),
+                    mock.call('MK2,C2,'), mock.call('10000,'),
+                    mock.call('15,'), mock.call('7'),
+                    mock.call('\n'), mock.call('MK1,C3,'),
+                    mock.call('1,'), mock.call('45,'),
+                    mock.call('7'), mock.call(',VE'),
+                    mock.call(',Tt')
+
+                ]
+                mock_open.assert_called_once_with("~/results", "w+")
+                filehandler = mock_open()
+                filehandler.write.assert_has_calls(write_calls)
+
+
+    @mock.patch("wqflask.marker_regression.run_mapping.random.choice")
+    def test_write_input_for_browser(self,mock_choice):
+    	mock_choice.side_effect=["F","i","l","e","s","x"]
+    	with mock.patch("builtins.open",mock.mock_open()) as mock_open:
+    		expected=['GP1__Filesx_GWAS', 'GP1__Filesx_ANNOT']
+
+    		results=write_input_for_browser(this_dataset=self.dataset,gwas_results={},annotations={})
+    		self.assertEqual(results,expected)
-- 
cgit v1.2.3


From b695ea7f5ee11c80c9b1c547fedd1216b459acd4 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Tue, 10 Nov 2020 13:01:23 -0600
Subject: Added CSS file for all pages that include a table of traits

---
 wqflask/wqflask/static/new/css/trait_list.css | 34 +++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)
 create mode 100644 wqflask/wqflask/static/new/css/trait_list.css

diff --git a/wqflask/wqflask/static/new/css/trait_list.css b/wqflask/wqflask/static/new/css/trait_list.css
new file mode 100644
index 00000000..87bd244e
--- /dev/null
+++ b/wqflask/wqflask/static/new/css/trait_list.css
@@ -0,0 +1,34 @@
+div.tool-button-container {
+    min-width: 950px;
+}
+
+div.collection-table-options {
+    min-width: 1100px;
+}
+
+div.show-hide-container {
+    margin-bottom: 5px;
+    margin-top: 10px;
+}
+
+/* div.dts_label {
+    position: absolute;
+    right: 10px;
+    background: rgba(0, 0, 0, 0.8);
+    color: white;
+    box-shadow: 3px 3px 10px rgba(0, 0, 0, 0.5);
+    text-align: right;
+    border-radius: 3px;
+    padding: 0.4em;
+    z-index: 2;
+    display: none;
+} */
+
+table.dataTable tbody td {
+    /* word-wrap: break-word;
+    word-break: break-all; */
+    overflow: hidden;
+    max-width: 500px;
+    overflow-wrap: break-word;
+    text-overflow: ellipsis;
+}
\ No newline at end of file
-- 
cgit v1.2.3


From ddcd54b505384b3d8f70c82cc97b9781672c5fb6 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Tue, 10 Nov 2020 13:02:15 -0600
Subject: Changed "BXD-Harvested" to "BXD-Longevity" since that group was
 renamed and using the wrong name was causing correlations to not work.

---
 wqflask/wqflask/show_trait/show_trait.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/show_trait/show_trait.py b/wqflask/wqflask/show_trait/show_trait.py
index 25ba1a1d..0c6ae198 100644
--- a/wqflask/wqflask/show_trait/show_trait.py
+++ b/wqflask/wqflask/show_trait/show_trait.py
@@ -372,7 +372,7 @@ class ShowTrait(object):
 
         # We're checking a string here!
         assert isinstance(this_group, str), "We need a string type thing here"
-        if this_group[:3] == 'BXD' and this_group != "BXD-Harvested":
+        if this_group[:3] == 'BXD' and this_group != "BXD-Longevity":
             this_group = 'BXD'
 
         if this_group:
-- 
cgit v1.2.3


From 61280ad47d8c0486b776011391deadcf7df39819 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Tue, 10 Nov 2020 13:36:13 -0600
Subject: Fixed issue where strain values in bytes needed to be decoded when
 loading a Temp trait

---
 wqflask/wqflask/show_trait/SampleList.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/show_trait/SampleList.py b/wqflask/wqflask/show_trait/SampleList.py
index 37c1d6d5..99ca7f88 100644
--- a/wqflask/wqflask/show_trait/SampleList.py
+++ b/wqflask/wqflask/show_trait/SampleList.py
@@ -35,7 +35,7 @@ class SampleList(object):
             # ZS: self.this_trait will be a list if it is a Temp trait
             if isinstance(self.this_trait, list):
                 if (counter <= len(self.this_trait) and
-                        str(self.this_trait[counter-1]).upper() != 'X'):
+                        self.this_trait[counter-1].decode("utf-8").lower() != 'x'):
                     sample = webqtlCaseData.webqtlCaseData(
                         name=sample_name,
                         value=float(self.this_trait[counter-1]))
-- 
cgit v1.2.3


From 4f9a389795d3ecade1a988347c834103f258e6dc Mon Sep 17 00:00:00 2001
From: zsloan
Date: Tue, 10 Nov 2020 14:15:43 -0600
Subject: * wqflask/wqflask/correlation/show_corr_results.py - Changed some of
 the parameters sent to the correlation table *
 wqflask/wqflask/templates_correlation_page.html - Fixed some column defs and
 removed unused table configuration code

---
 wqflask/wqflask/correlation/show_corr_results.py |  22 +++--
 wqflask/wqflask/templates/correlation_page.html  | 120 +++--------------------
 2 files changed, 26 insertions(+), 116 deletions(-)

diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index ebfea8fe..07b45860 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -522,19 +522,19 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
                 results_dict['tissue_corr'] = "%0.3f" % float(trait.tissue_corr)
                 results_dict['tissue_pvalue'] = "%0.3e" % float(trait.tissue_pvalue)
         elif target_dataset.type == "Publish":
-            results_dict['abbreviation'] = trait.abbreviation
-            if len(trait.abbreviation) > 20:
+            results_dict['abbreviation'] = "N/A"
+            if trait.abbreviation and len(trait.abbreviation) > 20:
                 results_dict['abbreviation_display'] = trait.abbreviation[:20] + "..."
             else:
                 results_dict['abbreviation_display'] = trait.abbreviation
-            if len(trait.description_display) > 40:
-                results_dict['description'] = trait.description_display[:40] + "..."
-            else:
-                results_dict['description'] = trait.description_display
+            # if len(trait.description_display) > 40:
+            #     results_dict['description'] = trait.description_display[:40] + "..."
+            # else:
+            results_dict['description'] = trait.description_display
             results_dict['authors'] = trait.authors
             authors_list = trait.authors.split(',')
-            if len(authors_list > 6):
-                results_dict['authors_display'] = authors_list[:6].join(", ") + ", et al."
+            if len(authors_list) > 6:
+                results_dict['authors_display'] = ", ".join(authors_list[:6]) + ", et al."
             else:
                 results_dict['authors_display'] = trait.authors
             if trait.pubmed_id:
@@ -542,13 +542,15 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
                     results_dict['pubmed_id'] = trait.pubmed_id
                     results_dict['year'] = trait.pubmed_text
                 else:
-                    results_dict['pubmed'] = " " + trait.pubmed_text + ""
+                    results_dict['pubmed_link'] = trait.pubmed_link
+                    results_dict['pubmed_text'] = trait.pubmed_text
             else:
                 if for_api:
                     results_dict['pubmed_id'] = "N/A"
                     results_dict['year'] = "N/A"
                 else:
-                    results_dict['pubmed'] = "N/A"
+                    results_dict['pubmed_link'] = "N/A"
+                    results_dict['pubmed_text'] = "N/A"
             results_dict['lrs_score'] = trait.LRS_score_repr
             results_dict['lrs_location'] = trait.LRS_location_repr
             if trait.additive and trait.additive != "":
diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html
index 6f92fcb5..2b79770a 100644
--- a/wqflask/wqflask/templates/correlation_page.html
+++ b/wqflask/wqflask/templates/correlation_page.html
@@ -260,7 +260,6 @@
 
             console.time("Creating table");
 
-            {% if target_dataset.type == "ProbeSet" %}
             table_conf = {
                 buttons: [
                     {
@@ -400,10 +399,10 @@
                       'width': "500px",
                       'data': null,
                       'render': function(data, type, row, meta) {
-			            try {
-                            return decodeURIComponent(escape(data.abbreviation))
-			            } catch(err){
-			                return data.abbreviation
+			                  try {
+                          return decodeURIComponent(escape(data.abbreviation))
+			                  } catch(err){
+			                    return data.abbreviation
                         }
                       }
                     },
@@ -413,10 +412,10 @@
                       'width': "500px",
                       'data': null,
                       'render': function(data, type, row, meta) {
-			            try {
-                            return decodeURIComponent(escape(data.description))
-			            } catch(err){
-			                return data.description
+			                  try {
+                          return decodeURIComponent(escape(data.description))
+			                  } catch(err){
+			                    return data.description
                         }
                       }
                     },
@@ -426,11 +425,12 @@
                       'width': "500px",
                       'data': null,
                       'render': function(data, type, row, meta) {
-			            try {
-                            return decodeURIComponent(escape(data.authors_display))
-			            } catch(err){
-			                return data.authors_display
+			                  try {
+                          return decodeURIComponent(escape(data.authors_display))
+			                  } catch(err){
+			                    return data.authors_display
                         }
+                      }
                     },
                     {
                       'title': "Year",
@@ -520,7 +520,7 @@
                     "orderable": false
                 } ],
                 "order": [[9, "asc" ]],
-                "sDom": "Bpitirp",
+                "sDom": "Bitir",
                 "autoWidth": true,
                 "bSortClasses": false,
                 "scrollY": "50vh",
@@ -528,98 +528,6 @@
                 "scrollCollapse": true
             }
 
-            {% elif target_dataset.type == "Publish" %}
-            table_conf =  {
-                "drawCallback": function( settings ) {
-                     $('#trait_table tr').click(function(event) {
-                         if (event.target.type !== 'checkbox') {
-                             $(':checkbox', this).trigger('click');
-                         }
-                     });
-                     $('.trait_checkbox:checkbox').on("change", change_buttons);
-                },
-                "buttons": [
-                    {
-                        extend: 'columnsToggle',
-                        columns: function( idx, data, node ) {
-                          if (idx != 0) {
-                            return true;
-                          } else {
-                            return false;
-                          }
-                        },
-                        postfixButtons: [ 'colvisRestore' ]
-                    }
-                ],
-                "columnDefs": [ 
-                    { "targets": 0, "orderable": false }
-                ],  
-                "columns": [
-                    { "type": "natural" },
-                    { "type": "natural" },
-                    { "type": "natural" },
-                    { "type": "natural" },
-                    { "type": "natural", "width": "20%" },
-                    { "type": "natural", "width": "12%" },
-                    { "type": "natural-minus-na" },
-                    { "orderDataType": "dom-innertext", 'orderSequence': [ "desc", "asc"] },
-                    { "type": "natural" },
-                    { "type": "scientific" },
-                    { "type": "natural-minus-na" },
-                    { "type": "natural-minus-na" },
-                    { "type": "natural-minus-na" }
-                ],
-                "order": [[9, "asc" ]],
-                "sDom": "Btir",
-                "iDisplayLength": -1,
-                "autoWidth": false,
-                "processing": true,
-                "deferRender": true,
-                "bSortClasses": false,
-                "scrollY": "50vh",
-                "scroller":  true,
-                "scrollCollapse": true
-            }
-            {% elif target_dataset.type == "Geno" %}
-            table_conf = {
-                "paging": false,
-                buttons: [
-                    {
-                        extend: 'columnsToggle',
-                        columns: function( idx, data, node ) {
-                          if (idx != 0) {
-                            return true;
-                          } else {
-                            return false;
-                          }
-                        },
-                        postfixButtons: [ 'colvisRestore' ]
-                    }
-                ],
-                "columnDefs": [ 
-                    { "targets": 0, "orderable": false }
-                ],  
-                "columns": [
-                    { "type": "natural" },
-                    { "type": "natural" },
-                    { "type": "natural" },
-                    { "type": "natural" },
-                    { "orderDataType": "dom-innertext", 'orderSequence': [ "desc", "asc"] },
-                    { "type": "natural-minus-na" },
-                    { "type": "scientific" }
-                ],
-                "order": [[6, "asc" ]],
-                "sDom": "Btir",
-                "autoWidth": false,
-                "bDeferRender": true,
-                "scrollY": "800px",
-                "scrollCollapse": false,
-                "scrollY": "50vh",
-                "scroller":  true,
-                "scrollCollapse": true
-            }
-            {% endif %}
-
             the_table = $('#trait_table').DataTable(table_conf);
 
             the_table.on( 'order.dt search.dt draw.dt', function () {
-- 
cgit v1.2.3


From 25f7d30b9f052ec5d812bfe3bf9713df850cc267 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Tue, 10 Nov 2020 14:24:26 -0600
Subject: Changed logic for creating temp trait SampleList to account for both
 traits encoded as bytes and traits encoded as strings, since temp traits
 created before and after the Python 3 switchover will have different encoding

---
 wqflask/wqflask/show_trait/SampleList.py | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/wqflask/wqflask/show_trait/SampleList.py b/wqflask/wqflask/show_trait/SampleList.py
index 99ca7f88..a535c493 100644
--- a/wqflask/wqflask/show_trait/SampleList.py
+++ b/wqflask/wqflask/show_trait/SampleList.py
@@ -34,13 +34,18 @@ class SampleList(object):
 
             # ZS: self.this_trait will be a list if it is a Temp trait
             if isinstance(self.this_trait, list):
-                if (counter <= len(self.this_trait) and
-                        self.this_trait[counter-1].decode("utf-8").lower() != 'x'):
-                    sample = webqtlCaseData.webqtlCaseData(
-                        name=sample_name,
-                        value=float(self.this_trait[counter-1]))
-                else:
-                    sample = webqtlCaseData.webqtlCaseData(name=sample_name)
+                sample = webqtlCaseData.webqtlCaseData(name=sample_name)
+                if counter <= len(self.this_trait):
+                    if isinstance(self.this_trait[counter-1], (bytes, bytearray)):
+                        if (self.this_trait[counter-1].decode("utf-8").lower() != 'x'):
+                            sample = webqtlCaseData.webqtlCaseData(
+                                name=sample_name,
+                                value=float(self.this_trait[counter-1]))
+                    else:
+                        if (self.this_trait[counter-1].lower() != 'x'):
+                            sample = webqtlCaseData.webqtlCaseData(
+                                name=sample_name,
+                                value=float(self.this_trait[counter-1]))
             else:
                 # ZS - If there's no value for the sample/strain,
                 # create the sample object (so samples with no value
-- 
cgit v1.2.3


From 0422cea46205ef6477949b1f0dc188f94024f8cf Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Wed, 11 Nov 2020 14:21:15 +0300
Subject: Add basic markdown styling

* wqflask/wqflask/static/new/css/markdown.css: New file.
* wqflask/wqflask/templates/glossary.html: add markdown id to markdown
container.
---
 wqflask/wqflask/static/new/css/markdown.css | 13 +++++++++++++
 wqflask/wqflask/templates/glossary.html     |  6 +++++-
 2 files changed, 18 insertions(+), 1 deletion(-)
 create mode 100644 wqflask/wqflask/static/new/css/markdown.css

diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
new file mode 100644
index 00000000..91167908
--- /dev/null
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -0,0 +1,13 @@
+#markdown {
+    padding: 20px;
+}
+
+#markdown h2, #markdown h3, #markdown h4, #markdown h5 {
+    font-weight: bold;
+}
+
+#markdown img {
+    display: block;
+    margin-right: auto;
+    margin-left: auto;
+}
diff --git a/wqflask/wqflask/templates/glossary.html b/wqflask/wqflask/templates/glossary.html
index 3b29f20e..146c7e86 100644
--- a/wqflask/wqflask/templates/glossary.html
+++ b/wqflask/wqflask/templates/glossary.html
@@ -2,9 +2,13 @@
 
 {% block title %}Glossary{% endblock %}
 
+{% block css %}
+
+{% endblock %}
+
 {% block content %}
 
-
    
+
    
     
         [Edit on Github]
     {{ rendered_markdown|safe }}
-- 
cgit v1.2.3


From 394a67ca2f28b18aa3ea8398ca48661985be8f5a Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Wed, 11 Nov 2020 14:22:07 +0300
Subject: Update image width

---
 wqflask/wqflask/static/markdown/glossary.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/static/markdown/glossary.md b/wqflask/wqflask/static/markdown/glossary.md
index 68276796..db94ae18 100644
--- a/wqflask/wqflask/static/markdown/glossary.md
+++ b/wqflask/wqflask/static/markdown/glossary.md
@@ -238,7 +238,7 @@ Conventional knockout lines (KOs) of mice are often mixtures of the genomes of t
 
 It is often thought that 10 generations of backcrossing will result in a pure genetic background (99.8% C57BL/6J). Unfortunately, this is not true for the region around the KO, and even after many generations of backcrossing of KO stock to C57BL/6J, a large region around the KO is still derived from the 129 substrain (see the residual white "line" at N10 in the figure below. 
 
-Congenic
+Congenic
 
 After 20 generations of backcrossing nearly +/-5 cM on either side of the KO will still usually be derived from 129 (see [Figure 3.6](http://www.informatics.jax.org/silverbook/frames/frame3-3.shtml)) This amounts to an average of +/- 10 megabases of DNA around the KO. The wildtype littermates do NOT have this flanking DNA from 129 and they will be like a true C57BL/6J. The +/- 10 megabases to either side of the KO is known as the "hitchhiking" chromosomal interval. Any polymorphism between 129 and B6 in this interval has the potential to have significant downstream effects on gene expression, protein expression, and higher order traits such as anxiety, activity, and maternal behavior. Much of the conventional KO literature is highly suspect due to this hitchhiker effect (see Gerlai R, [Trends in Neurosci 1996 19:177](http://gn1.genenetwork.org/images/upload/Gerlai_TINS_1996.pdf)).
 
-- 
cgit v1.2.3


From a40538d382b334b2fcb52c1f9bccbe0533ddbaba Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 11 Nov 2020 12:55:36 -0600
Subject: Switched checking if a trait is confidential to checking if it has a
 pubmed ID for the purposes of showing pre-publication
 abbreviation/description

---
 wqflask/base/trait.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/base/trait.py b/wqflask/base/trait.py
index cfc02f8b..e0afa915 100644
--- a/wqflask/base/trait.py
+++ b/wqflask/base/trait.py
@@ -513,7 +513,7 @@ def retrieve_trait_info(trait, dataset, get_qtl_info=False):
             # If the dataset is confidential and the user has access to confidential
             # phenotype traits, then display the pre-publication description instead
             # of the post-publication description
-            if trait.confidential:
+            if not trait.pubmed_id:
                 trait.abbreviation = trait.pre_publication_abbreviation
                 trait.description_display = trait.pre_publication_description
             else:
-- 
cgit v1.2.3


From 486b30324d67971f9fa9e741cf1c7f24d286dad2 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 11 Nov 2020 12:57:20 -0600
Subject: - Fixed an issue causing the phenotype abbreviation to not be
 displayed properly in correlation results - Set default description to N/A
 when there's no trait description for phenotype traits

---
 wqflask/wqflask/correlation/show_corr_results.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index 07b45860..394c113d 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -522,15 +522,12 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
                 results_dict['tissue_corr'] = "%0.3f" % float(trait.tissue_corr)
                 results_dict['tissue_pvalue'] = "%0.3e" % float(trait.tissue_pvalue)
         elif target_dataset.type == "Publish":
-            results_dict['abbreviation'] = "N/A"
-            if trait.abbreviation and len(trait.abbreviation) > 20:
-                results_dict['abbreviation_display'] = trait.abbreviation[:20] + "..."
-            else:
+            results_dict['abbreviation_display'] = "N/A"
+            if trait.abbreviation:
                 results_dict['abbreviation_display'] = trait.abbreviation
-            # if len(trait.description_display) > 40:
-            #     results_dict['description'] = trait.description_display[:40] + "..."
-            # else:
-            results_dict['description'] = trait.description_display
+            results_dict['description'] = "N/A"
+            if trait.description_display:
+                results_dict['description'] = trait.description_display
             results_dict['authors'] = trait.authors
             authors_list = trait.authors.split(',')
             if len(authors_list) > 6:
-- 
cgit v1.2.3


From e20d5f41d076659706442a8ab1db93cd65c9cb0f Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 11 Nov 2020 12:58:17 -0600
Subject: Changed some column widths and removed the width for description so
 it can vary with page width

---
 wqflask/wqflask/templates/correlation_page.html | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html
index 2b79770a..bb7d3df6 100644
--- a/wqflask/wqflask/templates/correlation_page.html
+++ b/wqflask/wqflask/templates/correlation_page.html
@@ -396,20 +396,19 @@
                     {
                       'title': "Abbreviation",
                       'type': "natural",
-                      'width': "500px",
+                      'width': "200px",
                       'data': null,
                       'render': function(data, type, row, meta) {
 			                  try {
-                          return decodeURIComponent(escape(data.abbreviation))
+                          return decodeURIComponent(escape(data.abbreviation_display))
 			                  } catch(err){
-			                    return data.abbreviation
+			                    return data.abbreviation_display
                         }
                       }
                     },
                     {
                       'title': "Description",
                       'type': "natural",
-                      'width': "500px",
                       'data': null,
                       'render': function(data, type, row, meta) {
 			                  try {
@@ -422,7 +421,7 @@
                     {
                       'title': "Authors",
                       'type': "natural",
-                      'width': "500px",
+                      'width': "400px",
                       'data': null,
                       'render': function(data, type, row, meta) {
 			                  try {
-- 
cgit v1.2.3


From 06102cab4b6a3bc898c65c6df485f328e3314980 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 11 Nov 2020 13:03:19 -0600
Subject: Added Bonface to the footer

---
 wqflask/wqflask/templates/base.html | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/templates/base.html b/wqflask/wqflask/templates/base.html
index 0f4e5ef5..1e4301bc 100644
--- a/wqflask/wqflask/templates/base.html
+++ b/wqflask/wqflask/templates/base.html
@@ -159,9 +159,10 @@
                 Saunak Sen,
                 Zachary Sloan,
                 Arthur Centeno,
-                and Christian Fischer.
+                Christian Fischer
+                and Bonface Munyoki.
             
    
-            
    Design and code by Pjotr Prins, Zach Sloan, Arthur Centeno, Christan Fischer, Danny Arends, Sam Ockman, Lei Yan, Xiaodong Zhou, Christian Fernandez, 
+            
    Design and code by Pjotr Prins, Zach Sloan, Arthur Centeno, Christan Fischer, Bonface Munyoki, Danny Arends, Sam Ockman, Lei Yan, Xiaodong Zhou, Christian Fernandez,
             Ning Liu, Rudi Alberts, Elissa Chesler, Sujoy Roy, Evan G. Williams, Alexander G. Williams, Kenneth Manly, Jintao Wang, Robert W. Williams, and 
             
             colleagues.
    
-- 
cgit v1.2.3


From 424f44c0656da8f99dce0fa0a00bd5e6ba9bace3 Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Wed, 11 Nov 2020 23:05:17 +0300
Subject: add tests for marker_regression/gemma_mapping.py

---
 wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
index 06ce55d8..ed72cb33 100644
--- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
@@ -151,18 +151,21 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4
             mock_open.assert_called_once_with(
                 "/home/user/img/gema_file_output.assoc.txt")
             self.assertEqual(results, expected)
+
     @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img")
     def test_parse_gemma_output_empty_return(self):
         output_file_results = """chr\t today"""
         with mock.patch("builtins.open", mock.mock_open(read_data=output_file_results)) as mock_open:
             results = parse_gemma_output(genofile_name="gema_file")
             self.assertEqual(results, [])
+
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
     def test_parse_loco_output_file_found(self, mock_os):
         mock_os.path.isfile.return_value = True
         file_to_write = """{"files":["file_1","file_2"]}"""
         pass
+
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
     def test_parse_loco_output_file_not_found(self, mock_os):
-- 
cgit v1.2.3


From 3396e1f008f3605439b38837f7a50407588d1f9b Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Wed, 11 Nov 2020 23:06:27 +0300
Subject: add tests for marker_regression/run_mapping.py

---
 .../wqflask/marker_regression/test_run_mapping.py  | 85 ++++++++++++++++++++++
 1 file changed, 85 insertions(+)

diff --git a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
index 6521c41d..be6e6e48 100644
--- a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
@@ -7,6 +7,8 @@ from wqflask.marker_regression.run_mapping import geno_db_exists
 from wqflask.marker_regression.run_mapping import write_input_for_browser
 from wqflask.marker_regression.run_mapping import export_mapping_results
 from wqflask.marker_regression.run_mapping import trim_markers_for_figure
+from wqflask.marker_regression.run_mapping import get_perm_strata
+from wqflask.marker_regression.run_mapping import get_chr_lengths
 
 
 class AttributeSetter:
@@ -23,10 +25,27 @@ class MockDataSetGroup(AttributeSetter):
 
 class TestRunMapping(unittest.TestCase):
     def setUp(self):
+
         self.group = MockDataSetGroup(
             {"genofile": "~/genofiles/g1_file", "name": "GP1_", "species": "Human"})
+        chromosomes={
+    	 "3":AttributeSetter({
+    	 "name":"C1",
+    	 "length":"0.04"
+    	 }),
+    	  "4":AttributeSetter({
+    	 "name":"C2",
+    	 "length":"0.03"
+    	 }),
+    	  "5":AttributeSetter({
+    	 "name":"C4",
+    	 "length":"0.01"
+    	 })
+    	}
         self.dataset = AttributeSetter(
             {"fullname": "dataser_1", "group": self.group, "type": "ProbeSet"})
+
+        self.chromosomes=AttributeSetter({"chromosomes":chromosomes})
         self.trait = AttributeSetter(
             {"symbol": "IGFI", "chr": "X1", "mb": 123313})
 
@@ -196,3 +215,69 @@ class TestRunMapping(unittest.TestCase):
 
     		results=write_input_for_browser(this_dataset=self.dataset,gwas_results={},annotations={})
     		self.assertEqual(results,expected)
+
+
+    def test_get_perm_strata(self):
+    	categorical_vars=["C1","C2","W1"]
+    	used_samples=["S1","S2"]
+    	sample_list=AttributeSetter({"sample_attribute_values":{
+    		"S1":{
+    		 "C1":"c1_value",
+    		 "C2":"c2_value",
+    		 "W1":"w1_value"
+
+    		},
+    		"S2":{
+    		"W1":"w2_value",
+    		 "W2":"w2_value"
+
+    		},
+    		"S3":{
+
+    		"C1":"c1_value",
+    		"C2":"c2_value"
+
+    		},
+    		
+    		}})
+
+    	results=get_perm_strata(this_trait={},sample_list=sample_list,categorical_vars=categorical_vars,used_samples=used_samples)
+    	self.assertEqual(results,[2,1])
+
+
+    def test_get_chr_length(self):
+    	chromosomes=AttributeSetter({"chromosomes":self.chromosomes})
+    	dataset=AttributeSetter({"species":chromosomes})
+    	results=get_chr_lengths(mapping_scale="physic",mapping_method="reaper",dataset=dataset,qtl_results=[])
+    	chr_lengths=[]
+    	for key,chromo in self.chromosomes.chromosomes.items():
+    		chr_lengths.append({"chr":chromo.name,"size":chromo.length})
+
+    	self.assertEqual(chr_lengths,results)
+
+    	qtl_results=[{
+    	"chr":"16",
+    	"cM":"0.2"
+    	},
+    	{
+    	"chr":"12",
+    	"cM":"0.5"
+    	},
+    	{
+    	"chr":"18",
+    	"cM":"0.1"
+    	},
+    	{
+    	"chr":"22",
+    	"cM":"0.4"
+    	},
+    	]
+
+
+
+    	result_with_other_mapping_scale=get_chr_lengths(mapping_scale="other",mapping_method="reaper",dataset=dataset,qtl_results=qtl_results)
+    	expected_value= [{'chr': '1', 'size': '0'}, {'chr': '16', 'size': '500000.0'}, {'chr': '18', 'size': '400000.0'}]
+
+    	self.assertEqual(result_with_other_mapping_scale,expected_value)    	
+
+
-- 
cgit v1.2.3


From 8f85c96b2094a9ce722b93a0e375ec3de90c959a Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Wed, 11 Nov 2020 23:08:14 +0300
Subject: add tests for marker_regression/qtlreaper_mapping.py

---
 .../marker_regression/test_qtlreaper_mapping.py    | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py

diff --git a/wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py b/wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py
new file mode 100644
index 00000000..7ece663a
--- /dev/null
+++ b/wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py
@@ -0,0 +1,22 @@
+import unittest
+from unittest import  mock
+from wqflask.marker_regression.qtlreaper_mapping import  gen_pheno_txt_file
+from wqflask.marker_regression.qtlreaper_mapping import  natural_sort
+
+#issues some methods in genofile object are not defined
+#modify samples should equal to vals
+class TestQtlReaperMapping(unittest.TestCase):
+	@mock.patch("wqflask.marker_regression.qtlreaper_mapping.TEMPDIR", "/home/user/data")
+	def  test_gen_pheno_txt_file(self):
+		vals=["V1","x","V4","V3","x"]
+		samples=["S1","S2","S3","S4","S5"]
+		trait_filename="trait_file"
+		with mock.patch("builtins.open", mock.mock_open())as mock_open:
+			gen_pheno_txt_file(samples=samples,vals=vals,trait_filename=trait_filename)
+			mock_open.assert_called_once_with("/home/user/data/gn2/trait_file.txt","w")
+			filehandler=mock_open()
+			write_calls= [mock.call('Trait\t'),mock.call('S1\tS3\tS4\n'),mock.call('T1\t'),mock.call('V1\tV4\tV3')]
+
+			filehandler.write.assert_has_calls(write_calls)
+
+	                                                                                                                    
-- 
cgit v1.2.3


From e63e85868ed7b63bbe784368b3c1babf0d1f2088 Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Wed, 11 Nov 2020 23:09:01 +0300
Subject: add tests for marker_regression/rqtl_mapping.py

---
 .../wqflask/marker_regression/test_rqtl_mapping.py | 55 ++++++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py

diff --git a/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py b/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
new file mode 100644
index 00000000..641644cc
--- /dev/null
+++ b/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
@@ -0,0 +1,55 @@
+import unittest
+from unittest import mock
+from wqflask import app
+from wqflask.marker_regression.rqtl_mapping import get_trait_data_type
+from wqflask.marker_regression.rqtl_mapping import sanitize_rqtl_phenotype
+from wqflask.marker_regression.rqtl_mapping import sanitize_rqtl_names
+
+class TestRqtlMapping(unittest.TestCase):
+
+	def setUp(self):
+		self.app_context=app.app_context()
+		self.app_context.push()
+
+	def tearDown(self):
+		self.app_context.pop()
+
+
+	@mock.patch("wqflask.marker_regression.rqtl_mapping.g")
+	@mock.patch("wqflask.marker_regression.rqtl_mapping.logger")
+	def test_get_trait_data_type_found(self,mock_logger,mock_db):
+		caller_value="""SELECT value FROM TraitMetadata WHERE type='trait_data_type'"""
+		mock_db.db.execute.return_value.fetchone.return_value=["""{"type":"trait_data_type","name":"T1","traid_id":"fer434f"}"""]
+		results=get_trait_data_type("traid_id")
+		mock_db.db.execute.assert_called_with(caller_value)
+		self.assertEqual(results,"fer434f")
+
+
+	@mock.patch("wqflask.marker_regression.rqtl_mapping.g")
+	@mock.patch("wqflask.marker_regression.rqtl_mapping.logger")
+	def test_get_trait_data_type_not_found(self,mock_logger,mock_db):
+		caller_value="""SELECT value FROM TraitMetadata WHERE type='trait_data_type'"""
+		mock_db.db.execute.return_value.fetchone.return_value=["""{"type":"trait_data_type","name":"T1","traid_id":"fer434f"}"""]
+		results=get_trait_data_type("other")
+		mock_db.db.execute.assert_called_with(caller_value)
+		self.assertEqual(results,"numeric")
+
+	def test_sanitize_rqtl_phenotype(self):
+		vals=['f',"x","r","x","x"]
+		results=sanitize_rqtl_phenotype(vals)
+		expected_phenotype_string='c(f,NA,r,NA,NA)'
+
+		self.assertEqual(results,expected_phenotype_string)
+
+	def test_sanitize_rqtl_names(self):
+		vals=['f',"x","r","x","x"]
+		expected_sanitized_name="c('f',NA,'r',NA,NA)"
+		results=sanitize_rqtl_names(vals)
+		self.assertEqual(expected_sanitized_name,results)
+
+
+		
+		
+
+
+
-- 
cgit v1.2.3


From f4dbdc51102d942079c7159ac48071282ec995ea Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Thu, 12 Nov 2020 00:45:03 +0300
Subject: modify tests  for marker regression

---
 .../marker_regression/test_gemma_mapping.py        |   8 ++
 .../marker_regression/test_plink_mapping.py        |   4 +
 .../marker_regression/test_qtlreaper_mapping.py    |   3 +-
 .../wqflask/marker_regression/test_rqtl_mapping.py |  13 +-
 .../wqflask/marker_regression/test_run_mapping.py  | 153 +++++++++++----------
 5 files changed, 93 insertions(+), 88 deletions(-)

diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
index ed72cb33..e47c8335 100644
--- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
@@ -24,6 +24,7 @@ class TestGemmaMapping(unittest.TestCase):
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output")
     def test_run_gemma_first_run_loco_set_false(self, mock_parse_loco):
+        """add tests for gemma function where  first run is set to false"""
         dataset = AttributeSetter(
             {"group": AttributeSetter({"genofile": "genofile.geno"})})
 
@@ -50,6 +51,7 @@ class TestGemmaMapping(unittest.TestCase):
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
     @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file")
     def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco):
+        """add tests for run_gemma where first run is set to true"""
         chromosomes = []
         for i in range(1, 5):
             chromosomes.append(AttributeSetter({"name": f"CH{i}"}))
@@ -81,6 +83,7 @@ class TestGemmaMapping(unittest.TestCase):
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data")
     def test_gen_pheno_txt_file(self):
+        """add tests for generating pheno txt file"""
         with mock.patch("builtins.open", mock.mock_open())as mock_open:
             gen_pheno_txt_file(this_dataset={}, genofile_name="", vals=[
                                "x", "w", "q", "we", "R"], trait_filename="fitr.re")
@@ -97,6 +100,7 @@ class TestGemmaMapping(unittest.TestCase):
     @mock.patch("wqflask.marker_regression.gemma_mapping.create_trait")
     @mock.patch("wqflask.marker_regression.gemma_mapping.create_dataset")
     def test_gen_covariates_file(self, create_dataset, create_trait, flat_files):
+        """add tests for generating covariates files"""
         covariates = "X1:X2,Y1:Y2,M1:M3,V1:V2"
         samplelist = ["X1", "X2", "X3", "X4"]
         create_dataset_side_effect = []
@@ -138,6 +142,7 @@ class TestGemmaMapping(unittest.TestCase):
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img/")
     def test_parse_gemma_output_obj_returned(self):
+        """add test for generating gemma output with obj returned"""
         file = """X/Y\t gn2\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5
 X/Y\tgn2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5
 chr\tgn1\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7
@@ -154,6 +159,7 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img")
     def test_parse_gemma_output_empty_return(self):
+        """add tests for parse gemma output where nothing returned"""
         output_file_results = """chr\t today"""
         with mock.patch("builtins.open", mock.mock_open(read_data=output_file_results)) as mock_open:
             results = parse_gemma_output(genofile_name="gema_file")
@@ -162,6 +168,7 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
     def test_parse_loco_output_file_found(self, mock_os):
+        """add tests for parse loco output file found"""
         mock_os.path.isfile.return_value = True
         file_to_write = """{"files":["file_1","file_2"]}"""
         pass
@@ -169,6 +176,7 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
     def test_parse_loco_output_file_not_found(self, mock_os):
+        """add tests for parse loco output file not found"""
 
         mock_os.path.isfile.return_value = False
         file_to_write = """{"files":["file_1","file_2"]}"""
diff --git a/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py b/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py
index a5fa0c04..428f45b9 100644
--- a/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py
@@ -15,6 +15,7 @@ class AttributeSetter:
 class TestPlinkMapping(unittest.TestCase):
 
     def test_build_line_list(self):
+        """testing for building line list"""
         line_1 = "this is line one test"
         irregular_line = "  this     is an, irregular line     "
         exp_line1 = ["this", "is", "line", "one", "test"]
@@ -26,6 +27,7 @@ class TestPlinkMapping(unittest.TestCase):
 
     @mock.patch("wqflask.marker_regression.plink_mapping.flat_files")
     def test_get_samples_from_ped_file(self, mock_flat_files):
+        """test for getting samples from ped file"""
         dataset = AttributeSetter({"group": AttributeSetter({"name": "n_1"})})
         file_sample = """Expected_1\tline test
 Expected_2\there
@@ -41,6 +43,7 @@ Expected_2\there
     @mock.patch("wqflask.marker_regression.plink_mapping.TMPDIR", "/home/user/data/")
     @mock.patch("wqflask.marker_regression.plink_mapping.get_samples_from_ped_file")
     def test_gen_pheno_txt_file_plink(self, mock_samples):
+        """test for getting gen_pheno txt file"""
         mock_samples.return_value = ["Expected_1", "Expected_2", "Expected_3"]
 
         trait = AttributeSetter({"name": "TX"})
@@ -62,6 +65,7 @@ Expected_2\there
     @mock.patch("wqflask.marker_regression.plink_mapping.TMPDIR", "/home/user/data/")
     @mock.patch("wqflask.marker_regression.plink_mapping.build_line_list")
     def test_parse_plink_output(self, mock_line_list):
+        """test for parsing plink output"""
         chromosomes = [0, 34, 110, 89, 123, 23, 2]
         species = AttributeSetter(
             {"name": "S1", "chromosomes": AttributeSetter({"chromosomes": chromosomes})})
diff --git a/wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py b/wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py
index 7ece663a..b47f877a 100644
--- a/wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py
@@ -1,13 +1,12 @@
 import unittest
 from unittest import  mock
 from wqflask.marker_regression.qtlreaper_mapping import  gen_pheno_txt_file
-from wqflask.marker_regression.qtlreaper_mapping import  natural_sort
 
 #issues some methods in genofile object are not defined
 #modify samples should equal to vals
 class TestQtlReaperMapping(unittest.TestCase):
 	@mock.patch("wqflask.marker_regression.qtlreaper_mapping.TEMPDIR", "/home/user/data")
-	def  test_gen_pheno_txt_file(self):
+	def  test_gen_pheno_txt_file(self):                   
 		vals=["V1","x","V4","V3","x"]
 		samples=["S1","S2","S3","S4","S5"]
 		trait_filename="trait_file"
diff --git a/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py b/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
index 641644cc..9fd32a30 100644
--- a/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
@@ -18,23 +18,15 @@ class TestRqtlMapping(unittest.TestCase):
 	@mock.patch("wqflask.marker_regression.rqtl_mapping.g")
 	@mock.patch("wqflask.marker_regression.rqtl_mapping.logger")
 	def test_get_trait_data_type_found(self,mock_logger,mock_db):
+		"""test for getting trait data_type return True"""
 		caller_value="""SELECT value FROM TraitMetadata WHERE type='trait_data_type'"""
 		mock_db.db.execute.return_value.fetchone.return_value=["""{"type":"trait_data_type","name":"T1","traid_id":"fer434f"}"""]
 		results=get_trait_data_type("traid_id")
 		mock_db.db.execute.assert_called_with(caller_value)
 		self.assertEqual(results,"fer434f")
 
-
-	@mock.patch("wqflask.marker_regression.rqtl_mapping.g")
-	@mock.patch("wqflask.marker_regression.rqtl_mapping.logger")
-	def test_get_trait_data_type_not_found(self,mock_logger,mock_db):
-		caller_value="""SELECT value FROM TraitMetadata WHERE type='trait_data_type'"""
-		mock_db.db.execute.return_value.fetchone.return_value=["""{"type":"trait_data_type","name":"T1","traid_id":"fer434f"}"""]
-		results=get_trait_data_type("other")
-		mock_db.db.execute.assert_called_with(caller_value)
-		self.assertEqual(results,"numeric")
-
 	def test_sanitize_rqtl_phenotype(self):
+		"""test for sanitizing rqtl phenotype"""
 		vals=['f',"x","r","x","x"]
 		results=sanitize_rqtl_phenotype(vals)
 		expected_phenotype_string='c(f,NA,r,NA,NA)'
@@ -42,6 +34,7 @@ class TestRqtlMapping(unittest.TestCase):
 		self.assertEqual(results,expected_phenotype_string)
 
 	def test_sanitize_rqtl_names(self):
+		"""test for sanitzing rqtl names"""
 		vals=['f',"x","r","x","x"]
 		expected_sanitized_name="c('f',NA,'r',NA,NA)"
 		results=sanitize_rqtl_names(vals)
diff --git a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
index be6e6e48..2af4c3e3 100644
--- a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
@@ -28,24 +28,24 @@ class TestRunMapping(unittest.TestCase):
 
         self.group = MockDataSetGroup(
             {"genofile": "~/genofiles/g1_file", "name": "GP1_", "species": "Human"})
-        chromosomes={
-    	 "3":AttributeSetter({
-    	 "name":"C1",
-    	 "length":"0.04"
-    	 }),
-    	  "4":AttributeSetter({
-    	 "name":"C2",
-    	 "length":"0.03"
-    	 }),
-    	  "5":AttributeSetter({
-    	 "name":"C4",
-    	 "length":"0.01"
-    	 })
-    	}
+        chromosomes = {
+            "3": AttributeSetter({
+                "name": "C1",
+                "length": "0.04"
+            }),
+            "4": AttributeSetter({
+                "name": "C2",
+                "length": "0.03"
+            }),
+            "5": AttributeSetter({
+                "name": "C4",
+                "length": "0.01"
+            })
+        }
         self.dataset = AttributeSetter(
             {"fullname": "dataser_1", "group": self.group, "type": "ProbeSet"})
 
-        self.chromosomes=AttributeSetter({"chromosomes":chromosomes})
+        self.chromosomes = AttributeSetter({"chromosomes": chromosomes})
         self.trait = AttributeSetter(
             {"symbol": "IGFI", "chr": "X1", "mb": 123313})
 
@@ -142,6 +142,7 @@ class TestRunMapping(unittest.TestCase):
         self.assertEqual(result_2, marker_2)
 
     def test_export_mapping_results(self):
+        """test for exporting mapping results"""
         datetime_mock = mock.Mock(wraps=datetime.datetime)
         datetime_mock.now.return_value = datetime.datetime(
             2019, 9, 1, 10, 12, 12)
@@ -206,78 +207,78 @@ class TestRunMapping(unittest.TestCase):
                 filehandler = mock_open()
                 filehandler.write.assert_has_calls(write_calls)
 
-
     @mock.patch("wqflask.marker_regression.run_mapping.random.choice")
-    def test_write_input_for_browser(self,mock_choice):
-    	mock_choice.side_effect=["F","i","l","e","s","x"]
-    	with mock.patch("builtins.open",mock.mock_open()) as mock_open:
-    		expected=['GP1__Filesx_GWAS', 'GP1__Filesx_ANNOT']
-
-    		results=write_input_for_browser(this_dataset=self.dataset,gwas_results={},annotations={})
-    		self.assertEqual(results,expected)
+    def test_write_input_for_browser(self, mock_choice):
+        """test for writing input for browser"""
+        mock_choice.side_effect = ["F", "i", "l", "e", "s", "x"]
+        with mock.patch("builtins.open", mock.mock_open()) as mock_open:
+            expected = ['GP1__Filesx_GWAS', 'GP1__Filesx_ANNOT']
 
+            results = write_input_for_browser(
+                this_dataset=self.dataset, gwas_results={}, annotations={})
+            self.assertEqual(results, expected)
 
     def test_get_perm_strata(self):
-    	categorical_vars=["C1","C2","W1"]
-    	used_samples=["S1","S2"]
-    	sample_list=AttributeSetter({"sample_attribute_values":{
-    		"S1":{
-    		 "C1":"c1_value",
-    		 "C2":"c2_value",
-    		 "W1":"w1_value"
+        categorical_vars = ["C1", "C2", "W1"]
+        used_samples = ["S1", "S2"]
+        sample_list = AttributeSetter({"sample_attribute_values": {
+            "S1": {
+                "C1": "c1_value",
+                "C2": "c2_value",
+                "W1": "w1_value"
 
-    		},
-    		"S2":{
-    		"W1":"w2_value",
-    		 "W2":"w2_value"
+            },
+            "S2": {
+                "W1": "w2_value",
+                "W2": "w2_value"
 
-    		},
-    		"S3":{
+            },
+            "S3": {
 
-    		"C1":"c1_value",
-    		"C2":"c2_value"
+                "C1": "c1_value",
+                "C2": "c2_value"
 
-    		},
-    		
-    		}})
+            },
 
-    	results=get_perm_strata(this_trait={},sample_list=sample_list,categorical_vars=categorical_vars,used_samples=used_samples)
-    	self.assertEqual(results,[2,1])
+        }})
 
+        results = get_perm_strata(this_trait={}, sample_list=sample_list,
+                                  categorical_vars=categorical_vars, used_samples=used_samples)
+        self.assertEqual(results, [2, 1])
 
     def test_get_chr_length(self):
-    	chromosomes=AttributeSetter({"chromosomes":self.chromosomes})
-    	dataset=AttributeSetter({"species":chromosomes})
-    	results=get_chr_lengths(mapping_scale="physic",mapping_method="reaper",dataset=dataset,qtl_results=[])
-    	chr_lengths=[]
-    	for key,chromo in self.chromosomes.chromosomes.items():
-    		chr_lengths.append({"chr":chromo.name,"size":chromo.length})
-
-    	self.assertEqual(chr_lengths,results)
-
-    	qtl_results=[{
-    	"chr":"16",
-    	"cM":"0.2"
-    	},
-    	{
-    	"chr":"12",
-    	"cM":"0.5"
-    	},
-    	{
-    	"chr":"18",
-    	"cM":"0.1"
-    	},
-    	{
-    	"chr":"22",
-    	"cM":"0.4"
-    	},
-    	]
-
-
-
-    	result_with_other_mapping_scale=get_chr_lengths(mapping_scale="other",mapping_method="reaper",dataset=dataset,qtl_results=qtl_results)
-    	expected_value= [{'chr': '1', 'size': '0'}, {'chr': '16', 'size': '500000.0'}, {'chr': '18', 'size': '400000.0'}]
-
-    	self.assertEqual(result_with_other_mapping_scale,expected_value)    	
+        """test for getting chromosome length"""
+        chromosomes = AttributeSetter({"chromosomes": self.chromosomes})
+        dataset = AttributeSetter({"species": chromosomes})
+        results = get_chr_lengths(
+            mapping_scale="physic", mapping_method="reaper", dataset=dataset, qtl_results=[])
+        chr_lengths = []
+        for key, chromo in self.chromosomes.chromosomes.items():
+            chr_lengths.append({"chr": chromo.name, "size": chromo.length})
+
+        self.assertEqual(chr_lengths, results)
+
+        qtl_results = [{
+            "chr": "16",
+            "cM": "0.2"
+        },
+            {
+            "chr": "12",
+            "cM": "0.5"
+        },
+            {
+            "chr": "18",
+            "cM": "0.1"
+        },
+            {
+            "chr": "22",
+            "cM": "0.4"
+        },
+        ]
 
+        result_with_other_mapping_scale = get_chr_lengths(
+            mapping_scale="other", mapping_method="reaper", dataset=dataset, qtl_results=qtl_results)
+        expected_value = [{'chr': '1', 'size': '0'}, {
+            'chr': '16', 'size': '500000.0'}, {'chr': '18', 'size': '400000.0'}]
 
+        self.assertEqual(result_with_other_mapping_scale, expected_value)
-- 
cgit v1.2.3


From a48e8d5e652551eb2d57953304ea0ec3587217ce Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Thu, 12 Nov 2020 21:11:28 +0300
Subject: add test for parse gemma output in marker_regression/gemma_mapping.py

---
 .../marker_regression/test_gemma_mapping.py        | 22 ++++++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
index e47c8335..bcc5dab9 100644
--- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
@@ -167,11 +167,29 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
-    def test_parse_loco_output_file_found(self, mock_os):
+    @mock.patch("wqflask.marker_regression.gemma_mapping.json")
+    def test_parse_loco_output_file_found(self,mock_json,mock_os):
         """add tests for parse loco output file found"""
+        mock_json.load.return_value={
+        "files":[["file_name","user","~/file1"],
+        ["file_name","user","~/file2"]]
+        }
+        return_file_1="""X/Y\t L1\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5
+X/Y\tL2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5
+chr\tL3\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7"""
+        return_file_2="""chr\tother\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5"""
         mock_os.path.isfile.return_value = True
         file_to_write = """{"files":["file_1","file_2"]}"""
-        pass
+        with mock.patch("builtins.open") as mock_open:
+
+            handles=(mock.mock_open(read_data="gwas").return_value,mock.mock_open(read_data=return_file_1).return_value,mock.mock_open(read_data=return_file_2).return_value)
+            mock_open.side_effect=handles
+            results = parse_loco_output(
+                this_dataset={}, gwa_output_filename=".xw/")
+            expected_results= [{'name': ' L1', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'L2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}]
+
+            self.assertEqual(expected_results,results)
+
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
-- 
cgit v1.2.3


From da794df7cf9094fc81edbcf007149db86bf68d01 Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Thu, 12 Nov 2020 21:12:48 +0300
Subject: add pep8 fixes in marker_regression/gemma_mapping.py

---
 .../marker_regression/test_gemma_mapping.py        | 23 +++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
index bcc5dab9..a44ccb00 100644
--- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
@@ -168,28 +168,29 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
     @mock.patch("wqflask.marker_regression.gemma_mapping.json")
-    def test_parse_loco_output_file_found(self,mock_json,mock_os):
+    def test_parse_loco_output_file_found(self, mock_json, mock_os):
         """add tests for parse loco output file found"""
-        mock_json.load.return_value={
-        "files":[["file_name","user","~/file1"],
-        ["file_name","user","~/file2"]]
+        mock_json.load.return_value = {
+            "files": [["file_name", "user", "~/file1"],
+                      ["file_name", "user", "~/file2"]]
         }
-        return_file_1="""X/Y\t L1\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5
+        return_file_1 = """X/Y\t L1\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5
 X/Y\tL2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5
 chr\tL3\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7"""
-        return_file_2="""chr\tother\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5"""
+        return_file_2 = """chr\tother\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5"""
         mock_os.path.isfile.return_value = True
         file_to_write = """{"files":["file_1","file_2"]}"""
         with mock.patch("builtins.open") as mock_open:
 
-            handles=(mock.mock_open(read_data="gwas").return_value,mock.mock_open(read_data=return_file_1).return_value,mock.mock_open(read_data=return_file_2).return_value)
-            mock_open.side_effect=handles
+            handles = (mock.mock_open(read_data="gwas").return_value, mock.mock_open(
+                read_data=return_file_1).return_value, mock.mock_open(read_data=return_file_2).return_value)
+            mock_open.side_effect = handles
             results = parse_loco_output(
                 this_dataset={}, gwa_output_filename=".xw/")
-            expected_results= [{'name': ' L1', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'L2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}]
-
-            self.assertEqual(expected_results,results)
+            expected_results = [{'name': ' L1', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {
+                'name': 'L2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}]
 
+            self.assertEqual(expected_results, results)
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
-- 
cgit v1.2.3


From d4b3e8f43c4f4b10fb9614d80cb9d4e2a723c879 Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Thu, 12 Nov 2020 21:40:49 +0300
Subject: refactor code

---
 wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
index a44ccb00..af20c1b8 100644
--- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
@@ -28,13 +28,12 @@ class TestGemmaMapping(unittest.TestCase):
         dataset = AttributeSetter(
             {"group": AttributeSetter({"genofile": "genofile.geno"})})
 
-        output_files = "file1"
-        use_loco = False
+        output_file = "file1"
         mock_parse_loco.return_value = []
         this_trait = AttributeSetter({"name": "t1"})
 
         result = run_gemma(this_trait=this_trait, this_dataset=dataset, samples=[], vals=[
-        ], covariates="", use_loco=True, first_run=False, output_files=output_files)
+        ], covariates="", use_loco=True, first_run=False, output_files=output_file)
 
         expected_results = ([], "file1")
         self.assertEqual(expected_results, result)
-- 
cgit v1.2.3


From 6feb7dd0f8020c7ed54b70be6a15c099dc62b490 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 12 Nov 2020 13:33:52 -0600
Subject: Show Mb positions to 6 decimal places in mapping results table

---
 wqflask/wqflask/marker_regression/run_mapping.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/marker_regression/run_mapping.py b/wqflask/wqflask/marker_regression/run_mapping.py
index fa61272f..9625349c 100644
--- a/wqflask/wqflask/marker_regression/run_mapping.py
+++ b/wqflask/wqflask/marker_regression/run_mapping.py
@@ -414,7 +414,7 @@ class RunMapping(object):
                           highest_chr = marker['chr']
                       if ('lod_score' in marker.keys()) or ('lrs_value' in marker.keys()):
                           if 'Mb' in marker.keys():
-                              marker['display_pos'] = "Chr" + str(marker['chr']) + ": " + "{:.3f}".format(marker['Mb'])
+                              marker['display_pos'] = "Chr" + str(marker['chr']) + ": " + "{:.6f}".format(marker['Mb'])
                           elif 'cM' in marker.keys():
                               marker['display_pos'] = "Chr" + str(marker['chr']) + ": " + "{:.3f}".format(marker['cM'])
                           else:
-- 
cgit v1.2.3


From d6227b9e0a331b2cb3db705a503dcfa5bbaf1555 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 12 Nov 2020 13:42:18 -0600
Subject: Substituted -log(p) with -logP as mentioned in Rob's e-mail

---
 wqflask/wqflask/marker_regression/display_mapping_results.py | 6 +++---
 wqflask/wqflask/marker_regression/run_mapping.py             | 8 ++++----
 wqflask/wqflask/templates/mapping_results.html               | 8 ++++----
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/wqflask/wqflask/marker_regression/display_mapping_results.py b/wqflask/wqflask/marker_regression/display_mapping_results.py
index 6d6572ff..3f6de2b2 100644
--- a/wqflask/wqflask/marker_regression/display_mapping_results.py
+++ b/wqflask/wqflask/marker_regression/display_mapping_results.py
@@ -2076,7 +2076,7 @@ class DisplayMappingResults(object):
         if self.lrsMax <= 0:  #sliding scale
             if "lrs_value" in self.qtlresults[0]:
                 LRS_LOD_Max = max([result['lrs_value'] for result in self.qtlresults])
-                if self.LRS_LOD == "LOD" or self.LRS_LOD == "-log(p)":
+                if self.LRS_LOD == "LOD" or self.LRS_LOD == "-logP":
                     LRS_LOD_Max = LRS_LOD_Max / self.LODFACTOR
                     if self.permChecked and self.nperm > 0 and not self.multipleInterval:
                         self.significant = min(self.significant / self.LODFACTOR, webqtlConfig.MAXLRS)
@@ -2172,7 +2172,7 @@ class DisplayMappingResults(object):
             TEXT_X_DISPLACEMENT = -12
         else:
             TEXT_X_DISPLACEMENT = -30
-        if self.LRS_LOD == "-log(p)":
+        if self.LRS_LOD == "-logP":
             TEXT_Y_DISPLACEMENT = -242
         else:
             TEXT_Y_DISPLACEMENT = -210
@@ -2397,7 +2397,7 @@ class DisplayMappingResults(object):
 
 
                 if 'lrs_value' in qtlresult:
-                    if self.LRS_LOD == "LOD" or self.LRS_LOD == "-log(p)":
+                    if self.LRS_LOD == "LOD" or self.LRS_LOD == "-logP":
                         if qtlresult['lrs_value'] > 460 or qtlresult['lrs_value']=='inf':
                             #Yc = yZero - webqtlConfig.MAXLRS*LRSHeightThresh/(LRSAxisList[-1]*self.LODFACTOR)
                             Yc = yZero - webqtlConfig.MAXLRS*LRSHeightThresh/(LRS_LOD_Max*self.LODFACTOR)
diff --git a/wqflask/wqflask/marker_regression/run_mapping.py b/wqflask/wqflask/marker_regression/run_mapping.py
index 9625349c..31d6a67c 100644
--- a/wqflask/wqflask/marker_regression/run_mapping.py
+++ b/wqflask/wqflask/marker_regression/run_mapping.py
@@ -228,7 +228,7 @@ class RunMapping(object):
                 self.output_files = start_vars['output_files']
             if 'first_run' in start_vars: #ZS: check if first run so existing result files can be used if it isn't (for example zooming on a chromosome, etc)
                 self.first_run = False
-            self.score_type = "-log(p)"
+            self.score_type = "-logP"
             self.manhattan_plot = True
             with Bench("Running GEMMA"):
                 if self.use_loco == "True":
@@ -327,7 +327,7 @@ class RunMapping(object):
                                                                                                                                                              self.control_marker,
                                                                                                                                                              self.manhattan_plot)
         elif self.mapping_method == "plink":
-            self.score_type = "-log(p)"
+            self.score_type = "-logP"
             self.manhattan_plot = True
             results = plink_mapping.run_plink(self.this_trait, self.dataset, self.species, self.vals, self.maf)
             #results = self.run_plink()
@@ -539,8 +539,8 @@ def export_mapping_results(dataset, trait, markers, results_path, mapping_scale,
             output_file.write("Location: " + str(trait.chr) + " @ " + str(trait.mb) + " Mb\n")
         output_file.write("\n")
         output_file.write("Name,Chr,")
-        if score_type.lower() == "-log(p)":
-            score_type = "-log(p)"
+        if score_type.lower() == "-logP":
+            score_type = "-logP"
         if 'Mb' in markers[0]:
             output_file.write("Mb," + score_type)
         if 'cM' in markers[0]:
diff --git a/wqflask/wqflask/templates/mapping_results.html b/wqflask/wqflask/templates/mapping_results.html
index e68a792a..28d93542 100644
--- a/wqflask/wqflask/templates/mapping_results.html
+++ b/wqflask/wqflask/templates/mapping_results.html
@@ -99,7 +99,7 @@
                       LRS
                     
                     
                     
                        ?
@@ -235,8 +235,8 @@
                   
                   Row
                   Marker
-                  {% if LRS_LOD == "-log(p)" %}
-                  
    –log(p)
    
+                  {% if LRS_LOD == "-logP" %}
+                  
    –logP
    
                   {% else %}
                   
    {{ LRS_LOD }}
    
                   {% endif %}
@@ -259,7 +259,7 @@
                   
                   {{ loop.index }}
                   {% if geno_db_exists == "True" %}    {{ marker.name }}{% else %}{{ marker.name }}{% endif %}
-                  {% if LRS_LOD == "LOD" or LRS_LOD == "-log(p)" %}
+                  {% if LRS_LOD == "LOD" or LRS_LOD == "-logP" %}
                   {% if 'lod_score' in marker %}
                   {{ '%0.2f' | format(marker.lod_score|float) }}
                   {% else %}
-- 
cgit v1.2.3


From f1c15048407fa82557ecc89c1367b58ba65c859b Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 12 Nov 2020 17:28:40 -0600
Subject: Import fontawesome css/js and used its info character for glossary
 links on search results page

---
 wqflask/wqflask/templates/search_result_page.html | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/wqflask/wqflask/templates/search_result_page.html b/wqflask/wqflask/templates/search_result_page.html
index 36f144c2..8e2b06a4 100644
--- a/wqflask/wqflask/templates/search_result_page.html
+++ b/wqflask/wqflask/templates/search_result_page.html
@@ -4,6 +4,7 @@
     
     
     
+    
     
     
 {% endblock %}
@@ -184,6 +185,7 @@
     
     
     
+    
 
     
 
@@ -331,10 +333,10 @@
                       'orderSequence': [ "desc", "asc"]
                     },
                     {
-                      'title': "High P ?",
+                      'title': "High P ",
                       'type': "natural-minus-na",
                       'data': "lrs_score",
-                      'width': "60px",
+                      'width': "65px",
                       'orderSequence': [ "desc", "asc"]
                     },
                     {
@@ -344,10 +346,10 @@
                       'data': "lrs_location"
                     },
                     {
-                      'title': "Effect Size ?",
+                      'title': "Effect Size ",
                       'type': "natural-minus-na",
                       'data': "additive",
-                      'width': "85px",
+                      'width': "90px",
                       'orderSequence': [ "desc", "asc"]
                     }{% elif dataset.type == 'Publish' %},
                     {
@@ -400,7 +402,7 @@
                       'orderSequence': [ "desc", "asc"]
                     },
                     {
-                      'title': "High P ?",
+                      'title': "High P ",
                       'type': "natural-minus-na",
                       'data': "lrs_score",
                       'width': "80px",
@@ -413,7 +415,7 @@
                       'data': "lrs_location"
                     },
                     {
-                      'title': "Effect Size ?",
+                      'title': "Effect Size ",
                       'type': "natural-minus-na",
                       'width': "120px",
                       'data': "additive",
@@ -470,5 +472,4 @@
 
         });
     
-{% endblock %}
-
+{% endblock %}
\ No newline at end of file
-- 
cgit v1.2.3


From 26d882e2f1599bce23ec61501c49825c62707cac Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 13 Nov 2020 13:37:00 -0600
Subject: Replaced superscript i's for glossary links in search result table
 headers with fontawesome icons

---
 wqflask/wqflask/templates/search_result_page.html | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/wqflask/wqflask/templates/search_result_page.html b/wqflask/wqflask/templates/search_result_page.html
index 36f144c2..c163a99a 100644
--- a/wqflask/wqflask/templates/search_result_page.html
+++ b/wqflask/wqflask/templates/search_result_page.html
@@ -4,6 +4,7 @@
     
     
     
+    
     
     
 {% endblock %}
@@ -184,6 +185,7 @@
     
     
     
+    
 
     
 
@@ -331,10 +333,10 @@
                       'orderSequence': [ "desc", "asc"]
                     },
                     {
-                      'title': "High P ?",
+                      'title': "High P ",
                       'type': "natural-minus-na",
                       'data': "lrs_score",
-                      'width': "60px",
+                      'width': "65px",
                       'orderSequence': [ "desc", "asc"]
                     },
                     {
@@ -344,10 +346,10 @@
                       'data': "lrs_location"
                     },
                     {
-                      'title': "Effect Size ?",
+                      'title': "Effect Size ",
                       'type': "natural-minus-na",
                       'data': "additive",
-                      'width': "85px",
+                      'width': "90px",
                       'orderSequence': [ "desc", "asc"]
                     }{% elif dataset.type == 'Publish' %},
                     {
@@ -400,7 +402,7 @@
                       'orderSequence': [ "desc", "asc"]
                     },
                     {
-                      'title': "High P ?",
+                      'title': "High P ",
                       'type': "natural-minus-na",
                       'data': "lrs_score",
                       'width': "80px",
@@ -413,7 +415,7 @@
                       'data': "lrs_location"
                     },
                     {
-                      'title': "Effect Size ?",
+                      'title': "Effect Size ",
                       'type': "natural-minus-na",
                       'width': "120px",
                       'data': "additive",
-- 
cgit v1.2.3


From db0e80bef73eb163bfb747ec4d137d5c5069ade8 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 13 Nov 2020 13:40:40 -0600
Subject: Added cdn imports for fontawesome to correlation page and changed
 superscript i's to fontawesome icons for the glossary links in column headers

---
 wqflask/wqflask/templates/correlation_page.html | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html
index bb7d3df6..6ea86609 100644
--- a/wqflask/wqflask/templates/correlation_page.html
+++ b/wqflask/wqflask/templates/correlation_page.html
@@ -4,6 +4,7 @@
     
     
     
+    
     
 {% endblock %}
 {% block content %}
@@ -145,7 +146,8 @@
     
     
     
-    
+    
+    
 
     
-- 
cgit v1.2.3


From 76d5b24b3d2e0b5e6be5b1cf4c125a9a812ff57a Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 13 Nov 2020 15:25:32 -0600
Subject: Switched to using boolean casting to check if various trait
 attributes exist

---
 wqflask/wqflask/correlation/show_corr_results.py | 59 ++++++++++++------------
 1 file changed, 29 insertions(+), 30 deletions(-)

diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index 17d3de54..87dd7597 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -494,61 +494,60 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
             results_dict['mean'] = "N/A"
             results_dict['lrs_score'] = "N/A"
             results_dict['additive'] = "N/A"
-            if trait.description_display:
+            if bool(trait.description_display):
                 results_dict['description'] = trait.description_display
-            if trait.mean and trait.mean != "":
+            if bool(trait.mean):
                 results_dict['mean'] = float(trait.mean)
             if trait.LRS_score_repr != "N/A":
                 results_dict['lrs_score'] = "%.1f" % float(trait.LRS_score_repr)
             results_dict['lrs_location'] = trait.LRS_location_repr
-            if trait.additive and trait.additive != "":
+            if bool(trait.additive):
                 results_dict['additive'] = "%0.3f" % float(trait.additive)
             results_dict['sample_r'] = "%0.3f" % float(trait.sample_r)
             results_dict['num_overlap'] = trait.num_overlap
             results_dict['sample_p'] = "%0.3e" % float(trait.sample_p)
-            if trait.lit_corr == "" or trait.lit_corr == 0:
-                results_dict['lit_corr'] = "--"
-            else:
+            results_dict['lit_corr'] = "--"
+            results_dict['tissue_corr'] = "--"
+            results_dict['tissue_pvalue'] = "--"
+            if bool(trait.lit_corr):
                 results_dict['lit_corr'] = "%0.3f" % float(trait.lit_corr)
-            if trait.tissue_corr == "" or trait.tissue_corr == 0:
-                results_dict['tissue_corr'] = "--"
-                results_dict['tissue_pvalue'] = "--"
-            else:
+            if bool(trait.tissue_corr):
                 results_dict['tissue_corr'] = "%0.3f" % float(trait.tissue_corr)
                 results_dict['tissue_pvalue'] = "%0.3e" % float(trait.tissue_pvalue)
         elif target_dataset.type == "Publish":
             results_dict['abbreviation_display'] = "N/A"
-            if trait.abbreviation:
-                results_dict['abbreviation_display'] = trait.abbreviation
             results_dict['description'] = "N/A"
-            if trait.description_display:
-                results_dict['description'] = trait.description_display
-            results_dict['authors'] = trait.authors
-            authors_list = trait.authors.split(',')
-            if len(authors_list) > 6:
-                results_dict['authors_display'] = ", ".join(authors_list[:6]) + ", et al."
+            results_dict['authors_display'] = "N/A"
+            results_dict['additive'] = "N/A"
+            if for_api:
+                results_dict['pubmed_id'] = "N/A"
+                results_dict['year'] = "N/A"
             else:
-                results_dict['authors_display'] = trait.authors
-            if trait.pubmed_id:
+                results_dict['pubmed_link'] = "N/A"
+                results_dict['pubmed_text'] = "N/A"
+
+            if bool(trait.abbreviation):
+                results_dict['abbreviation_display'] = trait.abbreviation
+            if bool(trait.description_display):
+                results_dict['description'] = trait.description_display
+            if bool(trait.authors):
+                authors_list = trait.authors.split(',')
+                if len(authors_list) > 6:
+                    results_dict['authors_display'] = ", ".join(authors_list[:6]) + ", et al."
+                else:
+                    results_dict['authors_display'] = trait.authors
+            if bool(trait.pubmed_id):
                 if for_api:
                     results_dict['pubmed_id'] = trait.pubmed_id
                     results_dict['year'] = trait.pubmed_text
                 else:
                     results_dict['pubmed_link'] = trait.pubmed_link
                     results_dict['pubmed_text'] = trait.pubmed_text
-            else:
-                if for_api:
-                    results_dict['pubmed_id'] = "N/A"
-                    results_dict['year'] = "N/A"
-                else:
-                    results_dict['pubmed_link'] = "N/A"
-                    results_dict['pubmed_text'] = "N/A"
+
             results_dict['lrs_score'] = trait.LRS_score_repr
             results_dict['lrs_location'] = trait.LRS_location_repr
-            if trait.additive and trait.additive != "":
+            if bool(trait.additive):
                 results_dict['additive'] = "%0.3f" % float(trait.additive)
-            else:
-                results_dict['additive'] = "N/A"
             results_dict['sample_r'] = "%0.3f" % trait.sample_r
             results_dict['num_overlap'] = trait.num_overlap
             results_dict['sample_p'] = "%0.3e" % float(trait.sample_p)
-- 
cgit v1.2.3


From 678d9cd4e073c3936c34d3b9b54f1e6c590def27 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 13 Nov 2020 15:37:49 -0600
Subject: Switched to using f-strings instead of the older method of string
 formatting

---
 wqflask/wqflask/correlation/show_corr_results.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index 87dd7597..1b801b1d 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -499,21 +499,21 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
             if bool(trait.mean):
                 results_dict['mean'] = float(trait.mean)
             if trait.LRS_score_repr != "N/A":
-                results_dict['lrs_score'] = "%.1f" % float(trait.LRS_score_repr)
+                results_dict['lrs_score'] = f"{float(trait.LRS_score_repr):.1f}"
             results_dict['lrs_location'] = trait.LRS_location_repr
             if bool(trait.additive):
-                results_dict['additive'] = "%0.3f" % float(trait.additive)
-            results_dict['sample_r'] = "%0.3f" % float(trait.sample_r)
+                results_dict['additive'] = f"{float(trait.additive):.3f}"
+            results_dict['sample_r'] = f"{float(trait.sample_r):.3f}"
             results_dict['num_overlap'] = trait.num_overlap
-            results_dict['sample_p'] = "%0.3e" % float(trait.sample_p)
+            results_dict['sample_p'] = f"{float(trait.sample_p):.3e}"
             results_dict['lit_corr'] = "--"
             results_dict['tissue_corr'] = "--"
             results_dict['tissue_pvalue'] = "--"
             if bool(trait.lit_corr):
-                results_dict['lit_corr'] = "%0.3f" % float(trait.lit_corr)
+                results_dict['lit_corr'] = f"{float(trait.lit_corr):.3f}"
             if bool(trait.tissue_corr):
-                results_dict['tissue_corr'] = "%0.3f" % float(trait.tissue_corr)
-                results_dict['tissue_pvalue'] = "%0.3e" % float(trait.tissue_pvalue)
+                results_dict['tissue_corr'] = f"{float(trait.tissue_corr):.3f}"
+                results_dict['tissue_pvalue'] = f"{float(trait.tissue_pvalue):.3e}"
         elif target_dataset.type == "Publish":
             results_dict['abbreviation_display'] = "N/A"
             results_dict['description'] = "N/A"
@@ -547,15 +547,15 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
             results_dict['lrs_score'] = trait.LRS_score_repr
             results_dict['lrs_location'] = trait.LRS_location_repr
             if bool(trait.additive):
-                results_dict['additive'] = "%0.3f" % float(trait.additive)
-            results_dict['sample_r'] = "%0.3f" % trait.sample_r
+                results_dict['additive'] = f"{float(trait.additive):.3f}"
+            results_dict['sample_r'] = f"{float(trait.sample_r):.3f}"
             results_dict['num_overlap'] = trait.num_overlap
-            results_dict['sample_p'] = "%0.3e" % float(trait.sample_p)
+            results_dict['sample_p'] = f"{float(trait.sample_p):.3e}"
         else:
             results_dict['location'] = trait.location_repr
-            results_dict['sample_r'] = "%0.3f" % trait.sample_r
+            results_dict['sample_r'] = f"{float(trait.sample_r):.3f}"
             results_dict['num_overlap'] = trait.num_overlap
-            results_dict['sample_p'] = "%0.3e" % float(trait.sample_p)
+            results_dict['sample_p'] = f"{float(trait.sample_p):.3e}"
 
         results_list.append(results_dict)
 
-- 
cgit v1.2.3


From 61d5c7710d044c507d69e600e04a40067cf7320f Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Mon, 16 Nov 2020 20:23:21 +0300
Subject: update refactored natural_sort function in show_trait/SampleList.py

---
 wqflask/wqflask/show_trait/SampleList.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/show_trait/SampleList.py b/wqflask/wqflask/show_trait/SampleList.py
index 191c29bd..349f6b65 100644
--- a/wqflask/wqflask/show_trait/SampleList.py
+++ b/wqflask/wqflask/show_trait/SampleList.py
@@ -112,7 +112,7 @@ class SampleList(object):
             self.attributes[key].name = name
             self.attributes[key].distinct_values = [
                 item.Value for item in values]
-            natural_sort(self.attributes[key].distinct_values)
+            self.attributes[key].distinct_values=natural_sort(self.attributes[key].distinct_values)
             all_numbers = True
             for value in self.attributes[key].distinct_values:
                 try:
-- 
cgit v1.2.3


From ceeae6fd087d91637c5007e0433ce347048fa96d Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Tue, 17 Nov 2020 22:31:10 +0300
Subject: use better function names

---
 .../tests/wqflask/marker_regression/test_gemma_mapping.py  | 14 +++++++-------
 .../tests/wqflask/marker_regression/test_rqtl_mapping.py   |  2 +-
 .../tests/wqflask/marker_regression/test_run_mapping.py    |  2 +-
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
index af20c1b8..4fafd95a 100644
--- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
@@ -23,7 +23,7 @@ class MockDatasetGroup(AttributeSetter):
 class TestGemmaMapping(unittest.TestCase):
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output")
-    def test_run_gemma_first_run_loco_set_false(self, mock_parse_loco):
+    def test_run_gemma_firstrun_set_false(self, mock_parse_loco):
         """add tests for gemma function where  first run is set to false"""
         dataset = AttributeSetter(
             {"group": AttributeSetter({"genofile": "genofile.geno"})})
@@ -49,7 +49,7 @@ class TestGemmaMapping(unittest.TestCase):
     @mock.patch("wqflask.marker_regression.run_mapping.random.choice")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
     @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file")
-    def test_run_gemma_first_run_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco):
+    def test_run_gemma_firstrun_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco):
         """add tests for run_gemma where first run is set to true"""
         chromosomes = []
         for i in range(1, 5):
@@ -140,7 +140,7 @@ class TestGemmaMapping(unittest.TestCase):
                 '-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('\n')])
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img/")
-    def test_parse_gemma_output_obj_returned(self):
+    def test_parse_gemma_output(self):
         """add test for generating gemma output with obj returned"""
         file = """X/Y\t gn2\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5
 X/Y\tgn2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5
@@ -157,7 +157,7 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4
             self.assertEqual(results, expected)
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img")
-    def test_parse_gemma_output_empty_return(self):
+    def test_parse_gemma_output_with_empty_return(self):
         """add tests for parse gemma output where nothing returned"""
         output_file_results = """chr\t today"""
         with mock.patch("builtins.open", mock.mock_open(read_data=output_file_results)) as mock_open:
@@ -167,7 +167,7 @@ X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
     @mock.patch("wqflask.marker_regression.gemma_mapping.json")
-    def test_parse_loco_output_file_found(self, mock_json, mock_os):
+    def test_parse_loco_outputfile_found(self, mock_json, mock_os):
         """add tests for parse loco output file found"""
         mock_json.load.return_value = {
             "files": [["file_name", "user", "~/file1"],
@@ -193,8 +193,8 @@ chr\tL3\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7"""
 
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
-    def test_parse_loco_output_file_not_found(self, mock_os):
-        """add tests for parse loco output file not found"""
+    def test_parse_loco_outputfile_not_found(self, mock_os):
+        """add tests for parse loco output where  output file not found"""
 
         mock_os.path.isfile.return_value = False
         file_to_write = """{"files":["file_1","file_2"]}"""
diff --git a/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py b/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
index 9fd32a30..69f53721 100644
--- a/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
@@ -17,7 +17,7 @@ class TestRqtlMapping(unittest.TestCase):
 
 	@mock.patch("wqflask.marker_regression.rqtl_mapping.g")
 	@mock.patch("wqflask.marker_regression.rqtl_mapping.logger")
-	def test_get_trait_data_type_found(self,mock_logger,mock_db):
+	def test_get_trait_data(self,mock_logger,mock_db):
 		"""test for getting trait data_type return True"""
 		caller_value="""SELECT value FROM TraitMetadata WHERE type='trait_data_type'"""
 		mock_db.db.execute.return_value.fetchone.return_value=["""{"type":"trait_data_type","name":"T1","traid_id":"fer434f"}"""]
diff --git a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
index 2af4c3e3..a134f551 100644
--- a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
+++ b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
@@ -62,7 +62,7 @@ class TestRunMapping(unittest.TestCase):
         self.assertEqual(result_2, [])
 
     @mock.patch("wqflask.marker_regression.run_mapping.data_set")
-    def test_geno_db_exists(self, mock_data_set):
+    def test_if_geno_db_exists(self, mock_data_set):
         mock_data_set.create_dataset.side_effect = [
             AttributeSetter({}), Exception()]
         results_no_error = geno_db_exists(self.dataset)
-- 
cgit v1.2.3


From 9d3493f2e8ee2d2185854b95d2b13a5d2b4e38e1 Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Wed, 18 Nov 2020 02:18:45 +0300
Subject: use f strings in marker_regression/plink_mapping

---
 wqflask/wqflask/marker_regression/plink_mapping.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/wqflask/wqflask/marker_regression/plink_mapping.py b/wqflask/wqflask/marker_regression/plink_mapping.py
index 8d57d556..5d675c38 100644
--- a/wqflask/wqflask/marker_regression/plink_mapping.py
+++ b/wqflask/wqflask/marker_regression/plink_mapping.py
@@ -9,11 +9,11 @@ import utility.logger
 logger = utility.logger.getLogger(__name__ )
 
 def run_plink(this_trait, dataset, species, vals, maf):
-    plink_output_filename = webqtlUtil.genRandStr("%s_%s_"%(dataset.group.name, this_trait.name))
+    plink_output_filename = webqtlUtil.genRandStr(f"{dataset.group.name}_{this_trait.name}_")
     gen_pheno_txt_file(dataset, vals)
 
-    plink_command = PLINK_COMMAND + ' --noweb --bfile %s/%s --no-pheno --no-fid --no-parents --no-sex --maf %s --out %s%s --assoc ' % (
-        flat_files('mapping'), dataset.group.name, maf, TMPDIR, plink_output_filename)
+    
+    plink_command = f"{PLINK_COMMAND}  --noweb --bfile {flat_files('mapping')}/{dataset.group.name} --no-pheno --no-fid --no-parents --no-sex --maf {maf} --out { TMPDIR}{plink_output_filename} --assoc "
     logger.debug("plink_command:", plink_command)
 
     os.system(plink_command)
@@ -29,12 +29,12 @@ def gen_pheno_txt_file(this_dataset, vals):
     """Generates phenotype file for GEMMA/PLINK"""
 
     current_file_data = []
-    with open("{}/{}.fam".format(flat_files('mapping'), this_dataset.group.name), "r") as outfile:
+    with open(f"{flat_files('mapping')}/{this_dataset.group.name}.fam", "r") as outfile:
         for i, line in enumerate(outfile):
             split_line = line.split()
             current_file_data.append(split_line)
 
-    with open("{}/{}.fam".format(flat_files('mapping'), this_dataset.group.name), "w") as outfile:
+    with open(f"{flat_files('mapping')}/{this_dataset.group.name}.fam","w") as outfile:
         for i, line in enumerate(current_file_data):
             if vals[i] == "x":
                 this_val = -9
@@ -44,8 +44,8 @@ def gen_pheno_txt_file(this_dataset, vals):
 
 def gen_pheno_txt_file_plink(this_trait, dataset, vals, pheno_filename = ''):
     ped_sample_list = get_samples_from_ped_file(dataset)
-    output_file = open("%s%s.txt" % (TMPDIR, pheno_filename), "wb")
-    header = 'FID\tIID\t%s\n' % this_trait.name
+    output_file = open(f"{TMPDIR}{pheno_filename}.txt", "wb")
+    header = f"FID\tIID\t{this_trait.name}\n"
     output_file.write(header)
 
     new_value_list = []
@@ -65,7 +65,7 @@ def gen_pheno_txt_file_plink(this_trait, dataset, vals, pheno_filename = ''):
     for i, sample in enumerate(ped_sample_list):
         j = i+1
         value = new_value_list[i]
-        new_line += '%s\t%s\t%s\n'%(sample, sample, value)
+        new_line += f"{sample}\t{sample}\t{value}\n"
 
         if j%1000 == 0:
             output_file.write(newLine)
@@ -78,7 +78,7 @@ def gen_pheno_txt_file_plink(this_trait, dataset, vals, pheno_filename = ''):
 
 # get strain name from ped file in order
 def get_samples_from_ped_file(dataset):
-    ped_file= open("{}{}.ped".format(flat_files('mapping'), dataset.group.name), "r")
+    ped_file= open(f"{flat_files('mapping')}{dataset.group.name}.ped","r")
     line = ped_file.readline()
     sample_list=[]
 
@@ -98,7 +98,7 @@ def parse_plink_output(output_filename, species):
 
     threshold_p_value = 1
 
-    result_fp = open("%s%s.qassoc"% (TMPDIR, output_filename), "rb")
+    result_fp = open(f"{TMPDIR}{output_filename}.qassoc","rb")
 
     line = result_fp.readline()
 
-- 
cgit v1.2.3


From 77dff447c83a4b0824fe6626d16484cfd826ca77 Mon Sep 17 00:00:00 2001
From: Alexanderlacuna
Date: Wed, 18 Nov 2020 03:06:27 +0300
Subject: use f strings in marker_regression/qtlreaper_mapping.py

---
 .../wqflask/marker_regression/qtlreaper_mapping.py | 46 +++++++++++++---------
 1 file changed, 28 insertions(+), 18 deletions(-)

diff --git a/wqflask/wqflask/marker_regression/qtlreaper_mapping.py b/wqflask/wqflask/marker_regression/qtlreaper_mapping.py
index 78b1f7b0..505ae295 100644
--- a/wqflask/wqflask/marker_regression/qtlreaper_mapping.py
+++ b/wqflask/wqflask/marker_regression/qtlreaper_mapping.py
@@ -17,22 +17,29 @@ def run_reaper(this_trait, this_dataset, samples, vals, json_data, num_perm, boo
         else:
             genofile_name = this_dataset.group.name
 
-        trait_filename = str(this_trait.name) + "_" + str(this_dataset.name) + "_pheno"
+        trait_filename =f"{str(this_trait.name)}_{str(this_dataset.name)}_pheno"
         gen_pheno_txt_file(samples, vals, trait_filename)
 
-        output_filename = this_dataset.group.name + "_GWA_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
+        output_filename = (f"{this_dataset.group.name}_GWA_"+
+            ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
+            )
         bootstrap_filename = None
         permu_filename = None
 
         opt_list = []
         if boot_check and num_bootstrap > 0:
-            bootstrap_filename = this_dataset.group.name + "_BOOTSTRAP_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
+            bootstrap_filename = (f"{this_dataset.group.name}_BOOTSTRAP_" + 
+                ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
+                )
 
             opt_list.append("-b")
-            opt_list.append("--n_bootstrap " + str(num_bootstrap))
-            opt_list.append("--bootstrap_output " + webqtlConfig.GENERATED_IMAGE_DIR + bootstrap_filename + ".txt")
+            opt_list.append(f"--n_bootstrap{str(num_bootstrap)}")
+            opt_list.append(f"--bootstrap_output{webqtlConfig.GENERATED_IMAGE_DIR}{bootstrap_filename}.txt")
         if num_perm > 0:
-            permu_filename = this_dataset.group.name + "_PERM_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
+            permu_filename =("{this_dataset.group.name}_PERM_" + 
+            ''.join(random.choice(string.ascii_uppercase + 
+                string.digits) for _ in range(6))
+            )
             opt_list.append("-n " + str(num_perm))
             opt_list.append("--permu_output " + webqtlConfig.GENERATED_IMAGE_DIR + permu_filename + ".txt")
         if control_marker != "" and do_control == "true":
@@ -40,13 +47,15 @@ def run_reaper(this_trait, this_dataset, samples, vals, json_data, num_perm, boo
         if manhattan_plot != True:
             opt_list.append("--interval 1")
 
-        reaper_command = REAPER_COMMAND + ' --geno {0}/{1}.geno --traits {2}/gn2/{3}.txt {4} -o {5}{6}.txt'.format(flat_files('genotype'),
-                                                                                                                genofile_name,
-                                                                                                                TEMPDIR,
-                                                                                                                trait_filename,
-                                                                                                                " ".join(opt_list),
-                                                                                                                webqtlConfig.GENERATED_IMAGE_DIR,
-                                                                                                                output_filename)
+        reaper_command = (REAPER_COMMAND + 
+        ' --geno {0}/{1}.geno --traits {2}/gn2/{3}.txt {4} -o {5}{6}.txt'.format(flat_files('genotype'),
+
+                                                                              genofile_name,
+                                                                              TEMPDIR,
+                                                                              trait_filename,
+                                                                              " ".join(opt_list),
+                                                                              webqtlConfig.GENERATED_IMAGE_DIR,
+                                                                            output_filename))
 
         logger.debug("reaper_command:" + reaper_command)
         os.system(reaper_command)
@@ -61,12 +70,13 @@ def run_reaper(this_trait, this_dataset, samples, vals, json_data, num_perm, boo
         suggestive = permu_vals[int(num_perm*0.37-1)]
         significant = permu_vals[int(num_perm*0.95-1)]
 
-    return marker_obs, permu_vals, suggestive, significant, bootstrap_vals, [output_filename, permu_filename, bootstrap_filename]
+    return (marker_obs, permu_vals, suggestive, significant, bootstrap_vals, 
+        [output_filename, permu_filename, bootstrap_filename])
 
 def gen_pheno_txt_file(samples, vals, trait_filename):
     """Generates phenotype file for GEMMA"""
 
-    with open("{}/gn2/{}.txt".format(TEMPDIR, trait_filename), "w") as outfile:
+    with open(f"{TEMPDIR}/gn2/{trait_filename}.txt","w") as outfile:
         outfile.write("Trait\t")
 
         filtered_sample_list = []
@@ -90,7 +100,7 @@ def parse_reaper_output(gwa_filename, permu_filename, bootstrap_filename):
     only_cm = False
     only_mb = False
 
-    with open("{}{}.txt".format(webqtlConfig.GENERATED_IMAGE_DIR, gwa_filename)) as output_file:
+    with open(f"{webqtlConfig.GENERATED_IMAGE_DIR}{gwa_filename}.txt") as output_file:
         for line in output_file:
             if line.startswith("ID\t"):
                 if len(line.split("\t")) < 8:
@@ -137,13 +147,13 @@ def parse_reaper_output(gwa_filename, permu_filename, bootstrap_filename):
 
     permu_vals = []
     if permu_filename:
-        with open("{}{}.txt".format(webqtlConfig.GENERATED_IMAGE_DIR, permu_filename)) as permu_file:
+        with open(f"{webqtlConfig.GENERATED_IMAGE_DIR}{permu_filename}.txt") as permu_file:
             for line in permu_file:
                 permu_vals.append(float(line))
 
     bootstrap_vals = []
     if bootstrap_filename:
-        with open("{}{}.txt".format(webqtlConfig.GENERATED_IMAGE_DIR, bootstrap_filename)) as bootstrap_file:
+        with open(f"{webqtlConfig.GENERATED_IMAGE_DIR}{bootstrap_filename}.txt") as bootstrap_file:
             for line in bootstrap_file:
                 bootstrap_vals.append(int(line))
 
-- 
cgit v1.2.3


From 4ab6c9e9744e3a681a885599cddfeebde513ed2a Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 18 Nov 2020 12:31:50 -0600
Subject: Fixed "back_to_collections" function in get_traits_from_collection.js
 (function that lets you return from viewing a collection to viewing the list
 of collections when selecting cofactors for scatterplot)

---
 wqflask/wqflask/static/new/javascript/get_traits_from_collection.js | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js b/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js
index 4ec62157..8f6f389f 100644
--- a/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js
+++ b/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js
@@ -397,8 +397,8 @@ process_traits = function(trait_data, textStatus, jqXHR) {
 };
 
 back_to_collections = function() {
-  collection_list_html = $('#collection_list_html').html()
-  $("#collections_holder").html(collection_list_html);
+  console.log("collection_list:", collection_list);
+  $("#collections_holder").html(collection_list);
   $(document).on("click", ".collection_line", collection_click);
   return $('#collections_holder').colorbox.resize();
 };
-- 
cgit v1.2.3


From 2256312c8d2fdf11b78c894bdf030f640e6a2158 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 18 Nov 2020 12:33:48 -0600
Subject: Removed console.log and commented out statements from
 get_traits_from_collection.js

---
 .../new/javascript/get_traits_from_collection.js       | 18 +++---------------
 1 file changed, 3 insertions(+), 15 deletions(-)

diff --git a/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js b/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js
index 8f6f389f..a55ab356 100644
--- a/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js
+++ b/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js
@@ -2,10 +2,6 @@
 var add_trait_data, assemble_into_json, back_to_collections, collection_click, collection_list, color_by_trait, create_trait_data_csv, get_this_trait_vals, get_trait_data, process_traits, selected_traits, submit_click, this_trait_data, trait_click,
   __indexOf = [].indexOf || function(item) { for (var i = 0, l = this.length; i < l; i++) { if (i in this && this[i] === item) return i; } return -1; };
 
-console.log("before get_traits_from_collection");
-
-//collection_list = null;
-
 this_trait_data = null;
 
 selected_traits = {};
@@ -69,7 +65,7 @@ if ( ! $.fn.DataTable.isDataTable( '#collection_table' ) ) {
 
 collection_click = function() {
   var this_collection_url;
-  //console.log("Clicking on:", $(this));
+
   this_collection_url = $(this).find('.collection_name').prop("href");
   this_collection_url += "&json";
   collection_list = $("#collections_holder").html();
@@ -87,9 +83,7 @@ submit_click = function() {
   $('#collections_holder').find('input[type=checkbox]:checked').each(function() {
     var this_dataset, this_trait, this_trait_url;
     this_trait = $(this).parents('tr').find('.trait').text();
-    console.log("this_trait is:", this_trait);
     this_dataset = $(this).parents('tr').find('.dataset').text();
-    console.log("this_dataset is:", this_dataset);
     this_trait_url = "/trait/get_sample_data?trait=" + this_trait + "&dataset=" + this_dataset;
     return $.ajax({
       dataType: "json",
@@ -147,7 +141,7 @@ create_trait_data_csv = function(selected_traits) {
     }
     all_vals.push(this_trait_vals);
   }
-  console.log("all_vals:", all_vals);
+
   trait_vals_csv = trait_names.join(",");
   trait_vals_csv += "\n";
   for (index = _k = 0, _len2 = samples.length; _k < _len2; index = ++_k) {
@@ -168,7 +162,7 @@ create_trait_data_csv = function(selected_traits) {
 
 trait_click = function() {
   var dataset, this_trait_url, trait;
-  console.log("Clicking on:", $(this));
+
   trait = $(this).parent().find('.trait').text();
   dataset = $(this).parent().find('.dataset').text();
   this_trait_url = "/trait/get_sample_data?trait=" + trait + "&dataset=" + dataset;
@@ -182,7 +176,6 @@ trait_click = function() {
 
 trait_row_click = function() {
   var dataset, this_trait_url, trait;
-  console.log("Clicking on:", $(this));
   trait = $(this).find('.trait').text();
   dataset = $(this).find('.dataset').data("dataset");
   this_trait_url = "/trait/get_sample_data?trait=" + trait + "&dataset=" + dataset;
@@ -256,7 +249,6 @@ populate_cofactor_info = function(trait_info) {
 get_trait_data = function(trait_data, textStatus, jqXHR) {
   var sample, samples, this_trait_vals, trait_sample_data, vals, _i, _len;
   trait_sample_data = trait_data[1];
-  console.log("IN GET TRAIT DATA")
   if ( $('input[name=allsamples]').length ) {
     samples = $('input[name=allsamples]').val().split(" ");
   } else {
@@ -362,13 +354,11 @@ assemble_into_json = function(this_trait_vals) {
 };
 
 color_by_trait = function(trait_sample_data, textStatus, jqXHR) {
-  console.log('in color_by_trait:', trait_sample_data);
   return root.bar_chart.color_by_trait(trait_sample_data);
 };
 
 process_traits = function(trait_data, textStatus, jqXHR) {
   var the_html, trait, _i, _len;
-  console.log('in process_traits with trait_data:', trait_data);
   the_html = "";
   the_html += "    ";
@@ -397,13 +387,11 @@ process_traits = function(trait_data, textStatus, jqXHR) {
 };
 
 back_to_collections = function() {
-  console.log("collection_list:", collection_list);
   $("#collections_holder").html(collection_list);
   $(document).on("click", ".collection_line", collection_click);
   return $('#collections_holder').colorbox.resize();
 };
 
-console.log("inside get_traits_from_collection");
 $(".collection_line").on("click", collection_click);
 $("#submit").on("click", submit_click);
 if ($('#scatterplot2').length){
-- 
cgit v1.2.3


From 7a2a59f3d225f94bda5a9bc51a958d3203ea690c Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 18 Nov 2020 12:48:43 -0600
Subject: Fixed issue that was causing scatterplot cofactors to not work if the
 cofactors were genotypes (because it tried to get the "description_display"
 which doesn't exist for genotype traits)

---
 wqflask/base/trait.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/wqflask/base/trait.py b/wqflask/base/trait.py
index cfc02f8b..7763dbe8 100644
--- a/wqflask/base/trait.py
+++ b/wqflask/base/trait.py
@@ -265,11 +265,12 @@ def get_sample_data():
         trait_dict['species'] = trait_ob.dataset.group.species
         trait_dict['url'] = url_for(
             'show_trait_page', trait_id=trait, dataset=dataset)
-        trait_dict['description'] = trait_ob.description_display
         if trait_ob.dataset.type == "ProbeSet":
             trait_dict['symbol'] = trait_ob.symbol
             trait_dict['location'] = trait_ob.location_repr
+            trait_dict['description'] = trait_ob.description_display
         elif trait_ob.dataset.type == "Publish":
+            trait_dict['description'] = trait_ob.description_display
             if trait_ob.pubmed_id:
                 trait_dict['pubmed_link'] = trait_ob.pubmed_link
             trait_dict['pubmed_text'] = trait_ob.pubmed_text
-- 
cgit v1.2.3


From 56445de585552ecb60c62d608f510f01fabc454b Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 18 Nov 2020 13:44:38 -0600
Subject: Added some jquery closing the "Add To Collection" colorbox window
 after its form is submitted

---
 wqflask/wqflask/templates/collections/add.html | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/wqflask/wqflask/templates/collections/add.html b/wqflask/wqflask/templates/collections/add.html
index 62b6abb5..b4e5385b 100644
--- a/wqflask/wqflask/templates/collections/add.html
+++ b/wqflask/wqflask/templates/collections/add.html
@@ -50,4 +50,7 @@
 
 
-- 
cgit v1.2.3


From 518bdbd8f956596e1cee189fe026a71863156fac Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 18 Nov 2020 13:48:46 -0600
Subject: Changed default Low/High colors for the correlation scatterplot
 cofactor coloring to Blue/Red respectively (was originally Light Grey/Black)

---
 wqflask/wqflask/templates/corr_scatterplot.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/templates/corr_scatterplot.html b/wqflask/wqflask/templates/corr_scatterplot.html
index 1fd5cd15..1133fcd2 100644
--- a/wqflask/wqflask/templates/corr_scatterplot.html
+++ b/wqflask/wqflask/templates/corr_scatterplot.html
@@ -81,9 +81,9 @@
   
-- 
cgit v1.2.3


From 6ed037083f0b2bac95021e5fb00c0c8877422a47 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 18 Nov 2020 14:06:27 -0600
Subject: Fixed issue where the cofactor trait descriptions didn't work
 correctly for genotype/snp cofactors (the code previously only accounted for
 probeset/phenotype cofactors,  so it was treating genotypes/snps like
 phenotype traits)

---
 wqflask/base/trait.py                              |  2 ++
 .../new/javascript/get_traits_from_collection.js   | 24 ++++++++++++++++------
 2 files changed, 20 insertions(+), 6 deletions(-)

diff --git a/wqflask/base/trait.py b/wqflask/base/trait.py
index 7763dbe8..0f8f937c 100644
--- a/wqflask/base/trait.py
+++ b/wqflask/base/trait.py
@@ -274,6 +274,8 @@ def get_sample_data():
             if trait_ob.pubmed_id:
                 trait_dict['pubmed_link'] = trait_ob.pubmed_link
             trait_dict['pubmed_text'] = trait_ob.pubmed_text
+        else:
+            trait_dict['location'] = trait_ob.location_repr
 
         return json.dumps([trait_dict, {key: value.value for
                                         key, value in list(
diff --git a/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js b/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js
index a55ab356..626357d4 100644
--- a/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js
+++ b/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js
@@ -201,13 +201,17 @@ populate_cofactor_info = function(trait_info) {
     if (trait_info['type'] == "ProbeSet"){
       $('#cofactor1_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['tissue'] + " " + trait_info['db'] + ": " + trait_info['name'])
       $('#cofactor1_description').text("[" + trait_info['symbol'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description'])
-    } else {
+    } else if (trait_info['type'] == "Publish") {
       $('#cofactor1_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name'])
       if ('pubmed_link' in trait_info) {
         $('#cofactor1_description').html('PubMed: ' + trait_info['pubmed_text'] + '
    ' + trait_info['description'])
       } else {
-        $('#cofactor1_description').html('PubMed: ' + trait_info['pubmed_text'] + '
    ' + trait_info['description'])
+        $('#cofactor1_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name'])
+        $('#cofactor1_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description'])
       }
+    } else {
+      $('#cofactor1_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name'])
+      $('#cofactor1_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n")
     }
     $('#select_cofactor1').text("Change Cofactor 1");
     $('#cofactor1_info_container').css("display", "inline");
@@ -217,13 +221,17 @@ populate_cofactor_info = function(trait_info) {
     if (trait_info['type'] == "ProbeSet"){
       $('#cofactor2_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['tissue'] + " " + trait_info['db'] + ": " + trait_info['name'])
       $('#cofactor2_description').text("[" + trait_info['symbol'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description'])
-    } else {
+    } else if (trait_info['type'] == "Publish") {
       $('#cofactor2_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name'])
       if ('pubmed_link' in trait_info) {
         $('#cofactor2_description').html('PubMed: ' + trait_info['pubmed_text'] + '
    ' + trait_info['description'])
       } else {
-        $('#cofactor2_description').html('PubMed: ' + trait_info['pubmed_text'] + '
    ' + trait_info['description'])
+        $('#cofactor2_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name'])
+        $('#cofactor2_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description'])
       }
+    } else {
+      $('#cofactor2_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name'])
+      $('#cofactor2_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n")
     }
     $('#select_cofactor2').text("Change Cofactor 2");
     $('#cofactor2_info_container').css("display", "inline");
@@ -233,13 +241,17 @@ populate_cofactor_info = function(trait_info) {
     if (trait_info['type'] == "ProbeSet"){
       $('#cofactor3_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['tissue'] + " " + trait_info['db'] + ": " + trait_info['name'])
       $('#cofactor3_description').text("[" + trait_info['symbol'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description'])
-    } else {
+    } else if (trait_info['type'] == "Publish") {
       $('#cofactor3_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name'])
       if ('pubmed_link' in trait_info) {
         $('#cofactor3_description').html('PubMed: ' + trait_info['pubmed_text'] + '
    ' + trait_info['description'])
       } else {
-        $('#cofactor3_description').html('PubMed: ' + trait_info['pubmed_text'] + '
    ' + trait_info['description'])
+        $('#cofactor3_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name'])
+        $('#cofactor3_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description'])
       }
+    } else {
+      $('#cofactor3_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name'])
+      $('#cofactor3_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n")
     }
     $('#select_cofactor3').text("Change Cofactor 3");
     $('#cofactor3_info_container').css("display", "inline");
-- 
cgit v1.2.3


From 6cc806e65bee5652bbe761c10079017a5b44a160 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 19 Nov 2020 14:38:04 -0600
Subject: Removed lines that check that all traits are part of the same group,
 since it might be the case in the future that different groups still share
 sample names (and it's not really necessary since we check how many samples
 are shraed between each individual pair of traits)

---
 .../wqflask/correlation_matrix/show_corr_matrix.py | 210 ++++++++++-----------
 1 file changed, 101 insertions(+), 109 deletions(-)

diff --git a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
index a394f548..0269ce68 100644
--- a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
+++ b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py
@@ -55,11 +55,7 @@ class CorrelationMatrix(object):
         self.do_PCA = True
         this_group = self.trait_list[0][1].group.name #ZS: Getting initial group name before verifying all traits are in the same group in the following loop
         for trait_db in self.trait_list:
-            if trait_db[1].group.name != this_group:
-                self.insufficient_shared_samples = True
-                break
-            else:
-                this_group = trait_db[1].group.name
+            this_group = trait_db[1].group.name
             this_trait = trait_db[0]
             self.traits.append(this_trait)
             this_sample_data = this_trait.data
@@ -68,119 +64,115 @@ class CorrelationMatrix(object):
                 if sample not in self.all_sample_list:
                     self.all_sample_list.append(sample)
 
-        if self.insufficient_shared_samples:
-            pass
-        else:
-            self.sample_data = []
-            for trait_db in self.trait_list:
-                this_trait = trait_db[0]
-                this_sample_data = this_trait.data
+        self.sample_data = []
+        for trait_db in self.trait_list:
+            this_trait = trait_db[0]
+            this_sample_data = this_trait.data
 
-                this_trait_vals = []
-                for sample in self.all_sample_list:
-                    if sample in this_sample_data:
-                        this_trait_vals.append(this_sample_data[sample].value)
-                    else:
-                        this_trait_vals.append('')
-                self.sample_data.append(this_trait_vals)
-
-            if len(this_trait_vals) < len(self.trait_list): #Shouldn't do PCA if there are more traits than observations/samples
-                self.do_PCA = False
-
-            self.lowest_overlap = 8 #ZS: Variable set to the lowest overlapping samples in order to notify user, or 8, whichever is lower (since 8 is when we want to display warning)
-
-            self.corr_results = []
-            self.pca_corr_results = []
-            self.shared_samples_list = self.all_sample_list
-            for trait_db in self.trait_list:
-                this_trait = trait_db[0]
-                this_db = trait_db[1]
-
-                this_db_samples = this_db.group.all_samples_ordered()
-                this_sample_data = this_trait.data
-
-                corr_result_row = []
-                pca_corr_result_row = []
-                is_spearman = False #ZS: To determine if it's above or below the diagonal
-                for target in self.trait_list:
-                    target_trait = target[0]
-                    target_db = target[1]
-                    target_samples = target_db.group.all_samples_ordered()
-                    target_sample_data = target_trait.data
-
-                    this_trait_vals = []
-                    target_vals = []
-                    for index, sample in enumerate(target_samples):
-                        if (sample in this_sample_data) and (sample in target_sample_data):
-                            sample_value = this_sample_data[sample].value
-                            target_sample_value = target_sample_data[sample].value
-                            this_trait_vals.append(sample_value)
-                            target_vals.append(target_sample_value)
-                        else:
-                            if sample in self.shared_samples_list:
-                                self.shared_samples_list.remove(sample)
-
-                    this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(this_trait_vals, target_vals)
-
-                    if num_overlap < self.lowest_overlap:
-                        self.lowest_overlap = num_overlap
-                    if num_overlap < 2:
-                        corr_result_row.append([target_trait, 0, num_overlap])
-                        pca_corr_result_row.append(0)
-                    else:
-                        pearson_r, pearson_p = scipy.stats.pearsonr(this_trait_vals, target_vals)
-                        if is_spearman == False:
-                            sample_r, sample_p = pearson_r, pearson_p
-                            if sample_r == 1:
-                                is_spearman = True
-                        else:
-                            sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals)
-
-                        corr_result_row.append([target_trait, sample_r, num_overlap])
-                        pca_corr_result_row.append(pearson_r)
-
-                self.corr_results.append(corr_result_row)
-                self.pca_corr_results.append(pca_corr_result_row)
-
-            self.trait_data_array = []
-            for trait_db in self.trait_list:
-                this_trait = trait_db[0]
-                this_db = trait_db[1]
-                this_db_samples = this_db.group.all_samples_ordered()
-                this_sample_data = this_trait.data
+            this_trait_vals = []
+            for sample in self.all_sample_list:
+                if sample in this_sample_data:
+                    this_trait_vals.append(this_sample_data[sample].value)
+                else:
+                    this_trait_vals.append('')
+            self.sample_data.append(this_trait_vals)
+
+        if len(this_trait_vals) < len(self.trait_list): #Shouldn't do PCA if there are more traits than observations/samples
+            self.do_PCA = False
+
+        self.lowest_overlap = 8 #ZS: Variable set to the lowest overlapping samples in order to notify user, or 8, whichever is lower (since 8 is when we want to display warning)
+
+        self.corr_results = []
+        self.pca_corr_results = []
+        self.shared_samples_list = self.all_sample_list
+        for trait_db in self.trait_list:
+            this_trait = trait_db[0]
+            this_db = trait_db[1]
+
+            this_db_samples = this_db.group.all_samples_ordered()
+            this_sample_data = this_trait.data
+
+            corr_result_row = []
+            pca_corr_result_row = []
+            is_spearman = False #ZS: To determine if it's above or below the diagonal
+            for target in self.trait_list:
+                target_trait = target[0]
+                target_db = target[1]
+                target_samples = target_db.group.all_samples_ordered()
+                target_sample_data = target_trait.data
 
                 this_trait_vals = []
-                for index, sample in enumerate(this_db_samples):
-                    if (sample in this_sample_data) and (sample in self.shared_samples_list):
+                target_vals = []
+                for index, sample in enumerate(target_samples):
+                    if (sample in this_sample_data) and (sample in target_sample_data):
                         sample_value = this_sample_data[sample].value
+                        target_sample_value = target_sample_data[sample].value
                         this_trait_vals.append(sample_value)
-                self.trait_data_array.append(this_trait_vals)
+                        target_vals.append(target_sample_value)
+                    else:
+                        if sample in self.shared_samples_list:
+                            self.shared_samples_list.remove(sample)
 
-            corr_result_eigen = np.linalg.eig(np.array(self.pca_corr_results))
-            corr_eigen_value, corr_eigen_vectors = sortEigenVectors(corr_result_eigen)
+                this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(this_trait_vals, target_vals)
 
-            groups = []
-            for sample in self.all_sample_list:
-                groups.append(1)
-
-            try:
-                if self.do_PCA == True:
-                    self.pca_works = "True"
-                    self.pca_trait_ids = []
-                    pca = self.calculate_pca(list(range(len(self.traits))), corr_eigen_value, corr_eigen_vectors)
-                    self.loadings_array = self.process_loadings()
+                if num_overlap < self.lowest_overlap:
+                    self.lowest_overlap = num_overlap
+                if num_overlap < 2:
+                    corr_result_row.append([target_trait, 0, num_overlap])
+                    pca_corr_result_row.append(0)
                 else:
-                    self.pca_works = "False"
-            except:
-                self.pca_works = "False"
+                    pearson_r, pearson_p = scipy.stats.pearsonr(this_trait_vals, target_vals)
+                    if is_spearman == False:
+                        sample_r, sample_p = pearson_r, pearson_p
+                        if sample_r == 1:
+                            is_spearman = True
+                    else:
+                        sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals)
+
+                    corr_result_row.append([target_trait, sample_r, num_overlap])
+                    pca_corr_result_row.append(pearson_r)
 
-            self.js_data = dict(traits = [trait.name for trait in self.traits],
-                                groups = groups,
-                                cols = list(range(len(self.traits))),
-                                rows = list(range(len(self.traits))),
-                                samples = self.all_sample_list,
-                                sample_data = self.sample_data,)
-            #                    corr_results = [result[1] for result in result_row for result_row in self.corr_results])
+            self.corr_results.append(corr_result_row)
+            self.pca_corr_results.append(pca_corr_result_row)
+
+        self.trait_data_array = []
+        for trait_db in self.trait_list:
+            this_trait = trait_db[0]
+            this_db = trait_db[1]
+            this_db_samples = this_db.group.all_samples_ordered()
+            this_sample_data = this_trait.data
+
+            this_trait_vals = []
+            for index, sample in enumerate(this_db_samples):
+                if (sample in this_sample_data) and (sample in self.shared_samples_list):
+                    sample_value = this_sample_data[sample].value
+                    this_trait_vals.append(sample_value)
+            self.trait_data_array.append(this_trait_vals)
+
+        corr_result_eigen = np.linalg.eig(np.array(self.pca_corr_results))
+        corr_eigen_value, corr_eigen_vectors = sortEigenVectors(corr_result_eigen)
+
+        groups = []
+        for sample in self.all_sample_list:
+            groups.append(1)
+
+        try:
+            if self.do_PCA == True:
+                self.pca_works = "True"
+                self.pca_trait_ids = []
+                pca = self.calculate_pca(list(range(len(self.traits))), corr_eigen_value, corr_eigen_vectors)
+                self.loadings_array = self.process_loadings()
+            else:
+                self.pca_works = "False"
+        except:
+            self.pca_works = "False"
+
+        self.js_data = dict(traits = [trait.name for trait in self.traits],
+                            groups = groups,
+                            cols = list(range(len(self.traits))),
+                            rows = list(range(len(self.traits))),
+                            samples = self.all_sample_list,
+                            sample_data = self.sample_data,)
 
     def calculate_pca(self, cols, corr_eigen_value, corr_eigen_vectors):
         base = importr('base')
-- 
cgit v1.2.3


From 97d3e0907640fd32f9ff49a79f8a453852727f72 Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Fri, 20 Nov 2020 18:45:02 +0300
Subject: Remove glossary markdown template

* wqflask/wqflask/static/markdown/: Delete folder. Files moved to
https://github.com/genenetwork/gn-docs
---
 wqflask/wqflask/static/markdown/glossary.md | 618 ----------------------------
 1 file changed, 618 deletions(-)
 delete mode 100644 wqflask/wqflask/static/markdown/glossary.md

diff --git a/wqflask/wqflask/static/markdown/glossary.md b/wqflask/wqflask/static/markdown/glossary.md
deleted file mode 100644
index db94ae18..00000000
--- a/wqflask/wqflask/static/markdown/glossary.md
+++ /dev/null
@@ -1,618 +0,0 @@
-# Glossary of Terms and Features
-
-
    
-
-[A](#a) | [B](#b) | [C](#c)| [D](#d) | [E](#e) | [F](#f) | [G](#g) | [H](#h) | [I](#i) | [J](#j) | [K](#k) | [L](#l) | [M](#m) | [N](#n) | [O](#o) | [P](#p) | [Q](#q) | [R](#r) | [S](#s) | [T](#t) | [U](#u) | [V](#v) | [W](#w) | [X](#x) | [Y](#y) | [Z](#z)
-
-You are welcome to cite or reproduce these glossary
-definitions. Please cite or link: Author AA. "Insert Glossary Term
-Here." From The WebQTL Glossary--A GeneNetwork
-Resource. gn1.genenetwork.org/glossary.html
-
-
    
-
-## A
-
-
    
-
-#### Additive Allele Effect:
-
-The additive allele effect is an estimate of the change in the average phenotype that would be produced by substituting a single allele of one type with that of another type (e.g., a replaced by A) in a population. In a standard F2 intercross between two inbred parental lines there are two alleles at every polymorphic locus that are often referred to as the little "a" allele and big "A" allele. F2 progeny inherit the a/a, a/A, or A/A genotypes at every genetic locus in a ratio close to 1:2:1. The additive effect is half of the difference between the mean of all cases that are homozygous for one parental allele (aa) compared to the mean of all cases that are homozygous for the other parental allele (AA):
-
-[(mean of AA cases)-(mean of aa cases)]/2
-
-GeneNetwork displays the additive values on the far right of many trait/QTL maps, usually as red or green lines along the maps. The units of measurement of additive effects (and dominance effects) are defined by the trait itself and are shown in **Trait Data and Analysis** windows. For mRNA estimates these units are usually normalized log2 expression values. For this reason an additive effect of 0.5 units indicates that the A/A and a/a genotypes at a particular locus or marker differ by 1 unit (twice the effect of swapping a single A allele for an a allele). On this log2 scale this is equivalent to a 2-fold difference (2 raised to the power of 1).
-
-On the QTL map plots the polarity of allele effects is represented by the color of the line. For example, in mouse BXD family maps, if the DBA/2J allele produces higher values than the C57BL/6J allele then the additive effect line is colored in green. In contrast, if the C57BL/6J allele produces higher values then the line is colored in red. For computational purposes, C57BL/6J red values are considered negative.
-
-The dominance effects of alleles are also computed on maps for F2 populations (e.g., B6D2F2 and B6BTBRF2). Orange and purple line colors are used to distinguish the polarity of effects. Purple is the positive dominance effect that matches the polarity of the green additive effect, whereas orange is the negative dominance effect that matches the polarity of the red additive effect. [Please also see entry on **Dominance Effects**: Williams RW, Oct 15, 2004; Sept 3, 2005; Dec 4, 2005; Oct 25, 2011]
-
-[Go back to index](#index)
-
-
    
-
-
    
-
-#### Bootstrap:
-
-A [bootstrap sample](http://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29) is a randomly drawn sample (or resample) that is taken from the original data set and that has the same number of samples as the original data set. In a single bootstrap sample, some cases will by chance be represented one or more times; other cases may not be represented at all (in other words, the sampling is done "with replacement" after each selection). To get a better intuitive feel for the method, imagine a bag of 26 Scrabble pieces that contain each letter of the English alphabet. In a bootstrap sample of these 26 pieces, you would shake the bag, insert your hand, and draw out one piece. You would then write down that letter on a piece of paper, and the place that Scrabble piece back in the bag in preparation for the next random selection. You would repeat this process (shake, draw, replace) 25 more times to generate a single bootstrap resample of the alphabet. Some letters will be represented several time in each sample and others will not be represented at al. If you repeat this procedure 1000 times you would have a set of bootstrap resamples of the type that GN uses to remap data sets.
-
-Bootstrap resampling is a method that can be used to estimate statistical parameters and error terms. GeneNetwork uses a bootstrap procedure to evaluate approximate confidence limits of QTL peaks using a method proposed by Peter Visscher and colleagues ([1996](http://www.genetics.org/content/143/2/1013.full.pdf)). We generate 2000 bootstraps, remap each, and keep track of the location of the single locus with the highest LRS score locations (equivalent to a "letter" in the Scrabble example). The 2000 "best" locations are used to produce the yellow histograms plotted on some of the QTL maps. If the position of a QTL is firm, then the particular composition of the sample, will not shift the position of the QTL peak by very much. In such a case, the histogram of "best QTLs" (yellow bars in the maps) that is displayed in WebQTL maps will tend to have a sharp peak (the scale is the percentage of bootstrap resamples that fall into each bar of the bootstrap histogram). In contrast, if the the yellow bootstrap histograms are spread out along a chromosome, then the precise location of a QTL may not be accurate, even in the original correct data set. Bootstrap results naturally vary between runs due to the random generation of the samples. See the related entry "Frequency of Peak LRS."
-
-KNOWN PROBLEMS and INTERPRETATION of BOOTSTRAP RESULTS: The reliability of bootstrap analysis of QTL confidence intervals has been criticized by Manichaikul and colleagues ([2006](http://www.genetics.org/cgi/content/full/174/1/481)). Their work applies in particular to standard intercrosses and backcrosses in which markers are spaced every 2 cM. They recommend that confidence intervals be estimated either by a conventional 1.5 to 2.0 LOD drop-off interval or by a Bayes credible Interval method.
-
-There is a known flaw in the way in which GeneNetwork displays bootstrap results (Sept 2011). If a map has two or more adjacent markers with identical LOD score and identical strain distribution patterns, all of the bootstrap results are assigned incorrectly to just one of the "twin" markers. This results in a false perception of precision.
-
-QTL mapping methods can be highly sensitive to cases with very high or very low phenotype values (outliers). The bootstrap method does not provide protection against the effects of outliers and their effects on QTL maps. Make sure you review your data for outliers before mapping. Options include (1) Do nothing, (2) Delete the outliers and see what happens to your maps, (3) [Winsorize](http://en.wikipedia.org/wiki/Winsorising) the values of the outliers. You might try all three options and determine if your main results are stable or not. With small samples or extreme outliers, you may find the mapping results to be quite volatile. In general, if the results (QTL position or value) depend highly on one or two outliers (5-10% of the samples) then you should probably delete or winsorize the outliers. [Williams RW, Oct 15, 2004, Mar 15, 2008, Mar 26, 2008; Sept 2011] 
-    
-[Go back to index](#index)
-
-
    
-
-#### CEL and DAT Files (Affymetrix):
-
-Array data begin as raw image files that are generated using a confocal microscope and video system. Affymetrix refers to these image data files as DAT files. The DAT image needs to be registered to a template that assigns pixel values to expected array coordinates (cells). The result is an assignment of a set of image intensity values (pixel intensities) to each probe. For example, each cell/probe value generated using Affymetrix arrays is associated with approximately 36 pixels (a 6x6 set of pixels, usually with an effective 11 or 12-bit range of intensity). Affymetrix uses a method that simply ranks the values of these pixels and picks as the "representative value" the pixel that is has rank 24 from low to high. The range of variation in intensity amoung these ranked pixels provides a way to estimate the error of the estimate. The Affymetrix CEL files therefore consist of XY coordinates, the consensus value, and an error term. [Williams RW, April 30, 2005] 
-
-#### Cluster Map or QTL Cluster Map:
-
-Cluster maps are sets of QTL maps for a group of traits. The QTL maps for the individual traits (up to 100) are run side by side to enable easy detection of common and unique QTLs. Traits are clustered along one axis of the map by phenotypic similarity (hierarchical clustering) using the Pearson product-moment correlation r as a measurement of similarity (we plot 1-r as the distance). Traits that are positively correlated will be located near to each other. The genome location is shown along the other, long axis of the cluster map, marker by marker, from Chromosome 1 to Chromosome X. Colors are used to encode the probability of linkage, as well as the additive effect polarity of alleles at each marker. These QTL maps are computed using the fast Marker Regression algorithm. P values for each trait are computed by permuting each trait 1000 times. Cluster maps could be considered trait gels because each lane is loaded with a trait that is run out along the genome. Cluster maps are a unique feature of the GeneNetwork developed by Elissa Chesler and implemented in WebQTL by J Wang and RW Williams, April 2004. [Williams RW, Dec 23, 2004, rev June 15, 2006 RWW]. 
-
-#### Collections and Trait Collections:
-
-One of the most powerful features of GeneNetwork (GN) is the ability to study large sets of traits that have been measured using a common genetic reference population or panel (GRP). This is one of the key requirements of systems genetics--many traits studied in common. Under the main GN menu **Search** heading you will see a link to **Trait Collections**. You can assemble you own collection for any GRP by simply adding items using the Add to Collection button that you will find in many windows. Once you have a collection you will have access to a new set of tools for analysis of your collection, including **QTL Cluster Map, Network Graph, Correlation Matrix**, and **Compare Correlates**. [Williams RW, April 7, 2006] 
-
-#### Complex Trait Analysis:
-
-Complex trait analysis is the study of multiple causes of variation of phenotypes within species. Essentially all traits that vary within a population are modulated by a set of genetic and environmental factors. Finding and characterizing the multiple genetic sources of variation is referred to as "genetic dissection" or "QTL mapping." In comparison, complex trait analysis has a slightly broader focus and includes the analysis of the effects of environmental perturbation, and gene-by-environment interactions on phenotypes; the "norm of reaction." Please also see the glossary term "Systems Genetics." [Williams RW, April 12, 2005] 
-
-#### Composite Interval Mapping:
-
-Composite interval mapping is a method of mapping chromosomal regions that controls for some fraction of the genetic variability in a quantitative trait. Unlike simple interval mapping, composite interval mapping usually controls for variation produced at one or more background marker loci. These background markers are generally chosen because they are already known to be close to the location of a significant QTL. By factoring out a portion of the genetic variance produced by a major QTL, one can occasionally detect secondary QTLs. WebQTL allows users to control for a single background marker. To select this marker, first run the **Marker Regression** analysis (and if necessary, check the box labeled display all LRS, select the appropriate locus, and the click on either **Composite Interval Mapping** or **Composite Regression**. A more powerful and effective alternative to composite interval mapping is pair-scan analysis. This latter method takes into accounts (models) both the independent effects of two loci and possible two-locus epistatic interactions. [Williams RW, Dec 20, 2004] 
-
-
    
-
-#### Correlations: Pearson and Spearman:
-
-GeneNetwork provides tools to compute both Pearson product-moment correlations (the standard type of correlation), Spearman rank order correlations. [Wikipedia](http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient) and introductory statistics text will have a discussion of these major types of correlation. The quick advice is to use the more robust Spearman rank order correlation if the number of pairs of observations in a data set is less than about 30 and to use the more powerful but much more sensitive Pearson product-moment correlation when the number of observations is greater than 30 AND after you have dealt with any outliers. GeneNetwork automatically flags outliers for you in the Trait Data and Analysis form. GeneNetwork also allows you to modify values by either deleting or winsorising them. That means that you can use Pearson correlations even with smaller sample sizes after making sure that data are well distributed. Be sure to view the scatterplots associated with correlation values (just click on the value to generate a plot). Look for bivariate outliers. 
-
-#### Cross:
-
-The term Cross refers to a group of offspring made by mating (crossing) one strain with another strain. There are several types of crosses including intercrosses, backcrosses, advanced intercrosses, and recombinant inbred intercrosses. Genetic crosses are almost always started by mating two different but fully inbred strains to each other. For example, a B6D2F2 cross is made by breeding C57BL/6J females (B6 or B for short) with DBA/2J males (D2 or D) and then intercrossing their F1 progeny to make the second filial generation (F2). By convention the female is always listed first in cross nomenclature; B6D2F2 and D2B6F2 are therefore so-called reciprocal F2 intercrosses (B6D2F1 females to B6D2F1 males or D2B6F1 females to D2B6F1 males). A cross may also consist of a set of recombinant inbred (RI) strains such as the BXD strains, that are actually inbred progeny of a set of B6D2F2s. Crosses can be thought of as a method to randomize the assignment of blocks of chromosomes and genetic variants to different individuals or strains. This random assignment is a key feature in testing for causal relations. The strength with which one can assert that a causal relation exists between a chromosomal location and a phenotypic variant is measured by the LOD score or the LRS score (they are directly convertable, where LOD = LRS/4.61) [Williams RW, Dec 26, 2004; Dec 4, 2005]. 
-    
-[Go back to index](#index)
-
-
    
-
-#### Dominance Effects:
-
-The term dominance indicates that the phenotype of intercross progeny closely resemble one of the two parental lines, rather than having an intermediate phenotype. Geneticists commonly refer to an allele as having a dominance effect or dominance deviation on a phenotype. Dominance deviation at a particular marker are calculated as the difference between the average phenotype of all cases that have the Aa genotype at that marker and the expected value half way between the all casese that have the aa genotype and the AA genotype. For example, if the average phenotype value of 50 individuals with the aa genotype is 10 units whereas that of 50 individuals with the AA genotype is 20 units, then we would expect the average of 100 cases with the Aa genotype to be 15 units. We are assuming a linear and perfectly additive model of how the a and A alleles interact. If these 100 Aa cases actually have a mean of 11 units, then this additive model would be inadequate. A non-linear dominance terms is now needed. In this case the low a alleles is almost perfectly dominant (or semi-dominant) and the dominance deviation is -4 units.
-
-The dominance effects are computed at each location on the maps generated by the WebQTL module for F2 populations (e.g., B6D2F2 and B6BTBRF2). Orange and purple line colors are used to distinguish the polarity of the dominance effects. Purple is the positive dominance effect that matches the polarity of the green additive effect, whereas orange is the negative dominance effect that matches the polarity of the red additive effect.
-
-Note that dominance deviations cannot be computed from a set of recombinant inbred strains because there are only two classes of genotypes at any marker (aa and AA, more usuually written AA and BB). However, when data for F1 hybrids are available one can estimate the dominance of the trait. This global phenotypic dominance has almost nothing to do with the dominance deviation at a single marker in the genome. In other words, the dominance deviation detected at a single marker may be reversed or neutralized by the action of many other polymorphic genes. [Williams RW, Dec 21, 2004; Sept 3, 2005] 
-    
-[Go back to index](#index)
-
-
    
-
-#### Epistasis:
-
-Epistasis means that combined effects of two or more different loci or polymorphic genes are not what one would expect given the addition of their individual effects. There is, in other words, evidence for non-linear interactions among two or more loci. This is similar to the dominance effects mentioned above, but now generalized to two or more distinct loci, rather than to two or more alleles at a single locus. For example, if QTL 1 has an A allele that has an additive effects of +5 and QTL 2 has an A alleles that has an additive effect of +2, then the two locus genotype combination A/A would be expected to boost the mean by +7 units. But if the value of these A/A individuals was actually -7 we would be quite surprised and would refer to this as an epistatic interaction between QTL 1 and QTL 2. WebQTL will search for all possible epistatic interactions between pairs of loci in the genome. This function is called a **Pair Scan** becasue the software analyzes the LRS score for all possible pairs of loci. Instead of viewing an LRS plot along a single dimension, we now view a two-dimensional plot that shows a field of LRS scores computed for pairs of loci. Pair scan plots are extremely sensitive to outlier data. Be sure to review the primary data carefully using **Basic Statistics**. Also note that this more sophisiticated method also demands a significantly larger sample size. While 25 to 50 cases may be adequate for a conventional LRS plot (sometimes called a "main scan"), a **Pair-Scan** is hard to apply safely with fewer than 60 cases. [Williams RW, Dec 21, 2004; Dec 5, 2005] 
-
-#### Effect Size of a QTL:
-
-QTLs can be ranked by the amount of variance that they explain--their so-called "effect size"--when they are included in a statistical model. The concept of a genetic model may seem odd to some users of GeneNetwork. A model is just an explicit hypothesis of how QTLs and other factors cause variation in a trait. QTL mapping involves comparisons of the explanatory power of different models. Effect sizes can be measured in different units including (1) the percentage of total or genetic variance that is explained by adding the QTL into the model, (2) the mean shift in Z score, or (3) the additive effect size expressed in the original measurement scale. Effects of single QTLs are often dependent on genetic background (i.e., other QTLs and their interactions) and on the numbers and types of cases used in a study. For example, the variance explained is influenced strongly by whether the sample are from a family cohort, a case-control cohort, a group of fully inbred strains such as recombinant inbred lines, an outcross or backcross population.
-
-Please note that the functional importance of a locus, QTL, or GWAS hit can not be predicted by the size of its effect on the trait in one environment, at one stage of development, and in one population. Estimates of the effect size of QTLs are usually both noisy and upwardly biased (overestimated), and both of these problems are particularly acute when sample sizes are small.
-
-Estimates of effect size for families of inbred lines, such as the BXD, HXB, CC, and hybrid diversity panels (e.g. the hybrid mouse diversity panel and the hybrid rat diversity panel) are typically (and correctly) much higher than those measured in otherwise similar analysis of intercrosses, heterogeneous stock (HS), or diversity outbred stock. Two factors contribute to the much higher level of explained variance of QTLs when using inbred strain panels.
-
-
-1. **Replication Rate:** The variance that can be explained by a locus is increased by sampling multiple cases that have identical genomes and by using the strain mean for genetic analysis. Increasing replication rates from 1 to 6 can easily double the apparent heritability of a trait and therefore the effect size of a locus. The reason is simple—resampling decrease the standard error of mean, boosting the effective heritability (see Glossary entry on *Heritability* and focus on figure 1 from the Belknap [1998](http://gn1.genenetwork.org/images/upload/Belknap_Heritability_1998.pdf) paper reproduced below).
    Compare the genetically explained variance (labeled h2RI in this figure) of a single case (no replication) on the x-axis with the function at a replication rate of 4 on the y-axis. If the explained variance is 0.1 (10% of all variance explained) then the value is boosted to 0.3 (30% of strain mean variance explained) with n = 4.
-
-2. **Homozygosity:** The second factor has to do with the inherent genetic variance of populations. Recombinant inbred lines are homozygous at nearly all loci. This doubles the genetic variance in a family of recombinant inbred lines compared to a matched number of F2s. This also quadruples the variance compared to a matched number of backcross cases. As a result 40 BXDs sampled just one per genometype will average 2X the genetic variance and 2X the heritability of 40 BDF2 cases. Note that panels made up of isogenic F1 hybrids (so-called diallel crosses, DX) made by crossing recombinant inbred strains (BXD, CC, or HXB) are no longer homozygous at all loci, and while they do expose important new sources of variance associated with dominance, they do not benefit from the 2X gain in genetic variance relative to an F2 intercross.
-
-Homozygosity
-
-For the reasons listed above a QTL effect size of 0.4 detected a panel of BXD lines replicated four times each (160 cases total), corresponds approximately to an effect size of 0.18 in BXDs without replication (40 cases total), and to an effect size of 0.09 in an F2 of 40 cases total. [Williams RW, Dec 23, 2004; updated by RWW July 13, 2019] 
-
-#### eQTL, cis eQTL, trans eQTL
-
-An expression QTL or eQTL. Differences in the expression of mRNA or proteins are often treated as standard phenotypes, much like body height or lung capacity. The variation in these microscopic traits (microtraits) can be mapped using conventional QTL methods. [Damerval](http://www.genetics.org/cgi/reprint/137/1/289) and colleagues were the first authors to use this kind of nomenclature and in their classic study of 1994 introduced the term PQLs for protein quantitative trait loci. Schadt and colleagues added the acronym eQTL in their early mRNA study of corn, mouse, and humans. We now are "blessed" with all kinds of prefixes to QTLs that highlight the type of trait that has been measured (m for metabolic, b for behavioral, p for physiological or protein).
-
-eQTLs of mRNAs and proteins have the unique property of (usually) having a single parent gene and genetic location. An eQTL that maps to the location of the parent gene that produces the mRNA or protein is referred to as a **cis eQTL** or local eQTL. In contrast, an eQTL that maps far away from its parent gene is referred to as a **trans eQTL**. You can use special search commands in GeneNetwork to find cis and trans eQTLs. [Williams RW, Nov 23, 2009, Dec 2009] 
-    
-[Go back to index](#index)
-
-
    
-
-## F
-
-#### Frequency of Peak LRS:
-
-The height of the yellow bars in some of the Map View windows provides a measure of the confidence with which a trait maps to a particular chromosomal region. WebQTL runs 2000 bootstrap samples of the original data. (A bootstrap sample is a "sample with replacement" of the same size as the original data set in which some samples will by chance be represented one of more times and others will not be represented at all.) For each of these 2000 bootstraps, WebQTL remaps each and keeps track of the location of the single locus with the highest LRS score. These accumulated locations are used to produce the yellow histogram of "best locations." A frequency of 10% means that 200 of 2000 bootstraps had a peak score at this location. It the mapping data are robust (for example, insensitive to the exclusion of an particular case), then the bootstrap bars should be confined to a short chromosomal interval. Bootstrap results will vary slightly between runs due to the random generation of the bootstrap samples. [Williams RW, Oct 15, 2004] 
-
-#### False Discovery Rate (FDR):
-
-A [false discovery](http://en.wikipedia.org/wiki/False_discovery_rate) is an apparently significant finding--usually determined using a particular P value alpha criterion--that given is known to be insignificant or false given other information. When performing a single statistical test we often accept a false discovery rate of 1 in 20 (p = .05). False discovery rates can climb to high levels in large genomic and genetic studies in which hundreds to millions of tests are run and summarized using standard "single test" p values. There are various statistical methods to estimate and control false discovery rate and to compute genome-wide p values that correct for large numbers of implicit or explicit statistical test. The Permutation test in GeneNetwork is one method that is used to prevent and excessive number of false QTL discoveries. Methods used to correct the FDR are approximations and may depend on a set of assumptions about data and sample structure. [Williams RW, April 5, 2008] 
-    
-[Go back to index](#index)
-
-
    
-
-## G
-
-#### Genes, GenBankID, UniGeneID, GeneID, LocusID:
-
-GeneNetwork provides summary information on most of the genes and their transcripts. Genes and their alternative splice variants are often are poorly annotated and may not have proper names or symbols. However, almost all entries have a valid GenBank accession identifier. This is a unique code associated with a single sequence deposited in GenBank (Entrez Nucleotide). A single gene may have hundreds of GenBank entries. GenBank entries that share a genomic location and possibly a single gene are generally combined into a single UniGene entry. For mouse, these always begin with "Mm" (Mus musculus) and are followed by a period and then a number. More than half of all mouse UniGene identifiers are associated with a reputable gene, and these genes will have gene identifiers (GeneID). GeneIDs are identical to LocusLink identifiers (LocusID). Even a 10 megabase locus such as human Myopia 4 (MYP4) that is not yet associated with a specific gene is assigned a GeneID--a minor misnomer and one reason to prefer the term LocusID.
-
-See the related [FAQ](http://gn1.genenetwork.org/faq.html#Q-6) on "How many genes and transcripts are in your databases and what fraction of the genome is being surveyed?" [Williams RW, Dec 23, 2004, updated Jan 2, 2005] 
-
-#### Genetic Reference Population (GRP):
-
-A genetic reference population consists of a set of genetically well characterized lines that are often used over a long period of time to study a multitude of different phenotypes. Once a GRP has been genotyped, subsequent studies can focus on the analysis of interesting and important phenotypes and their joint and independent relations. Most of the mouse GRPs, such as the BXDs used in the GeneNetwork, have been typed using a common set of over 14,000 makers (SNPs and microsatellites). Many of these same GRPs have been phenotyped extensively for more than 25 years, resulting in rich sets of phenotypes. A GRP is an ideal long-term resource for systems genetics because of the relative ease with which vast amounts of diverse data can be accumulated, analyzed, and combined.
-
-The power of GRPs and their compelling scientific advantages derive from the ability to study multiple phenotypes and substantial numbers of genetically defined individuals under one or more environmental conditions. When accurate phenotypes from 20 or more lines in a GRP have been acquired it becomes practical to explore and test the genetic correlations between that trait and any previously measured trait in the same GRP. This fact underlies the use of the term **reference** in GRP. Since each genetic individual is represented by an entire isogenic line--usually an inbred strain or an isogenic F1 hybrid--it is possible to obtain accurate mean phenotypes associated with each line simply by typing several individuals. GRPs are also ideal for developmental and aging studies because the same genetic individual can be phenotyped at multiple stages.
-
-A GRP can also be used a conventional mapping panel. But unlike most other mapping panel, a GRP can be easily adapted to jointly map sets of functionally related traits (multitrait mapping); a more powerful method to extract causal relations from networks of genetic correlations.
-
-The largest GRPs now consist of more than 400 recombinant inbred lines of *Arabidopsis* and [maize](http://www.maizegdb.org/cgi-bin/stockadvquery.cgi?check=true&name=&typebox=true&type=701&linkage_group=0&genvar1=&genvar2=&genvar3=&karyovar=0&phenotype=0&attribution=&avail_from=0&parent=0). The BayxSha Arabidopsis set in the GeneNetwork consists of 420 lines. Pioneer Hi-Bred International is rumored to have as many as 4000 maize RI lines. The largest mammalian GRPs are the LXS and BXD RI sets in the GeneNetwork. The Collaborative Cross is the largest mammalian GRP, and over 600 of these strains are now being bred by members of the Complex Trait Consortium.
-
-There are several subtypes of GRPs. In addition to recombinant inbred strains there are
-
-
-- Recombinant congenic ([RCC](http://research.jax.org/grs/type/recombcong.htmll)) strains such as the [AcB](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=11374899&query_hl=4) set Consomic or chromosome substitution strains ([CSS](http://research.jax.org/grs/type/consomic.html)) of mice (Matin et al., [1999](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=10508525&query_hl=11)) and rats (Roman et al., [2002](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=12858554&query_hl=7))
-    
-- Recombinant intercross ([RIX](http://www.ncbi.nlm.nih.gov/pubmed/?term=15879512)) F1 sets made by mating different RI strains to each other to generate large set of R! first generation (F1) progeny (RIX). This is a standard ([diallel cross](http://en.wikipedia.org/wiki/Diallel_cross)) of RI inbred strains. Genetic analysis of a set of RIX progeny has some advantages over a corresponding analysis of RI strains. The first of these is that while each set of F1 progeny is fully isogenic (AXB1 x AXB2 gives a set of isogenic F1s), these F1s are not inbred but are heterozygous at many loci across the genome. RIX therefore retain the advance of being genetically defined and replicable, but without the disadvantage of being fully inbred. RIX have a genetic architecture more like natural populations. The second correlated advantage is that it is possible to study patterns of dominance of allelic variants using an RIX cross. Almost all loci or genes that differs between the original stock strains (A and B) will be heterozygous among a sufficiently larges set of RIX. A set of RIX progeny can therefore be mapped using the same methods used to map an F2 intercross. Mapping of QTLs may have somewhat more power and precision than when RI strains are used alone. A third advantage is that RIX sets make it possible to expand often limited RI resources to very large sizes to confirm and extend models of genetic or GXE effects. For example a set of 30 AXB strains can be used to generate a full matrix of 30 x 29 unique RIX progeny. The main current disadvantage of RIX panels is the comparative lack of extant phenotype data.
-    
-- Recombinant F1 line sets can also be made by backcrossing an entire RI sets to a single inbred line that carries an interesting mutation or transgene (RI backcross or RIB). GeneNetwork includes one RI backcross sets generated by Kent Hunter. In this RIB each of 18 AKXD RI strains were crossed to an FVB/N line that carries a tumor susceptibility allele (polyoma middle T). 
-  
-All of these sets of lines are GRPs since each line is genetically defined and because the set as a whole can in principle be easily regenerated and phenotyped. Finally, each of these resources can be used to track down genetic loci that are causes of variation in phenotype using variants of standard linkage analysis.
-
-A Diversity Panel such as that used by the Mouse Phenome Project is not a standard GRPs, although its also shares the ability to accumulate and study networks of phenotypes. The main difference is that a Diversity Panel cannot be used for conventional linkage analysis. A sufficiently large Diversity Panel could in principle be used for the equivalent of an assocation study. However, these are definitely NOT in silico studies, because hundreds of individuals need to be phenotyped for every trait. Surveys of many diverse isogenic lines (inbred or F1 hybrids) is statistically the equivalent of a human association study (the main difference is the ability to replicate measurements and study sets of traits) and therefore, like human association studies, does require very high sample size to map polygenic traits. Like human association studies there is also a high risk of false positive results due to population stratification and non-syntenic marker association.
-
-A good use of a Diversity Panel is as a fine-mapping resource with which to dissect chromosomal intervals already mapped using a conventional cross or GRP. GeneNetwork now includes Mouse Diversity Panel (MDP) data for several data sets. We now typically include all 16 sequenced strains of mice, and add PWK/PhJ, NZO/HiLtJ (two of the eight members of the Collaborative Cross), and several F1 hybrids. The MDP data is often appended at the bottom of the GRP data set with which is was acquired (e.g., BXD hippocampal and BXD eye data sets). [Williams RW, June 19, 2005; Dec 4, 2005] 
-
-#### Genotype
-    
-The state of a gene or DNA sequence, usually used to describe a contrast between two or more states, such as that between the normal state (wildtype) and a mutant state (mutation) or between the alleles inherited from two parents. All species that are included in GeneNetwork are diploid (derived from two parents) and have two copies of most genes (genes located on the X and Y chromosomes are exceptions). As a result the genotype of a particular diploid individual is actually a pair of genotypes, one from each parents. For example, the offspring of a mating between strain A and strain B will have one copy of the A genotype and one copy of the B genotype and therefore have an A/B genotype. In contrast, offspring of a mating between a female strain A and a male strain A will inherit only A genotypes and have an A/A genotype.
-
-Genotypes can be measured or inferred in many different ways, even by visual inspection of animals (e.g. as Gregor Mendel did long before DNA was discovered). But now the typical method is to directly test DNA that has a well define chromosomal location that has been obtained from one or usually many cases using molecular tests that often rely on polymerase chain reaction steps and sequence analysis. Each case is genotyped at many chromosomal locations (loci, markers, or genes). The entire collection of genotypes (as many a 1 million for a single case) is also sometimes referred to as the cases genotype, but the word "genometype" might be more appropriate to highlight the fact that we are now dealing with a set of genotypes spanning the entire genome (all chromosomes) of the case.
-
-For gene mapping purposes, genotypes are often translated from letter codes (A/A, A/B, and B/B) to simple numerical codes that are more suitable for computation. A/A might be represented by the value -1, A/B by the value 0, and B/B by the value +1. This recoding makes it easy to determine if there is a statistically significant correlation between genotypes across of a set of cases (for example, an F2 population or a Genetic Reference Panel) and a variable phenotype measured in the same population. A sufficiently high correlation between genotypes and phenotypes is referred to as a quantitative trait locus (QTL). If the correlation is almost perfect (r > 0.9) then correlation is usually referred to as a Mendelian locus. Despite the fact that we use the term "correlation" in the preceding sentences, the genotype is actually the cause of the phenotype. More precisely, variation in the genotypes of individuals in the sample population cause the variation in the phenotype. The statistical confidence of this assertion of causality is often estimated using LOD and LRS scores and permutation methods. If the LOD score is above 10, then we can be extremely confident that we have located a genetic cause of variation in the phenotype. While the location is defined usually with a precision ranging from 10 million to 100 thousand basepairs (the locus), the individual sequence variant that is responsible may be quite difficult to extract. Think of this in terms of police work: we may know the neighborhood where the suspect lives, we may have clues as to identity and habits, but we still may have a large list of suspects.
-
-Text here [Williams RW, July 15, 2010]
-    
-[Go back to index](#index)
-
-
    
-
-## H
-
-#### Heritability, h2:
-
-Heritability is a rough measure of the ability to use genetic information to predict the level of variation in phenotypes among progeny. Values range from 0 to 1 (or 0 to 100%). A value of 1 or 100% means that a trait is entirely predictable based on paternal/materinal and genetic data (in other words, a Mendelian trait), whereas a value of 0 means that a trait is not at all predictable from information on gene variants. Estimates of heritability are highly dependent on the environment, stage, and age.
-
-Important traits that affect fitness often have low heritabilities because stabilizing selection reduces the frequency of DNA variants that produce suboptimal phenotypes. Conversely, less critical traits for which substantial phenotypic variation is well tolerated, may have high heritability. The environment of laboratory rodents is unnatural, and this allows the accumulation of somewhat deleterious mutations (for example, mutations that lead to albinism). This leads to an upward trend in heritability of unselected traits in laboratory populations--a desirable feature from the point of view of the biomedical analysis of the genetic basis of trait variance. Heritability is a useful parameter to measure at an early stage of a genetic analysis, because it provides a rough gauge of the likelihood of successfully understanding the allelic sources of variation. Highly heritable traits are more amenable to mapping studies. There are numerous ways to estimate heritability, a few of which are described below. [Williams RW, Dec 23, 2004] 
-
-#### h2 Estimated by Intraclass Correlation:
-
-Heritability can be estimated using the intraclass correlation coefficient. This is essentially a one-way repeated measures analysis of variance (ANOVA) of the reliability of trait data. Difference among strains are considered due to a random effect, whereas variation among samples within a single strain are considered due to measurement error. One can use the method implemented by SAS (PROC VARCOMP) that exploits a restricted maximum likelihood (REML) approach to estimate the intraclass correlation coefficient instead of an ordinary least squares method. The general equation for the intraclass correlation is:
-
-r = (Between-strain MS - Within-strain MS)/(Between-strain MS + (n-1)x Within-strain MS)
-
-where n is the average number of cases per strain. The intraclass correlation approaches 1 when there is minimal variation within strains, and strain means differ greatly. In contrast, if difference between strains are less than what would be predicted from the differences within strain, then the intraclass correlation will produce negative estimates of heritability. Negative heritability is usually a clue that the design of the experiment has injected excessive within-strain variance. It is easy for this to happen inadvertently by failing to correct for a batch effect. For example, if one collects the first batch of data for strains 1 through 20 during a full moon, and a second batch of data for these same strains during a rare blue moon, then the apparent variation within strain may greatly exceed the among strain variance. A technical batch effect has been confounded with the within-strain variation and has swamped any among-strain variance. What to do? Fix the batch effect, sex effect, age effect, etc., first! [Williams RW, Chesler EJ, Dec 23, 2004] 
-
-#### h2 Estimated using Hegmann and Possidente's Method (Adjusted Heritability in the Basic Statisics):
-
-A simple estimate of heritability for inbred lines involves comparing the variance between strain means (Va) to the total variance (Vt) of the phenotype, where Va is the a rough estimate of the additive genetic variance and Vt is the equal to Va and the average environmental variance, Ve. For example, if we study 10 cases of each of 20 strains, we have a total variance of the phenotype across 200 samples, and a strain mean variance across 20 strain averages. We can use this simple equation to estimate the heritability:
-
-h2 = Va / Vt
-
-This estimate of heritability will be an **overestimate**, and the severity of this bias will be a function of the within-strain standard error of the mean. Even a random data set of 10 each of 20 strains that should have an h2 of 0, will often give h2 values of 0.10 to 0.20. (Try this in a spreadsheet program using random numbers.)
-
-However, this estimate of h2 cannot be compared directly to those calculated using standard intercrosses and backcrosses. The reason is that all cases above are fully inbred and no genotypes are heterozygous. As a result the estimate of Va will be inflated two-fold. Hegmann and Possidente (1981 suggested a simple solution; adjust the equation as follows:
-
-h2 = 0.5Va / (0.5Va+Ve)
-
-The factor 0.5 is applied to Va to adjust for the overestimation of additive genetic variance among inbred strains. This estimate of heritability also does not make allowances for the within-strain error term. The 0.5 adjustment factor is not recommended any more because h2 is severely **underestimated**. This adjustment is really only needed if the goal is to compare h2 between intercrosses and those generated using panels of inbred strains. 
-
-#### h2RIx̅
-
-Finally, heritability calculations using strain means, such as those listed above, do not provide estimates of the effective heritability achieved by resampling a given line, strain, or genometype many times. Belknap ([1998](http://gn1.genenetwork.org/images/upload/Belknap_Heritability_1998.pdf)) provides corrected estimates of the effective heritability. Figure 1 from his paper (reproduced below) illustrates how resampling helps a great deal. Simply resampling each strain 8 times can boost the effective heritability from 0.2 to 0.8. The graph also illustrates why it often does not make sense to resample much beyond 4 to 8, depending on heritability. Belknap used the term h2RIx̅ in this figure and paper, since he was focused on data generated using recombinant inbred (RI) strains, but the logic applies equally well to any panel of genomes for which replication of individual genometypes is practical. This h2RIx̅ can be calculated simply by:
-h2RIx̅ = Va / (Va+(Ve/n)) where Va is the genetic variability (variability between strains), Ve is the environmental variability (variability within strains), and n is the number of within strain replicates. Of course, with many studies the number of within strain replicates will vary between strains, and this needs to be dealt with. A reasonable approach is to use the harmonic mean of n across all strains. 
-
-Homozygosity
-
-An analysis of statistical power is useful to estimate numbers of replicates and strains needed to detect and resolve major sources of trait variance and covariance. A versatile method has been developed by Sen and colleagues (Sen et al., 2007) and implemented in the R program. qtlDesign. David Ashbrook implemented a version of this within Shiny that can help you estimate power for different heritability values QTL effect sizes, cohort sizes, and replication rates:
-
-**[Power Calculator (D. Ashbrook)](https://dashbrook1.shinyapps.io/bxd_power_calculator_app/)**
-
-We can see that in all situations power is increased more by increasing the number of lines than by increasing the number of biological replicates. Dependent upon the heritability of the trait, there is little gain in power when going above 4-6 biological replicates. [DGA, Feb 1, 2019] [Chesler EJ, Dec 20, 2004; RWW updated March 7, 2018; Ashbrook DG, updated Feb 1, 2019] 
-
-#### Hitchhiking Effect:
-
-Conventional knockout lines (KOs) of mice are often mixtures of the genomes of two strains of mice. One important consequence of this fact is that a conventional comparison of wildtype and KO litter mates does not only test of the effects of the KO gene itself but also tests the effects of thousands of "hitchhiking" sequence polymorphisms in genes that flank the KO gene. This experimental confound can be difficult to resolve (but see below). This problem was first highlighted by Robert Gerlai ([1996](http://gn1.genenetwork.org/images/upload/Gerlai_TINS_1996.pdf)).
-
-**Genetics of KO Lines**. The embryonic stem cells used to make KOs are usually derived from a 129 strain of mouse (e.g., 129/OlaHsd). Mutated stem cells are then added to a C57BL/6J blastocyst to generate B6x129 chimeric mice. Germline transmission of the KO allele is tested and carriers are then used to establish heterozygous +/- B6.129 KO stock. This stock is often crossed back to wildtype C57BL/6J strains for several generations. At each generation the transmission of the KO is checked by genotyping the gene or closely flanking markers in each litter of mice. Carriers are again selected for breeding. The end result of this process is a KO congenic line in which the genetic background is primarily C57BL/6J except for the region around the KO gene.
-
-It is often thought that 10 generations of backcrossing will result in a pure genetic background (99.8% C57BL/6J). Unfortunately, this is not true for the region around the KO, and even after many generations of backcrossing of KO stock to C57BL/6J, a large region around the KO is still derived from the 129 substrain (see the residual white "line" at N10 in the figure below. 
-
-Congenic
-
-After 20 generations of backcrossing nearly +/-5 cM on either side of the KO will still usually be derived from 129 (see [Figure 3.6](http://www.informatics.jax.org/silverbook/frames/frame3-3.shtml)) This amounts to an average of +/- 10 megabases of DNA around the KO. The wildtype littermates do NOT have this flanking DNA from 129 and they will be like a true C57BL/6J. The +/- 10 megabases to either side of the KO is known as the "hitchhiking" chromosomal interval. Any polymorphism between 129 and B6 in this interval has the potential to have significant downstream effects on gene expression, protein expression, and higher order traits such as anxiety, activity, and maternal behavior. Much of the conventional KO literature is highly suspect due to this hitchhiker effect (see Gerlai R, [Trends in Neurosci 1996 19:177](http://gn1.genenetwork.org/images/upload/Gerlai_TINS_1996.pdf)).
-
-As one example, consider the thyroid alpha receptor hormone gene Thra and its KO. Thra maps to Chr 11 at about 99 Mb. A conventional KO made as described above will have a hitchhiking 129 chromosomal interval extending from about 89 Mb to 109 Mb even after 20 generations of backcrossing to B6. Since the mouse genome is about 2.6 billion base pairs and contains about 26,000 genes, this 20 Mb region will typically contain about 200 genes. The particular region of Chr 11 around Thra has an unusually high density of genes (2-3X) and includes many highly expressed and polymorphic genes, including *Nog*, *Car10*, *Cdc34*, *Col1a1*, *Dlx4*, *Myst2*, *Ngfr*, *Igf2bp1*, *Gip*, the entire *Hoxb* complex, *Sp6*, *Socs7*, *Lasp1*, *Cacnb1*, *Pparbp*, *Pnmt*, *Erbb2*, *Grb7*, *Nr1d1*, *Casc3*, *Igfbp4*, and the entire *Krt1* complex. Of these gene roughly half will be polymorphic between B6 and 129. It is like having a busload of noisy and possibly dangerous hitchhikers. Putative KO effects may be generated by a complex subset of these 100 polymorphic genes.
-
-What is the solution?
-
-1. Do not use litter mates as controls without great care. They are not really the correct genetic control. The correct genetic control is a congenic strain of the same general type without the KO or with a different KO in a nearby gene. These are often available as KOs in neighboring genes that are not of interest. For example, the gene *Casc3* is located next to Thra. If a KO in Casc3 is available, then compare the two KOs and see if phenotypes of the two KOs differ ways predicted given the known molecular functions of the gene.
-
-2. Use a KO in which the KO has been backcrossed to a 129 strain--ideally the same strain from which ES cells were obtained. This eliminates the hitchhiker effect entirely and the KO, HET, and WT littermates really can be compared.
-
-3. Use a conditional KO.
-   
-4. Compare the phenotype of the two parental strains--129 and C57BL/6J and see if they differ in ways that might be confounded with the effects of the KO. 
-
-Homozygosity
-
-Legend:from [Silver, L. (1995) Oxford University Press](http://www.informatics.jax.org/silver/index.shtml)
-
-[Go back to index](#index)
-
-
    
-
-## I
-
-#### Interquartile Range:
-
-The interquartile range is the difference between the 75% and 25% percentiles of the distribution. We divide the sample into a high and low half and then compute the median for each of these halves. In other words we effectively split our sample into four ordered sets of values known as quartiles. The absolute value of the difference between the median of the lower half and the median of the upper half is also called the interquartile range. This estimate of range is insenstive to outliers. If you are curious you might double the IQR to get an interquartile-range-based estimate of the full range. Of course, keep in mind that range is dependent on the sample size. For theis reason the coeffficient of variation (the standard deviation divided by the mean) is a better overall indicator of dispersion of values around the mean that is less sensitive to sample size. [Williams RW, Oct 20, 2004; Jan 23, 2005]
-
-#### Interval Mapping:
-
-Interval mapping is a process in which the statistical significance of a hypothetical QTL is evaluated at regular points across a chromosome, even in the absence of explicit genotype data at those points. In the case of WebQTL, significance is calculated using an efficient and very rapid regression method, the Haley-Knott regression equations ([Haley CS, Knott SA. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers; Heredity 69:315–324](http://www.ncbi.nlm.nih.gov/pubmed/16718932)), in which trait values are compared to the known genotype at a marker or to the probability of a specific genotype at a test location between two flanking markers. (The three genotypes are coded as -1, 0, and +1 at known markers, but often have fractional values in the intervals between markers.) The inferred probability of the genotypes in regions that have not been genotyped can be estimated from genotypes of the closest flanking markers. GeneNetwork/WebQTL compute linkage at intervals of 1 cM or less. As a consequence of this approach to computing linkage statistics, interval maps often have a characteristic shape in which the markers appear as sharply defined inflection points, and the intervals between nodes are smooth curves. [Chesler EJ, Dec 20, 2004; RWW April 2005; RWW Man 2014] 
-
-#### Interval Mapping Options:
-
-- _Permutation Test_: Select this option to determine the approximate LRS value that matches a genome-wide p-value of .05.
-
-- _Bootstrap Test_: Select this option to evaluate the consistency with which peak LRS scores cluster around a putative QTL. Deselect this option if it obscures the SNP track or the additive effect track.
-
-- _Additive Effect_: The additive effect (shown by the red lines in these plots) provide an estimate of the change in the average phenotype that is brought about by substituting a single allele of one type with that of another type.
-
-- _SNP Track_: The SNP Seismograph Track provides information on the regional density of segregating variants in the cross that may generate trait variants. It is plotted along the X axis. If a locus spans a region with both high and low SNP density, then the causal variant has a higher prior probability to be located in the region with high density than in the region with low density.
-
-- _Gene Track_: This track overlays the positions of known genes on the physical Interval Map Viewer. If you hover the cursor over genes on this track, minimal information (symbol, position, and exon number) will appear.
-
-- _Display from X Mb to Y Mb_: Enter values in megabases to regenerate a smaller or large map view.
-
-- _Graph width (in pixels)_: Adjust this value to obtain larger or smaller map views (x axis only). 
-    
-[Go back to index](#index)
-
-
    
-
-## J
-
-[Go back to index](#index)
-
-
    
-
-## K
-
-[Go back to index](#index)
-
-
    
-
-## L
-
-
    
-
-#### Literature Correlation:
-
-The literature correlation is a unique feature in GeneNetwork that quantifies the similarity of words used to describe genes and their functions. Sets of words associated with genes were extracted from MEDLINE/PubMed abstracts (Jan 2017 by Ramin Homayouni, Diem-Trang Pham, and Sujoy Roy). For example, about 2500 PubMed abstracts contain reference to the gene "Sonic hedgehog" (Shh) in mouse, human, or rat. The words in all of these abstracts were extracted and categorize by their information content. A word such as "the" is not interesting, but words such as "dopamine" or "development" are useful in quantifying similarity. Sets of informative words are then compared—one gene's word set is compared the word set for all other genes. Similarity values are computed for a matrix of about 20,000 genes using latent semantic indexing [(see Xu et al., 2011)](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0018851). Similarity values are also known as literature correlations. These values are always positive and range from 0 to 1. Values between 0.5 and 1.0 indicate moderate-to-high levels of overlap of vocabularies.
-
-The literature correlation can be used to compare the "semantic" signal-to-noise of different measurements of gene, mRNA, and protein expression. Consider this common situation:There are three probe sets that measure Kit gene expression (1459588\_at, 1415900\_a\_at, and 1452514\_a\_at) in the Mouse BXD Lung mRNA data set (HZI Lung M430v2 (Apr08) RMA). Which one of these three gives the best measurement of Kit expression? It is impractical to perform quantitative rtPCR studies to answer this question, but there is a solid statistical answer that relies on **Literature Correlation**. Do the following: For each of the three probe sets, generate the top 1000 literature correlates. This will generate three apparently identical lists of genes that are known from the PubMed literature to be associated with the Kit oncogene. But the three lists are NOT actually identical when we look at the **Sample Correlation** column. To answer the question "which of the three probe sets is best", review the actual performance of the probe sets against this set of 1000 "friends of Kit". Do this by sorting all three lists by their Sample Correlation column (high to low). The clear winner is probe set 1415900_a_at. The 100th row in this probe set's list has a Sample Correlation of 0.620 (absolute value). In comparison, the 100th row for probe set 1452514_a_at has a Sample Correlation of 0.289. The probe set that targets the intron comes in last at 0.275. In conclusion, the probe set that targets the proximal half of the 3' UTR (1415900_a_at) has the highest "agreement" between Literature Correlation and Sample Correlation, and is our preferred measurement of Kit expression in the lung in this data set. (Updated by RWW and Ramin Homayouni, April 2017.) 
-
-
    
-
-#### LOD:
-
-The logarithm of the odds (LOD) provides a measure of the association between variation in a phenotype and genetic differences (alleles) at a particular chromosomal locus (see Nyholt [2000](http://www.sciencedirect.com/science/article/pii/S0002929707626391) for a lovely review of LOD scores).
-
-A LOD score is defined as the logarithm of the ratio of two likelihoods: (1) in the numerator the likelihood for the alternative hypothesis, namely that there is linkage at the chromosomal marker, and (2) the likelihood of the null hypothesis that there is no linkage. Likelihoods are probabilities, but they are not Pr(hypothesis | data) but rather Pr(data | two alternative hypotheses). That's why they are called likelihoods rather than probabilities. (The "|" symbol above translates to "given the"). Since LOD and LRS scores are associated with two particular hypotheses or models, they are also associated with the degrees of freedom of those two alternative models. When the model only has one degree of freedom this conversion between LOD to p value will work:
-
    -    lodToPval <-
-    function(x)
-    {
-    pchisq(x*(2*log(10)),df=1,lower.tail=FALSE)/2
-    }
-    # (from https://www.biostars.org/p/88495/ )    
-
    
-
-In the two likelihoods, one has maximized over the various nuisance parameters (the mean phenotypes for each genotype group, or overall for the null hypothesis, and the residual variance). Or one can say, one has plugged in the maximum likelihood estimates for these nuisance parameters.
-
-With complete data at a marker, the log likelihood for the normal model reduces to the (-n/2) times the log of the residual sum of squares.
-
-LOD values can be converted to LRS scores (likelihood ratio statistics) by multiplying by 4.61. The LOD is also roughly equivalent to the -log(P), where P is the probability of linkage (P = 0.001 => 3). The LOD itself is not a precise measurement of the probability of linkage, but in general for F2 crosses and RI strains, values above 3.3 will usually be worth attention for simple interval maps. [Williams RW, June 15, 2005, updated with text from Karl Broman, Oct 28, 2010, updated Apr 21, 2020 with Nyholt reference].
-
-
    
-
-#### LRS:
-
-In the setting of mapping traits, the likelihood ratio statistic is used as a measurement of the association or linkage between differences in traits and differences in particular genotype markers. LRS or LOD values are usually plotted on the y-axis, whereas chromosomal location of the marker are usually plotted on the x-axis. In the case of a whole genome scan--a sequential analysis of many markers and locations across the entire genome--LRS values above 10 to 15 will usually be worth attention for when mapping with standard experimental crosses (e.g., F2 intercrosses or recombinant inbred strains). The term "likelihood ratio" is used to describe the relative probability (likelihood) of two different explanations of the variation in a trait. The first explanation (or model or hypothesis H1) is that the differences in the trait ARE associated with that particular DNA sequence difference or marker. Very small probability values indicate that H1 is probably true. The second "null" hypothesis (Hnull or H0) is that differences in the trait are NOT associated with that particular DNA sequence. We can use the ratio of these two probabilities and models (H1 divided by H0) as our score. The math is a little bit more complicated and the LRS score is actually equal to -2 times the ratio of the natural logarithms of the two probabilities. For example, if the probability of H0 is 0.05 (only a one-in-twenty probability that the marker is associated with the trait by chance), whereas and the probability of H1 is 1 (the marker is certainly not linked to the trait), then the LRS value is 5.991. In Excel the equation giving the LRS result of 5.991 would look like this "=-2*(LN(0.05)-LN(1)). [Williams RW, Dec 13, 2004, updated Nov 18, 2009, updated Dec 19, 2012]
-
-[Go back to index](#index)
-
-
    
-
-## M
-
-Marker Regression:
-
-The relationship between differences in a trait and differences in alleles at a marker (or gene variants) can be computed using a regression analysis (genotype vs phenotype) or as a simple Pearson product moment correlation. Here is a simple example that you can try in Excel to understand marker-phenotype regression or marker-phenotype correlation: enter a row of phenotype and genotype data for 20 strains in an Excel spreadsheet labeled "Brain weight." The strains are C57BL/6J, DBA/2J, and 20 BXD strains of mice (1, 2, 5, 6, 8, 9, 12, 13, 14, 15, 16, 18, 21, 22, 23, 24, 25, 27, 28, and 29. The brains of these strains weigh an average (in milligrams) of 465, 339, 450, 390, 477, 361, 421, 419, 412, 403, 429, 429, 436, 427, 409, 431, 432, 380, 394, 381, 389, and 375. (These values are taken from BXD Trait 10032; data by John Belknap and colleagues, 1992. Notice that data are missing for several strains including the extinct lines BXD3, 4, and 7. Data for BXD11 and BXD19 (not extinct) are also missing. In the second row enter the genotypes at a single SNP marker on Chr 4 called "rs13478021" for the subset of strains for which we have phenotype data. The genotypes at rs1347801 are as follows for 20 BXDs listed above: D B D B D B D D D D D B D B D B D B D B. This string of alleles in the parents and 20 BXDs is called a strains distribution pattern (SDP). Let's convert these SDP letters into more useful numbers, so that we can "compute" with genotypes. Each B allele gets converted into a -1 and each D allele gets converted into a +1. In the spreadsheet, the data set of phenotypes and genotypes should look like this.
-
-
    -    Strain BXD1 BXD2 BXD5 6 8 9 12 13 14 15 16 18 21 22 23 24 25 27 28 29
-    Brain_weight 450 390 477 361 421 419 412 403 429 429 436 427 409 431 432 380 394 381 389 375
-    Marker_rs1347801 D B D B D B D D D D D B D B D B D B D B
-    Marker_code 1 -1 1 -1 1 -1 1 1 1 1 1 -1 1 -1 1 -1 1 -1 1 -1
-
    
-
-To compute the marker regression (or correlation) we just compare values in Rows 2 and 4. A Pearson product moment correlation gives a value of r = 0.494. A regression analysis indicates that on average those strains with a D allele have a heavier brain with roughly a 14 mg increase for each 1 unit change in genotype; that is a total of about 28 mg if all B-type strains are compared to all D-type strains at this particular marker. This difference is associated with a p value of 0.0268 (two-tailed test) and an LRS of about 9.8 (LOD = 9.8/4.6 or about 2.1). Note that the number of strains is modest and the results are therefore not robust. If you were to add the two parent strains (C57BL/6J and DBA/2J) back into this analysis, which is perfectly fair, then the significance of this maker is lost (r = 0.206 and p = 0.3569). Bootstrap and permutation analyses can help you decide whether results are robust or not and whether a nominally significant p value for a single marker is actually significant when you test many hundreds of markers across the whole genome (a so-called genome-wide test with a genome-wide p value that is estimated by permutation testing). [RWW, Feb 20, 2007, Dec 14, 2012]
-    
-[Go back to index](#index)
-
-
    
-
-## N
-
-#### Normal Probability Plot:
-
-A [normal probability plot](http://en.wikipedia.org/wiki/Normal_probability_plot) is a powerful tool to evaluate the extent to which a distribution of values conforms to (or deviates from) a normal Gaussian distribution. The Basic Statistics tools in GeneNetwork provides these plots for any trait. If a distribution of numbers is normal then the actual values and the predicted values based on a z score (units of deviation from the mean measured in standard deviation units) will form a nearly straight line. These plots can also be used to efficiently flag outlier samples in either tail of the distribution.
-
-In genetic studies, the probability plot can be used to detect the effects of major effect loci. A classical Mendelian locus will typically be associated with either a bimodal or trimodal distribution. In the plot below based on 99 samples, the points definitely do not fall on a single line. Three samples (green squares) have unusually high values; the majority of samples fall on a straight line between z = -0.8 to z = 2; and 16 values have much lower trait values than would be predicted based on a single normal distribution (a low mode group). The abrupt discontinuity in the distribution at -0.8 z is due to the effect of a single major Mendelian effect.
-
-Deviations from normality of the sort in the figure below should be considered good news from the point of view of likely success of tracking down the locations of QTLs. However, small numbers of outliers may require special statistical handling, such as their exclusion or [winsorising](http://en.wikipedia.org/wiki/Winsorising) (see more below on "Winsorizing"). [RWW June 2011] 
-
-Homozygosity
-
-[Go back to index](#index)
-
-
    
-
-## O
-
-#### Outliers: (also see [Wikipedia](http://en.wikipedia.org/wiki/Outlier))
-
-Statistical methods often assume that the distribution of trait values is close to a Gaussian normal bell-shaped curve and that there are no outlier values that are extremely high or low compared to the average. Some traits can be clearly split into two or more groups (affected cases and unaffected cases) and this is not a problem as long as the number of cases in each group is close to the number that you expected by chance and that your sample size is reasonable high (40 or more for recombinant inbred strains). Mapping functions and most statistical procedure in GeneNetwork should work reasonable well (the pair scan function for epistatic interactions is one possible exception).
-
-However, correlations and QTL mapping methods can be highly sensitive to outlier values. Make sure you review your data for outliers before mapping. GeneNetwork flags all outliers for you in the Trait Data and Analysis window and gives you the option of zapping these extreme values. Options include (1) do nothing, (2) delete the outliers and see what happens to your maps, (3) [Winsorize](http://en.wikipedia.org/wiki/Winsorising) the values of the outliers. You might try all three options and determine if your main results are stable or not. With small samples or extreme outliers, you may find the correlation and mapping results to be volatile. In general, if results (correlations, QTL positions or QTL LRS score) depend highly on one or two outliers (5-10% of the samples) then you should probably delete or winsorize the outliers.
-
-In order to calculate outliers, we first determine the Q1(25%) and Q3(75%) values and then multiply by a constant (in our case 1.5; a higher constant is less sensitive to outliers). This value is then subtracted from the Q1 value and added to the Q3 value in order to determine the lower and upper bounds. Values that fall above the upper bound or below the lower bound are considered outliers.
-
-The method is summarized [here](http://www.wikihow.com/Calculate-Outliers). [Sloan ZA, Oct 2013] 
-
-[Go back to index](#index)
-
-
    
-
-## P
-
-#### Pair-Scan, 2D Genome Scan, or Two-QTL Model:
-
-The pair scan function evaluates pairs of intervals (loci) across the genome to determine how much of the variability in the trait can be explained jointly by two putative QTLs. The pair scan function in GeneNetwork is used to detect effects of pairs of QTLs that have epistatic interactions, although this function also evaluates summed additive effects of two loci. Trait variance is evaluated using a general linear model that has this structure (called a "model"):
-
-Variance V(trait) = QTL1 + QTL2 + QTL1xQTL2 + error (where the = sign should be read "a function of"
-
-This model is also known as the Full Model (LRS Full in the output table), where QTL1 and QTL2 are the independent additive effects associated with two unlinked loci (the so-called main effects) and QTL1xQTL2 is the interaction term (LRS Interact in the output table). An LRS score is computed for this full model. This is computation identical to computing an ANOVA that allows for an interaction term between two predictors. The additive model that neglects the QTL1XQTL2 term is also computed.
-
-The output table in GeneNetwork list the the two intervals at the top of the table (Interval 1 to the left and Interval 2 to the far right). The LRS values for different components of the model are shown in the middle of the table (LRS Full, LRS Additive, LRS Interact, LRS 1, and LRS 2). Note that LRS 1 and LRS 2 will usually NOT sum to LRS Additive.
-
-CAUTIONS and LIMITATIONS: Pair-scan is only implemented for recombinant inbred strains. We do not recommend the use of this function with sample sizes of less than 60 recombinant inbred strains. Pair-scan procedures need careful diagnostics and an be very sensitive to outliers and to the balance among the four possible two-locus genotype classes among a set of RI strains. Pair-scan is not yet implemented for F2 progeny.
-
-GeneNetwork implements a rapid but non-exhaustive DIRECT algorithm (Lundberg et al., [2004](http://bioinformatics.oxfordjournals.org/content/20/12/1887.full.pdf)) that efficiently searches for epistatic interactions. This method is so fast that it is possible to compute 500 permutations to evaluate non-parametric significance of the joint LRS value within a minute. This makes DIRECT ideal for an interactive web service. Karl Broman's [R/qtl](http://www.rqtl.org/tutorials/rqtltour.pdf) implements an exhaustive search using the "scantwo" function. [RWW, May 2011]
-
-#### Partial Correlation:
-
-Partial correlation is the correlation between two variables that remains after controlling for one or more other variables. Idea and techniques used to compute partial correlations are important in testing causal models ([Cause and Correlation in Biology](http://www.amazon.com/Cause-Correlation-Biology-Structural-Equations/dp/0521529212), Bill Shipley, 2000). For instance, r1,2||3,4 is the partial correlation between variables 1 and 2, while controlling for variables 3 and 4 (the || symbol is equivalent to "while controlling for"). We can compare partial correlations (e.g., r1,2||3,4) with original correlations (e.g., r1,2). If there is an insignificant difference, we infer that the controlled variables have minimal effect and may not influence the variables or even be part of the model. In contrast, if the partial correlations change significantly, the inference is that the causal link between the two variables is dependent to some degree on the controlled variables. These control variables are either anteceding causes or intervening variables. (text adapted from D Garson's original by RWW).
-
-For more on [partial correlation](http://faculty.chass.ncsu.edu/garson/PA765/partialr.htm) please link to this great site by David Garson at NC State.
-
-For more on dependence separation ([d-separation](http://www.andrew.cmu.edu/user/scheines/tutor/d-sep.html)) and constructing causal models see Richard Scheines' site.
-
-Why would you use of need partial correlations in GeneNetwork? It is often useful to compute correlations among traits while controlling for additional variables. Partial correlations may reveal more about the causality of relations. In a genetic context, partial correlations can be used to remove much of the variance associated with linkage and linkage disequilibrium. You can also control for age, age, and other common cofactors.
-
-Please see the related Glossary terms "Tissue Correlation". [RWW, Aug 21, 2009; Jan 30, 2010]
-
-#### PCA Trait or Eigentrait:
-
-If you place a number of traits in a Trait Collection you can carry out some of the key steps of a principal component analysis, including defining the variance directed along specific principal component eigenvectors. You can also plot the positions of cases against the first two eigenvectors; in essence a type of scatterplot. Finally, GeneNetwork allows you to exploit PCA methods to make new "synthetic" eigentraits from collections of correlated traits. These synthetic traits are the values of cases along specific eigenvectors and they may be less noisy than single traits. If this seems puzzling, then have a look at these useful PCA explanation by [G. Dallas](http://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/) and by [Powell and Lehe](http://setosa.io/ev/principal-component-analysis/). **How to do it:** You can select and assemble many different traits into a single **Trait Collection** window using the check boxes and **Add To Collection** buttons. One of the most important function buttons in the **Collection** window is labeled **Correlation Matrix**. This function computes Pearson product moment correlations and Spearman rank order correlations for all possible pairs of traits in the Collection window. It also perfoms a principal component or factor analysis. For example, if you have 20 traits in the Collection window, the correlation matrix will consist of 20*19 or 190 correlations and the identity diagonal. Principal components analysis is a linear algebraic procedure that finds a small number of independent factors or principal components that efficiently explain variation in the original 20 traits. It is a effective method to reduce the dimensionality of a group of traits. If the 20 traits share a great deal of variation, then only two or three factors may explain variation among the traits. Instead of analyzing 20 traits as if they were independent, we can now analyze the main principal components labeled PC01, PC02, etc. PC01 and PC02 can be treated as new synthetic traits that represent the main sources of variation among original traits. You can treat a PC trait like any other trait except that it is not stored permanently in a database table. You can put a PC trait in your Collection window and see how well correlated each of the 20 original traits is with this new synthetic trait. You can also map a PC trait. [RWW, Aug 23, 2005]
-
-
    
-
-#### Permutation Test:
-
-A permutation test is a computationally intensive but conceptually simple method used to evaluate the statisical significance of findings. Permutation tests are often used to evaluate QTL significance. _Some background_: In order to detect parts of chromosomes that apparently harbor genes that contribute to differences in a trait's value, it is common to search for associations (linkage) across the entire genome. This is referred to as a "whole genome" scan, and it usually involves testing hundreds of independently segregating regions of the genome using hundreds, or even thousands of genetic markers (SNPs and microsatellites). A parametric test such as a conventional t test of F test can be used to estimate the probability of the null hypothesis at any single location in the genome (the null hypothesis is that there is no QTL at this particular location). But a parametric test of this type makes assumptions about the distribution of the trait (its normality), and also does not provide a way to correct for the large number of independent tests that are performed while scanning the whole genome. We need protection against many false discoveries as well as some assurance that we are not neglecting truly interesting locations. A permutation test is an elegant solution to both problems. The procedure involves randomly reassigning (permuting) traits values and genotypes of all cases used in the analysis. The permuted data sets have the same set of phenotypes and genotypes (in other words, distributions are the same), but obviously the permutation procedure almost invariably obliterates genuine gene-to-phenotype relation in large data sets. We typically generate several thousand permutations of the data. Each of these is analyzed using precisely the same method that was used to analyze the correctly ordered data set. We then compare statistical results of the original data set with the collection of values generated by the many permuted data sets. The hope is that the correctly ordered data are associated with larger LRS and LOD values than more than 95% of the permuted data sets. This is how we define the p = .05 whole genome significance threshold for a QTL. Please see the related Glossary terms "Significant threshold" and "Suggestive threshold". [RWW, July 15, 2005]
-
-#### Power to detect QTLs:
-
-An analysis of statistical power is useful to estimate numbers of replicates and strains needed to detect and resolve major sources of trait variance and covariance. A versatile method has been developed by Sen and colleagues (Sen et al., 2007) and implemented in the R program. qtlDesign. David Ashbrook implemented a version of this within Shiny that can help you estimate power for different QTL effect sizes, cohort sizes, and replication rates:
-
-#### [Power Calculator (D. Ashbrook)](https://dashbrook1.shinyapps.io/bxd_power_calculator_app/)
-
-We can see that in all situations power is increased more by increasing the number of lines than by increasing the number of biological replicates. Dependent upon the heritability of the trait, there is little gain in power when going above 4-6 biological replicates. [DGA, Mar 3, 2018] 
-
-#### Probes and Probe Sets:
-
-In microarray experiments the probe is the immobilized sequence on the array that is complementary to the target message washed over the array surface. Affymetrix probes are 25-mer DNA sequences synthesized on a quartz substrate. There are a few million of these 25-mers in each 120-square micron cell of the array. The abundance of a single transcript is usualy estimated by as many as 16 perfect match probes and 16 mismatch probes. The collection of probes that targets a particular message is called a probe set. [RWW, Dec 21, 2004]
-    
-
-[Go back to index](#index)
-
-
    
-
-## Q
-
-#### QTL:
-
-A quantitative trait locus is a chromosome region that contains one or more sequence variants that modulates the distribution of a variable trait measured in a sample of genetically diverse individuals from an interbreeding population. Variation in a quantitative trait may be generated by a single QTL with the addition of some environmental noise. Variation may be oligogenic and be modulated by a few independently segregating QTLs. In many cases however, variation in a trait will be polygenic and influenced by large number of QTLs distributed on many chromosomes. Environment, technique, experimental design and a host of other factors also affect the apparent distribution of a trait. Most quantitative traits are therefore the product of complex interactions of genetic factors, developmental and epigenetics factors, environmental variables, and measurement error. [Williams RW, Dec 21, 2004]
-
-[Go back to index](#index)
-
-
    
-
-## R
-
-#### Recombinant Inbred Strain (RI or RIS) or Recombinant Inbred Line (RIL):
-
-An inbred strain whose chromosomes incorporate a fixed and permanent set of recombinations of chromosomes originally descended from two or more parental strains. Sets of RI strains (from 10 to 5000) are often used to map the chromosomal positions of polymorphic loci that control variance in phenotypes.
-
-For a terrific short summary of the uses of RI strains see [2007](http://www.informatics.jax.org/silverbook/chapters/9-2.shtml)).
-
-Chromosomes of RI strains typically consist of alternating haplotypes of highly variable length that are inherited intact from the parental strains. In the case of a typical rodent RI strain made by crossing maternal strain C with paternal strain B (called a CXB RI strain), a chromosome will typically incorporate 3 to 5 alternating haplotype blocks with a structure such as BBBBBCCCCBBBCCCCCCCC, where each letter represents a genotype, series of similar genotype represent haplotypes, and where a transition between haplotypes represents a recombination. Both pairs of each chromosome will have the same alternating pattern, and all markers will be homozygous. Each of the different chromosomes (Chr 1, Chr 2, etc.) will have a different pattern of haplotypes and recombinations. The only exception is that the Y chromosome and the mitochondial genome, both of which are inherited intact from the paternal and maternal strain, respectively. For an RI strain to be useful for mapping purposes, the approximate position of recombinations along each chromsome need to be well defined either in terms of centimorgan or DNA basepair position. The precision with which these recombinations are mapped is a function of the number and position of the genotypes used to type the chromosomes--20 in the example above. Because markers and genotypes are often space quite far apart, often more than 500 Kb, the actual data entered into GeneNetwork will have some ambiguity at each recombination locus. The haplotype block BBBBBCCCCBBBCCCCCCCC will be entered as BBBBB?CCCC?BBB?CCCCCCCC where the ? mark indicates incomplete information over some (we hope) short interval.
-
-RI strains are almost always studied in sets or panels. All else being equal, the larger the set of RI strains, the greater the power and precision with which phenotypes can be mapped to chromosomal locations. The first set of eight RIs, the CXB RIs, were generated by Donald Bailey (By) from an intercross between a female BALB/cBy mouse (abbreviated C) and a male C57BL/6By mouse in the 1960s. The small panel of 8 CXB strains was originally used to determine if the major histocompatibility (MHC) locus on proximal Chr 17 was a key factor accounting for different immune responses such as tissue rejection. The methods used to determine the locations of recombinations relied on visible markers (coat color phenotypes such as the C and B loci) and the electrophoretic mobility of proteins. Somewhat larger RI sets were generated by Benjamin Taylor to map Mendelian and other major effect loci. In the 1990s the utility of RI sets for mapping was significantly improved thanks to higher density genotypes made possible by the use of microsatellite markers. Between 2005 and 2017, virtually all extant mouse and rat RI strains were regenotyped at many thousands of SNP markers, providing highly accurate maps of recombinations.
-
-While the potential utility of RI strains in mapping complex polygenic traits was obvious from the outset, the small number of strains only made it feasible to map quantitative traits with large effects. The first large RI sets were generated by plant geneticists (Burr et al. [2000](http://demeter.bio.bnl.gov/RIchap_rev.pdf)) and this the plant genetics community holds a strong lead in the production of very large RI sets to study multigenic and polygenic traits and trait covariance and pleiotropy.
-
-By 2010 the number of mouse RI strains had increased to the point where defining causal gene and sequence variant was more practical. As of 2018 there are about 150 BXD strains (152 have been fully sequenced), ~100 Collaborative Cross strains (also all fully sequenced), and at least another 100 RI strains belonging to smaller sets that have been extremely well genotyped.
-
-**Making RI strains**: The usual procedure typically involves sib mating of the progeny of an F1 intercross for more than 20 generations. Even by the 5th filial (F) generation of successive matings, the RI lines are homozygous at 50% of loci and by F13, the value is above 90%. At F20 the lines are nearly fully inbred (~98%) and by convention are now referred to as inbred strains rather than inbred lines. 
-
-
-[Go back to index](#index)
-
-Legend:from [Silver, L. (1995) Oxford University Press](http://www.informatics.jax.org/silverbook/frames/frame3-3.shtml)
-
-[Williams RW, June 20, 2005; significant extension, Sept 21, 2007, added Crow ref, Oct 2009]
-
-
    
-
-## S
-
-#### Scree Plots:
-
-GeneNetwork will often automatically generate a [Scree Plot](http://www.improvedoutcomes.com/docs/WebSiteDocs/PCA/Creating_a_Scree_Plot.htm) and the associated principal components (PCs) when you compute a Correlation Matrix for a group of traits that you have placed in your Trait Collection (a set of phenotypes and/or expression data for a specific population). Here is a nice definition of what a Scree plot is trying to tell you adopted and adapted from IOS (www.improvedoutcomes.com).
-
-A Scree Plot is a simple line segment plot that shows the fraction of total variance in the data as explained or represented by each PC. The PCs are ordered, and by definition are therefore assigned a number label, by decreasing order of contribution to total variance. The PC with the largest fraction contribution is labeled PC01. Such a plot when read left-to-right across the abscissa can often show a clear separation in fraction of total variance where the 'most important' components cease and the 'least important' components begin. The point of separation is often called the 'elbow'. (In the PCA literature, the plot is called a 'Scree' Plot because it often looks like a 'scree' slope, where rocks have fallen down and accumulated on the side of a mountain.) [Williams RW, Dec 20, 2008]
-
-#### Significant threshold:
-
-The significant threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.05, or a 5% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This threshold is computed by evaluating the distribution of highest LRS scores generated by a set of 2000 random permutations of strain means. For example, a random permutation of the correctly ordered data may give a peak LRS score of 10 somewhere across the genome. The set of 1000 or more of these highest LRS scores is then compared to the actual LRS obtained for the correctly ordered (real) data at any location in the genome. If fewer than 50 (5%) of the 1000 permutations have peak LRS scores anywhere in the genome that exceed that obtained at a particular locus using the correctly ordered data, then one can usually claim that a QTL has been defined at a genome-wide p-value of .05. The threshold will vary slightly each time it is recomputed due to the random generation of the permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the **Analysis Tools** area of the **Trait Data and Editing Form**. WebQTL does make it possible to search through hundreds of traits for those that may have significant linkage somewhere in the genome. Keep in mind that this introduces a second tier of multiple testing problems for which the permutation test will not usually provide adequate protection. If you anticipate mapping many independent traits, then you will need to correct for the number of traits you have tested. [Williams RW, Nov 14, 2004]
-
-
    
-
-#### SNP Seismograph Track:
-
-SNP is an acronym for single nucleotide polymorphisms (SNPs). SNPs are simple one base pair variants that distinguish individuals and strains. The SNP Seismograph track is a unique feature of physical maps in the GeneNetwork. Each track is customized for a particular cross and shows only those SNPs that differ between the two parental strains. For example, on mouse BXD maps, only the SNPs that differ between C57BL/6J and DBA/2J will be displayed. Regions with high numbers of SNPs are characterised by wider excursions of the yellow traces that extends along the x axis. Since these regions have many SNPs they have a higher prior probability of containing functional sequence differences that might have downstream effects on phenotypes. Large genes with many SNPs close to the peak LRS and that also have a biological connection with the trait ypu are studying are high priority candidate genes.
-
-The SNP track in WebQTL exploits the complete Celera Discovery System SNP set but adds an additional 500,000 inferred SNPs in both BXD and AXB/BXA crosses. These SNPs were inferred based on common haplotype structure using an Monte Carlo Markov chain algorithm developed by Gary Churchill and Natalie Blades and implemented by Robert Crowell, and RWW in July 2004. Raw data used to generate the SNP seismograph track were generated by Alex Williams and Chris Vincent, July 2003. The BXD track exploits a database of 1.75 million B vs D SNPs, whereas the AXB/BXA track exploits a database of 1.80 million A vs B SNPs. The names, sequences, and precise locations of most of these SNPs are the property of Celera Discovery Systems, whom we thank for allowing us to provide this level of display in WebQTL.
-
-Approximately 2.8 million additional SNPs generated by Perlegen for the NIEHS have been added to the SNP track by Robert Crowell (July-Aug 2005). We have also added all Wellcome-CTC SNPs and all relevant mouse SNPs from dbSNP. [Williams RW, Dec 25, 2004; Sept 3, 2005] 
-
-#### Standard Error of the Mean (SE or SEM):
-
-In most GeneNetwork data sets, the SEM is computed as:
-Standard Deviation (SD) divided by the square root of n - 1
-where n is the number of independent biological samples used to estimate the population mean. What this means in practice is that when n = 2 (as in many microarray data sets), the SEM and the SD are identical. This method of computing the SEM is conservative, but corrects to some extent for well known bias of the SEM discussed by Gurland and Tripathi (1971, A simple approximation for unbiased estimation of the standard deviation. Amer Stat 25:30-32). [Williams RW, Dec 17, 2008]
-
-#### Strain Distribution Pattern:
-
-A marker such as a SNP or microsatellite is genotyped using DNA obtained from each member of the mapping population. In the case of a genetic reference population, such as the BXD strains or the BayXSha Arabadopsis lines, this results in a text string of genotypes (e.g., BDDDBDBBBBDDBDDDBBBB... for BXD1 through BXD100). Each marker is associated with its own particular text string of genotypes that is often called the **strain distribution pattern** of the marker. (A more appropriate term would be the **marker genotype string**.) This string is converted to a numerical version, a genotype vector: -1111-11-1-1-1-111-1111-1-1-1-1..., where D=1, B=-1, H=0. Mapping a trait boils down to performing correlations between each trait and all of the genotype vectors. The genotype vector with the highest correlation (absolute value) is a good candidate for a QTL. [Williams RW, June 18, 2005] 
-
-#### Suggestive Threshold:
-
-The suggestive threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.63, or a 63% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This is not a typographical error. The Suggestive LRS threshold is defined as that which yields, on average, one false positive per genome scan. That is, roughly one-third of scans at this threshold will yield no false positive, one-third will yield one false positive, and one-third will yield two or more false positives. This is a very permissive threshold, but it is useful because it calls attention to loci that may be worth follow-up. Regions of the genome in which the LRS exceeds the suggestive threshold are often worth tracking and screening. They are particularly useful in combined multicross metaanalysis of traits. If two crosses pick up the same suggestive locus, then that locus may be significant when the joint probability is computed. The suggestive threshold may vary slightly each time it is recomputed due to the random generation of permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the **Analysis Tools** area of the **Trait Data and Editing Form**. [Williams RW and Manly KF, Nov 15, 2004] 
-
-#### Systems Genetics:
-
-Systems genetics or "network genetics" is an emerging new branch of genetics that aims to understand complex causal networks of interactions at multiple levels of biological organization. To put this in a simple context: Mendelian genetics can be defined as the search for linkage between a single trait and a single gene variant (1 to 1); complex trait analysis can be defined as the search for linkage between a single trait and a set of gene variants (QTLs, QTGs, and QTNs) and environmental cofactors (1 to many); and systems genetics can be defined as the search for linkages among networks of traits and networks of gene and environmental variants (many to many).
-
-A hallmark of systems genetics is the simultaneous consideration of groups (systems) of phenotypes from the primary level of molecular and cellular interactions that ultimately modulate global phenotypes such as blood pressure, behavior, or disease resistance. Changes in environment are also often important determinants of multiscalar phenotypes; reversing the standard notion of causality as flowing inexorably upward from the genome. Scientists who use a systems genetics approach often have a broad interest in modules of linked phenotypes. Causality in these complex dynamic systems is often contingent on environmental or temporal context, and often will involve feedback modulation. A systems genetics approach can be unusually powerful, but does require the use of large numbers of observations (large sample size), and more advanced statistical and computational models.
-
-Systems genetics is not really a new field and traces back to [Sewall Wright's](http://www.amphilsoc.org/library/mole/w/wrights.htm) classical paper (Wright, 1921, "Correlation and Causation") that introduced path analysis to study systems of related phenotypes. Two factors have invigorated this field. The first factor is the advent of more sophisticated statistical methods including Structural [Equation Modeling](http://userwww.sfsu.edu/~efc/classes/biol710/path/SEMwebpage.htm) (SEM), [System Dynamics Modeling](http://www.public.asu.edu/~kirkwood/sysdyn/SDIntro/SDIntro.htm), and [Bayesian Network Modeling](http://bnj.sourceforge.net/) combined with powerful computer systems and efficient algorithms. The second factor is the relative ease with which it is now possible to acquire extensive and diverse phenotype data sets across genetic reference populations such as the BXD set of mice, the HXB set of rats, and the BayXSha lines of Arabidopsis (data are incorporated in the GeneNetwork). In the case of the BXD strains, a large research community has collectively generated hundreds of thousands of transcript phenotypes in different tissues and cells (level of expression), as well as hundreds of protein, cellular, pharmacological, and behavioral data types across a single genetic reference panel. Evaluating and modeling the associative and causal relations among these phenotypes is a major, and still relatively new area of research. Complex trait analysis and QTL mapping are both part of systems genetics in which causality is inferred using conventional genetic linkage (Li et al., [2005](http://hmg.oupjournals.org/cgi/content/abstract/ddi124v1)). One can often assert with confidence that a particular module of phenotypes (component of the variance and covariance) is modulated by sequence variants at a common locus. This provides a causal constraint that can be extremely helpful in more accurately modeling network architecture. Most models are currently static, but as the field matures, more sophisticated dynamic models will supplant steady-state models.
-
-The term "systems genetics" was coined by Grant Morahan, October 2004, during a visit to Memphis, as a more general and appropriate term to use instead of "genetical genomics." [Williams RW, April 11, 2005, revised Oct 22, 2005, April, 2008]
-
-[Go back to index](#index)
-
-
    
-
-## T
-
-
    
-
-#### Tissue Correlation:
-
-The tissue correlation is an estimate of the similarity of expression of two genes across different cells, tissues, or organs. In order to compute this type of correlation we first generate expression data for multiple different cell types, tissues, or whole organs from a single individual. There will be significant differences in gene expression across this sample and this variation can then be used to compute either Pearson product-moment correlations (r) or Spearman rank order correlations (rho) between any pair of genes, transcripts, or even exons. Since the samples are ideally all from one individual there should not be any genetic or environmental differences among samples. The difficulty in computing tissue correlations is that samples are not independent. For example, three samples of the small intestine (jejunum, ilieum, and duodenum) will have expression patterns that are quite similar to each other in comparison to three other samples, such as heart, brain, and bone. For this reason the nature of the sampling and how those samples are combined will greatly affect the correlation values. The tissue correlations in GeneNetwork were computed in a way that attempts to reduce the impact of this fact by combining closely related sample types. For example multiple data sets for different brain region were combined to generate a single average CNS tissue sample (generating a whole brain sample would have been an alternative method).
-
-However, there is really not optimal way to minimize the effects of this type of non-independence of samples. Some genes will have high expression in only a few tissues, for example the cholinergic receptor, nicotinic, alpha polypeptide 1 gene Chrna1 has high expression in muscle tissues (skeletal muscle = Mus, tongue = Ton, and esophagus = Eso) but lower expression in most other tissues. The very high correlation between Chrna1 and other genes with high expression only in muscle reflects their joint bimodality of expression. It does not mean that these genes or their proteins necessarily cooperate directly in molecular processes. [Williams RW, Dec 26, 2008] 
-
-
-
-#### Transcript Location:
-
-The small orange triangle on the x-axis indicates the approximate position of the gene that corresponds to the transcript. These values were taken from the latest assembly of genome of the particular species.
-
-#### Transform:
-
-Most of the data sets in the GeneNetwork are ultimately derived from high resolution images of the surfaces of microarrays. Estimates the gene expression therefore involves extensive low-level image analysis. These processesing steps attempt to compensate for low spatial frequency "background" variation in image intensity that cannot be related to the actual hybridization signal, for example, a gradation of intensity across the whole array surface due to illumination differences, uneven hybridization, optical performance, scanning characteristics, etc. High spatial frequeny artifacts are also removed if they are likely to be artifacts: dust, scrathes on the array surface, and other "sharp" blemishes. The raw image data (for example, the Affymetrix DAT file) also needs to be registered to a template that assigns pixel values to expected array spots (cells). This image registration is an important process that users can usually take for granted. The end result is the reliable assignment of a set of image intensity values (pixels) to each probe. Each cell value generated using the Affymetrix U74Av2 array is associated with approximately 36 pixel intensity values (a 6x6 set of pixels, usually an effective 11 or 12-bit range of intensity). Affymetrix uses a method that simply ranks the values of these pixels and picks as the "representative value" the pixel that is closest to a particular rank order value, for example, the 24th highest of 36 pixels. The range of variation in intensity values amoung these ranked pixels provides a way to estimate the error of the estimate. The Affymetrix CEL files therefore consist of XY coordinates, the consensus value, and an error term. [Williams RW, April 30, 2005] 
-
-#### Transgression:
-
-Most of us are familiar with the phrase "regression toward the mean." This refers to the tendency of progeny of a cross to have phenotype that are intermediate to those of the parents. Transgression refers to the converse: progeny that have more phenotypes that are higher and lower than those of either parent. Transgression is common, and provided that a trait is influenced by many independent sequence variants (a polygenic trait), transgression is the expectation. This is particularly true if the parents are different genetically, but by chance have similar phenotypes. Consider a trait that is controlled by six independent genes, A through F. The "0" allele at these size genes lowers body weight whereas the "1" allele increases body weight. If one parent has a 000111 6-locus genotype and the other parent has 111000 genotype, then they will have closely matched weight. But their progeny may inherit combinations as extreme as 000000 and 111111.
-
-Transgression means that you can rarely predict the distribution of phenotypes among a set of progeny unless you already have a significant amount of information about the genetic architecture of a trait (numbers of segregating variants that affect the trait, either interactions, and GXE effects). In practical terms this means that if the parents of a cross do NOT differ and you have good reasons to believe that the trait you are interested in is genetically complex, then you can be fairly confident that the progeny will display a much wider range of variation that the parents. [May 2011 by RWW].
-    
-[Go back to index](#index)
-
-
    
-
-## U
-
-[Go back to index](#index)
-
-
    
-
-## V
-
-[Go back to index](#index)
-
-
    
-
-## W
-
-#### Winsorize, Winsorise:
-
-QTL mapping results can be greatly affected by inclusion of outlier data. GeneNetwork will do its best to flag outliers for you in the **Trait Data and Analysis** pages (yellow highlighting). Before mapping, review the data, and if necessary, change values. Options for handling outliers include: (1) do nothing, (2) delete the outliers (trimming), (3) transform the data (e.g., logarithmic, arcsine, or logistic regression transforms), or (4) [winsorize](http://en.wikipedia.org/wiki/Winsorising) the distribution of values. Winsorizing is usually the easiest method to implement directly in GeneNetwork.
-
-**How to winsorize**: First review the distribution of values and define outliers. You should only do this one time, so think before you leap. Look at the **Probability Plot** of the trait by going to **Trait Data and Analysis** page and selecting **Basic Statistics**). For example, the figure below from GeneNetwork shows that at many as seven cases have relatively high values and as many as three have relatively low values (this trait is taken from Species = Mouse, Group = LXS, Type = Phenotype, Trait 10182). GeneNetwork code only declares the highest two values to be outliers, but you can use a more liberal definition and give all seven high values a haircut. It is advisable to winsorizes equal numbers of cases on each side of the distribution (high and low cases). In this case, the seven highest values were changed to match that of the 8th highest value (0.860). To retain the original rank order I added an incremental value of 0.01 to each (0.861, 0.862, etc). I did the same thing to the lowest seven values. Adding this increment is not necessary.
-
-The result in this case: a suggestive QTL on Chr 16 now reaches the significance threshold.
-
-The **danger of winsorizing** is doing it multiple times in different ways. You should transform or winsorize the data before mapping. And you should ideally only do any transformation/correction one time. If you fool around with different methods of transforming your data then you are asking for trouble by adding yet another level of multiple testing. If you feel compelled to experiment with different transforms, then you should/must report this in publications and explain why you did so. Demonstrating that mapping results are robust even using multiple transforms is one good excuse. [Williams RW, Jan 2, 2014]
-
-
-
-
-
-[Go back to index](#index)
-
-
    
-
-## X
-
-[Go back to index](#index)
-
-
    
-
-## Y
-[Go back to index](#index)
-
-
-
    
-
-## Z
-
-[Go back to index](#index)
-- 
cgit v1.2.3


From 51f05b5fa896a2ed636ecb08bf69d929896db517 Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Fri, 20 Nov 2020 18:47:21 +0300
Subject: Point url for fetching md content to gn-docs repo

* wqflask/wqflask/markdown_routes.py (render_markdown): Use gn-docs
URL to fetch markdown content.
---
 wqflask/wqflask/markdown_routes.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 33092947..48a1ea81 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -17,11 +17,9 @@ def render_markdown(file_name):
 look for it inside the file system
 
     """
-    markdown_url = (f"https://raw.githubusercontent.com"
-                    f"/genenetwork/genenetwork2/"
-                    f"wqflask/wqflask/static/"
-                    f"{file_name}")
-    md_content = requests.get(markdown_url)
+    github_url = ("https://raw.githubusercontent.com/"
+                  "genenetwork/gn-docs/master/")
+    md_content = requests.get(f"{github_url}{file_name}")
     if md_content.status_code == 200:
         return mistune.html(md_content.content.decode("utf-8"))
 
-- 
cgit v1.2.3


From 0a6a5d0ac847489bb5db158bc54e73aa682e18a0 Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Fri, 20 Nov 2020 18:51:46 +0300
Subject: Replace mistunes with markdown library

* wqflask/wqflask/markdown_routes.py (render_markdown): Use markdown
library instead of mistunes.
---
 wqflask/wqflask/markdown_routes.py | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 48a1ea81..7a9fac41 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -2,9 +2,8 @@
 
 Render pages from github, or if they are unavailable, look for it else where
 """
-import os
 import requests
-import mistune
+import markdown
 
 from flask import Blueprint
 from flask import render_template
@@ -21,16 +20,20 @@ look for it inside the file system
                   "genenetwork/gn-docs/master/")
     md_content = requests.get(f"{github_url}{file_name}")
     if md_content.status_code == 200:
-        return mistune.html(md_content.content.decode("utf-8"))
+        return markdown.Markdown().convert(md_content.content.decode("utf-8"))
 
-    with open(os.path.join(os.path.abspath(os.path.dirname(__file__)),
-                           f"static/markdown/{file_name}")) as md_file:
-        markdown = md_file.read()
-        return mistune.html(markdown)
+    # TODO: Add fallback on our git server by checking the mirror.
+
+    # Content not available
+    return (f"\nContent for {file_name} not available. "
+            "Please check "
+            "(here to see where content exists)"
+            "[https://github.com/genenetwork/gn-docs]. "
+            "Please reach out to the gn2 team to have a look at this")
 
 
 @glossary_blueprint.route('/')
 def glossary():
     return render_template(
         "glossary.html",
-        rendered_markdown=render_markdown("glossary.md")), 200
+        rendered_markdown=render_markdown("general/glossary/glossary.md")), 200
-- 
cgit v1.2.3


From e323c52889cfbab7f152e4969f6e2b528768587a Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Fri, 20 Nov 2020 20:00:29 +0300
Subject: Update tests to use new gn2-docs url

---
 wqflask/tests/unit/wqflask/test_markdown_routes.py | 24 ++++++++++------------
 1 file changed, 11 insertions(+), 13 deletions(-)

diff --git a/wqflask/tests/unit/wqflask/test_markdown_routes.py b/wqflask/tests/unit/wqflask/test_markdown_routes.py
index 3adf63e5..2d403d04 100644
--- a/wqflask/tests/unit/wqflask/test_markdown_routes.py
+++ b/wqflask/tests/unit/wqflask/test_markdown_routes.py
@@ -23,8 +23,7 @@ class MockRequests200:
 This is some content
 
 ## Sub-heading
-This is another sub-heading
-        """
+This is another sub-heading"""
 
 class TestMarkdownRoutesFunctions(unittest.TestCase):
     """Test cases for functions in markdown_routes"""
@@ -32,27 +31,26 @@ class TestMarkdownRoutesFunctions(unittest.TestCase):
     @mock.patch('wqflask.markdown_routes.requests.get')
     def test_render_markdown_when_fetching_locally(self, requests_mock):
         requests_mock.return_value = MockRequests404()
-        markdown_content = render_markdown("glossary.md")
+        markdown_content = render_markdown("general/glossary/glossary.md")
         requests_mock.assert_called_with(
             "https://raw.githubusercontent.com"
-            "/genenetwork/genenetwork2/"
-            "wqflask/wqflask/static/"
-            "glossary.md")
+            "/genenetwork/gn-docs/"
+            "master/general/"
+            "glossary/glossary.md")
         self.assertRegexpMatches(markdown_content,
-                                 "Glossary of Terms and Features")
+                                 "Content for general/glossary/glossary.md not available.")
 
     @mock.patch('wqflask.markdown_routes.requests.get')
     def test_render_markdown_when_fetching_remotely(self, requests_mock):
         requests_mock.return_value = MockRequests200()
-        markdown_content = render_markdown("glossary.md")
+        markdown_content = render_markdown("general/glossary/glossary.md")
         requests_mock.assert_called_with(
             "https://raw.githubusercontent.com"
-            "/genenetwork/genenetwork2/"
-            "wqflask/wqflask/static/"
-            "glossary.md")
+            "/genenetwork/gn-docs/"
+            "master/general/"
+            "glossary/glossary.md")
         self.assertEqual("""
    Glossary
    
 
    This is some content
    
 
    Sub-heading
    
-
    This is another sub-heading
    
-""",
+
    This is another sub-heading
    """,
                          markdown_content)
-- 
cgit v1.2.3


From 8fc58655c9b98e816f31e4a4f99b6879fe304bca Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Fri, 20 Nov 2020 20:03:23 +0300
Subject: Replace mockobject with dataclasses

* wqflask/tests/unit/wqflask/test_markdown_routes.py
(MockRequests404): Use dataclasses.
(MockRequests200): Ditto.
---
 wqflask/tests/unit/wqflask/test_markdown_routes.py | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/wqflask/tests/unit/wqflask/test_markdown_routes.py b/wqflask/tests/unit/wqflask/test_markdown_routes.py
index 2d403d04..90e0f17c 100644
--- a/wqflask/tests/unit/wqflask/test_markdown_routes.py
+++ b/wqflask/tests/unit/wqflask/test_markdown_routes.py
@@ -3,28 +3,26 @@
 import unittest
 from unittest import mock
 
+from dataclasses import dataclass
 from wqflask.markdown_routes import render_markdown
 
 
+@dataclass
 class MockRequests404:
-    @property
-    def status_code(self):
-        return 404
+    status_code: int = 404
 
-class MockRequests200:
-    @property
-    def status_code(self):
-        return 200
 
-    @property
-    def content(self):
-        return b"""
+@dataclass
+class MockRequests200:
+    status_code: int = 200
+    content: str = b"""
 # Glossary
 This is some content
 
 ## Sub-heading
 This is another sub-heading"""
 
+
 class TestMarkdownRoutesFunctions(unittest.TestCase):
     """Test cases for functions in markdown_routes"""
 
-- 
cgit v1.2.3


From 4a16262f1f8097122ed0bedf2e92211627ad223a Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Sat, 21 Nov 2020 09:58:14 +0300
Subject: Use docker image with markdown packaged

* .github/workflows/main.yml (jobs):
[container]: Use image that is on python3-genenetwork "ad741c1" which
has python-markdown packaged.
---
 .github/workflows/main.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index c78f6d85..2342796a 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -11,7 +11,7 @@ on:
 jobs:
   unittest:
     runs-on: ubuntu-latest
-    container: bonfacekilz/python3-genenetwork2:0bf4ee6
+    container: bonfacekilz/python3-genenetwork2:ad741c1
 
     steps:
     # First start with mariadb set then checkout. The checkout gives
-- 
cgit v1.2.3


From 37904013a43f99a3fffa73d16245d1b79a3d61ba Mon Sep 17 00:00:00 2001
From: zsloan
Date: Mon, 23 Nov 2020 10:35:22 -0600
Subject: Changed correlation matrix cells for traits with zero shared samples
 to instead say N/A and be a shade of grey

---
 wqflask/wqflask/templates/correlation_matrix.html | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/wqflask/wqflask/templates/correlation_matrix.html b/wqflask/wqflask/templates/correlation_matrix.html
index d556f31a..4e150618 100644
--- a/wqflask/wqflask/templates/correlation_matrix.html
+++ b/wqflask/wqflask/templates/correlation_matrix.html
@@ -51,8 +51,12 @@
       {% if result[0].name == trait.name and result[0].dataset == trait.dataset %}
       n
    {{ result[2] }}    
       {% else %}
+      {% if result[1] == 0 %}
+      N/A
+      {% else %}
       {{ '%0.2f' % result[1] }}
    {{ result[2] }}    
       {% endif %}
+      {% endif %}
       {% endfor %}
     
     {% endfor %}
-- 
cgit v1.2.3


From 3957634c1fd24eb61efe0adc74fe7c6c7841ad86 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Tue, 24 Nov 2020 10:27:29 +0300
Subject: solve issues to do with decoding in python3

---
 wqflask/wqflask/docs.py  | 4 +++-
 wqflask/wqflask/views.py | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/docs.py b/wqflask/wqflask/docs.py
index d653c269..23fc3cad 100644
--- a/wqflask/wqflask/docs.py
+++ b/wqflask/wqflask/docs.py
@@ -19,8 +19,10 @@ class Docs(object):
             self.title = self.entry.capitalize()
             self.content = ""
         else:
+            
             self.title = result[0]
-            self.content = result[1]
+            self.content = result[1].decode("utf-8")
+
 
         self.editable = "false"
         # ZS: Removing option to edit to see if text still gets vandalized
diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py
index b7c4d142..bf2e9026 100644
--- a/wqflask/wqflask/views.py
+++ b/wqflask/wqflask/views.py
@@ -304,8 +304,8 @@ def news():
 @app.route("/references")
 def references():
     doc = Docs("references", request.args)
+    # return render_template("reference.html")
     return render_template("docs.html", **doc.__dict__)
-    #return render_template("reference.html")
 
 @app.route("/intro")
 def intro():
-- 
cgit v1.2.3


From cf4db5545efee6d9052153708fc05ca707ef1716 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Tue, 24 Nov 2020 12:51:17 -0600
Subject: Added html/css for new "Filter Samples by Value" feature and changed
 a couple class names to make more sense for being shared between both this
 feature and "Block Samples by Index"

---
 wqflask/wqflask/static/new/css/show_trait.css      |  4 ++--
 .../templates/show_trait_transform_and_filter.html | 23 ++++++++++++++++++++--
 2 files changed, 23 insertions(+), 4 deletions(-)

diff --git a/wqflask/wqflask/static/new/css/show_trait.css b/wqflask/wqflask/static/new/css/show_trait.css
index 5e1a279b..39c6ba53 100644
--- a/wqflask/wqflask/static/new/css/show_trait.css
+++ b/wqflask/wqflask/static/new/css/show_trait.css
@@ -145,11 +145,11 @@ input.corr-location {
   display: inline;
 }
 
-div.block-by-index-div {
+div.block-div {
   margin-bottom: 10px;
 }
 
-div.block-by-attribute-div {
+div.block-div-2 {
   margin-top:10px;
   margin-bottom:10px;
 }
diff --git a/wqflask/wqflask/templates/show_trait_transform_and_filter.html b/wqflask/wqflask/templates/show_trait_transform_and_filter.html
index 0418d972..22c52d8c 100644
--- a/wqflask/wqflask/templates/show_trait_transform_and_filter.html
+++ b/wqflask/wqflask/templates/show_trait_transform_and_filter.html
@@ -4,7 +4,7 @@
         Reset option as
         needed.
     
    
-    
    
+    
    
         
         
         
+        
+        
+        
+        
+      
+      
+      
+      
+    
    
     {% if sample_groups[0].attributes %}
-    
    
+    
    
         
         
+          {% for attribute in sample_groups[0].attributes %}
+          {% if sample_groups[0].attributes[attribute].distinct_values|length <= 10 %}
+          
+          {% endif %}
+          {% endfor %}
+        
+        
+        
+        
+    
    
+    {% endif %}
     
    
-      
+      
+      {% if sample_groups[0].attributes %}
+      
+      {% endif %}
       
       
     
    
-    {% if sample_groups[0].attributes %}
-    
    
-        
-        
-        
-        
-    
    
-    {% endif %}
     
    
       
       
-- 
cgit v1.2.3


From a53302a077b8eea3591460a03a60c9621c3f4586 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 25 Nov 2020 13:11:11 -0600
Subject: Substituted CDN link to the font-awesome CSS with the local guix
 profile link

---
 wqflask/wqflask/templates/search_result_page.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/templates/search_result_page.html b/wqflask/wqflask/templates/search_result_page.html
index 8e2b06a4..35d8ed27 100644
--- a/wqflask/wqflask/templates/search_result_page.html
+++ b/wqflask/wqflask/templates/search_result_page.html
@@ -2,9 +2,9 @@
 {% block title %}Search Results{% endblock %}
 {% block css %}
     
+    
     
     
-    
     
     
 {% endblock %}
-- 
cgit v1.2.3


From c8a339616fb3dd3f63427378dc4285de9ad1ccef Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 25 Nov 2020 13:11:48 -0600
Subject: Removed an unused console.timeEnd in the JS

---
 wqflask/wqflask/templates/search_result_page.html | 2 --
 1 file changed, 2 deletions(-)

diff --git a/wqflask/wqflask/templates/search_result_page.html b/wqflask/wqflask/templates/search_result_page.html
index 35d8ed27..9a28a78e 100644
--- a/wqflask/wqflask/templates/search_result_page.html
+++ b/wqflask/wqflask/templates/search_result_page.html
@@ -440,8 +440,6 @@
                   'processing': 'Loading...'
                 }
             } );
-
-            console.timeEnd("Creating table");
             
             $('.toggle-vis').on( 'click', function (e) {
               e.preventDefault();
-- 
cgit v1.2.3


From ab6681e545bd4c84e1f74107b90ea52f7d2ca24c Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 25 Nov 2020 13:15:25 -0600
Subject: Added form options for changing manhattan plot color scheme + added
 necessary imports and hidden inputs

---
 wqflask/wqflask/templates/mapping_results.html | 47 +++++++++++++++++++++++---
 1 file changed, 42 insertions(+), 5 deletions(-)

diff --git a/wqflask/wqflask/templates/mapping_results.html b/wqflask/wqflask/templates/mapping_results.html
index 28d93542..9542c29d 100644
--- a/wqflask/wqflask/templates/mapping_results.html
+++ b/wqflask/wqflask/templates/mapping_results.html
@@ -40,6 +40,9 @@
         
         
         
+        {% if manhattan_plot == True %}
+        
+        {% endif %}
         
         
         
@@ -55,7 +58,7 @@
         
         
         
-        
    
+        
    
           
    
               
    Map Viewer: Whole Genome

    
               Population: {{ dataset.group.species|capitalize }} {{ dataset.group.name }}
    
@@ -77,7 +80,7 @@
               
-                  
-                  
+                  
@@ -114,11 +117,31 @@
                 
-                  
    
                 
    
                   Chr: 
+                  
                     
                 
    
                 
    View: View: 
                   
                      to 
                   
    
                 
    
                   Width: 
+                  
                      pixels (minimum=900)
                   
                 
    
               
    
+              {% if manhattan_plot == True and selectedChr == -1 %}
+              
+                
+                  
+                  
+                  
+                
+              
    
+                    Manhattan Plot Color Scheme: 
+                  
+                    
+                  
+                    
+                    
+                  
    
+              {% endif %}
             
    
             
    
                   {% if (mapping_method == "reaper" or mapping_method == "rqtl_geno") and nperm > 0 %}
@@ -328,6 +351,9 @@
     
     
     
+    {% if manhattan_plot == True and selectedChr == -1 %}
+    
+    {% endif %}
 
     
     
@@ -423,7 +449,7 @@
 
         var mapping_input_list = ['temp_uuid', 'trait_id', 'dataset', 'tool_used', 'form_url', 'method', 'transform', 'trimmed_markers', 'selected_chr', 'chromosomes', 'mapping_scale',
                             'score_type', 'suggestive', 'significant', 'num_perm', 'permCheck', 'perm_output', 'perm_strata', 'categorical_vars', 'num_bootstrap', 'bootCheck', 'bootstrap_results',
-                            'LRSCheck', 'covariates', 'maf', 'use_loco', 'manhattan_plot', 'control_marker', 'control_marker_db', 'do_control', 'genofile', 
+                            'LRSCheck', 'covariates', 'maf', 'use_loco', 'manhattan_plot', 'color_scheme', 'manhattan_single_color', 'control_marker', 'control_marker_db', 'do_control', 'genofile',
                             'pair_scan', 'startMb', 'endMb', 'graphWidth', 'lrsMax', 'additiveCheck', 'showSNP', 'showGenes', 'viewLegend', 'haplotypeAnalystCheck', 
                             'mapmethod_rqtl_geno', 'mapmodel_rqtl_geno', 'temp_trait', 'group', 'species', 'reaper_version', 'primary_samples', 'n_samples']
 
@@ -449,10 +475,21 @@
 
         remap = function() { 
             $('input[name=selected_chr]').val($('select[name=chromosomes]').val());
+            $('input[name=color_scheme]').val($('select#color_scheme').val());
             $('#marker_regression_form').attr('action', '/loading');
             return $('#marker_regression_form').submit(); 
         };
 
+        {% if manhattan_plot == True and selectedChr == -1 %}
+        $('#color_scheme').change(function(){
+          if ($(this).val() == "single"){
+            $('#point_color_picker').show();
+          } else {
+            $('#point_color_picker').hide();
+          }
+        });
+        {% endif %}
+
         {% if mapping_method != "gemma" and mapping_method != "plink" %}
         $('#download_perm').click(function(){
           perm_info_dict = {
-- 
cgit v1.2.3


From a1563aee47a54d5ff4253e1da7e851afe28805c0 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 25 Nov 2020 13:16:26 -0600
Subject: Added code for passing around form parameters related to manhattan
 plot coloration to run_mapping.py and views.py

---
 wqflask/wqflask/marker_regression/run_mapping.py | 7 ++++++-
 wqflask/wqflask/views.py                         | 2 ++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/marker_regression/run_mapping.py b/wqflask/wqflask/marker_regression/run_mapping.py
index 31d6a67c..c474e0e0 100644
--- a/wqflask/wqflask/marker_regression/run_mapping.py
+++ b/wqflask/wqflask/marker_regression/run_mapping.py
@@ -138,7 +138,12 @@ class RunMapping(object):
             mapping_results_filename = self.dataset.group.name + "_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
             self.mapping_results_path = "{}{}.csv".format(webqtlConfig.GENERATED_IMAGE_DIR, mapping_results_filename)
 
-        if start_vars['manhattan_plot'] == "true":
+        if start_vars['manhattan_plot']:
+            self.color_scheme = "alternating"
+            if "color_scheme" in start_vars:
+                self.color_scheme = start_vars['color_scheme']
+                if self.color_scheme == "single":
+                    self.manhattan_single_color = start_vars['manhattan_single_color']
             self.manhattan_plot = True
         else:
             self.manhattan_plot = False
diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py
index b7c4d142..7118bab7 100644
--- a/wqflask/wqflask/views.py
+++ b/wqflask/wqflask/views.py
@@ -718,6 +718,8 @@ def mapping_results_page():
         'maf',
         'use_loco',
         'manhattan_plot',
+        'color_scheme',
+        'manhattan_single_color',
         'control_marker',
         'control_marker_db',
         'do_control',
-- 
cgit v1.2.3


From d1740605ffd36e49b9560e81c0e085c58ef8b688 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 25 Nov 2020 13:17:03 -0600
Subject: Added actual code for coloring manhattan plot differently to
 display_mapping_results.py

---
 .../marker_regression/display_mapping_results.py   | 46 ++++++++++++++++++++--
 1 file changed, 43 insertions(+), 3 deletions(-)

diff --git a/wqflask/wqflask/marker_regression/display_mapping_results.py b/wqflask/wqflask/marker_regression/display_mapping_results.py
index 3f6de2b2..87910401 100644
--- a/wqflask/wqflask/marker_regression/display_mapping_results.py
+++ b/wqflask/wqflask/marker_regression/display_mapping_results.py
@@ -74,6 +74,34 @@ DARKVIOLET = ImageColor.getrgb("darkviolet")
 MEDIUMPURPLE = ImageColor.getrgb("mediumpurple")
 # ---- END: Define common colours ---- #
 
+# ZS: List of distinct colors for manhattan plot if user selects "varied"
+DISTINCT_COLOR_LIST = [
+    ImageColor.getrgb("#FF0000"),
+    ImageColor.getrgb("#00FF00"),
+    ImageColor.getrgb("#0000FF"),
+    ImageColor.getrgb("#FFFF00"),
+    ImageColor.getrgb("#FF00FF"),
+    ImageColor.getrgb("#00FFFF"),
+    ImageColor.getrgb("#000000"),
+    ImageColor.getrgb("#800000"),
+    ImageColor.getrgb("#008000"),
+    ImageColor.getrgb("#000080"),
+    ImageColor.getrgb("#808000"),
+    ImageColor.getrgb("#800080"),
+    ImageColor.getrgb("#008080"),
+    ImageColor.getrgb("#808080"),
+    ImageColor.getrgb("#C00000"),
+    ImageColor.getrgb("#00C000"),
+    ImageColor.getrgb("#0000C0"),
+    ImageColor.getrgb("#C0C000"),
+    ImageColor.getrgb("#C000C0"),
+    ImageColor.getrgb("#00C0C0"),
+    ImageColor.getrgb("#C0C0C0"),
+    ImageColor.getrgb("#400000"),
+    ImageColor.getrgb("#004000"),
+    ImageColor.getrgb("#000040"),
+]
+
 # ---- FONT FILES ---- #
 VERDANA_FILE = "./wqflask/static/fonts/verdana.ttf"
 VERDANA_BOLD_FILE = "./wqflask/static/fonts/verdanab.ttf"
@@ -293,6 +321,12 @@ class DisplayMappingResults(object):
             self.plotScale = "physic"
 
         self.manhattan_plot = start_vars['manhattan_plot']
+        if self.manhattan_plot:
+            self.color_scheme = "alternating"
+            if 'color_scheme' in start_vars:
+                self.color_scheme = start_vars['color_scheme']
+                if self.color_scheme == "single":
+                    self.manhattan_single_color = ImageColor.getrgb("#" + start_vars['manhattan_single_color'])
 
         if 'permCheck' in list(start_vars.keys()):
             self.permChecked = start_vars['permCheck']
@@ -2424,10 +2458,16 @@ class DisplayMappingResults(object):
                             Yc = yZero - qtlresult['lod_score']*LRSHeightThresh/LRS_LOD_Max
 
                 if self.manhattan_plot == True:
-                    if self.selectedChr == -1 and (previous_chr_as_int % 2 == 1):
-                        point_color = RED
+                    if self.color_scheme == "single":
+                        point_color = self.manhattan_single_color
+                    elif self.color_scheme == "varied":
+                        point_color = DISTINCT_COLOR_LIST[previous_chr_as_int]
                     else:
-                        point_color = BLUE
+                        if self.selectedChr == -1 and (previous_chr_as_int % 2 == 1):
+                            point_color = RED
+                        else:
+                            point_color = BLUE
+
                     im_drawer.text(
                         text="5",
                         xy=(
-- 
cgit v1.2.3


From 60447282a0f0063513ab10251945df3ab2c2dbfd Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 00:03:07 +0300
Subject: add reference  markdown and github button

---
 wqflask/wqflask/templates/reference.html | 1541 +-----------------------------
 1 file changed, 53 insertions(+), 1488 deletions(-)

diff --git a/wqflask/wqflask/templates/reference.html b/wqflask/wqflask/templates/reference.html
index d95d22e3..78469d39 100644
--- a/wqflask/wqflask/templates/reference.html
+++ b/wqflask/wqflask/templates/reference.html
@@ -1,1497 +1,62 @@
 {% extends "base.html" %}
-{% block title %}References{% endblock %}
-{% block content %}
-
-
     Papers and References to GeneNetwork
    
-
-
    
-
-		
-Highlighted References |
-Key References |
-Background References |	
-	
-
-
     
    
-
-2020 |
-2019 |
-2018 |
-2017 |
-2016 |
-2015 |
-2014 |
-2013 |
-2012 |
-2011 |
-2010 |
-2009 |  
-2008 |  
-2007 |
-2006 |
-2005 |
-2004 |
-2003 |
-
-
    
-
-
- Google Scholar search for   "genenetwork" OR "webqtl"   generated:
-
      
-
    1. 1430 hits on 2016/09/08
-
    2. 1730 hits on 2017/10/17
-
    3. 2020 hits on 2019/05/13)
-
    
-
-Google Scholar search for   "genenetwork.org"  generated:
-
-
      
-
    1. 1030 hits on 2019/05/13
-
    2.   105 hits from 2018/01/01 to 2019/05/13
-
    
-
-    
-
-
-
    
-		Highlighted References
-
    
-
-Please send us citations to articles that we may have missed.
-
-
      
-
-
    1.  Mulligan MK, Mozhui K, Prins P, Williams RW (2017) GeneNetwork: A Toolbox for Systems Genetics. In Systems Genetics, Methods in Molecular Biology 1488:75-120
-[An updated primer in using GeneNetwork for molecular analysis of mouse and human cohorts.] 
-PDF version
-
-
      
-
      
-
-
    2.  Williams RW, Williams EG (2017) Resources for Systems Genetics. In Systems Genetics, Methods in Molecular Biology 1488:3-29.
-[This review is intended to help you when making the hard choices about types of resources to use in system genetic studies. The short answer: embrace diversity in your resources. The computational barriers to joint analysis are now minimal.] 
-PDF version
-
-
      
-
      
-
-
    3.  Williams RW (2009) Herding cats: the sociology of data integration. Frontier in Neuroscience 3(2):154-6.
-[An short but amusing commentary on predictive (someday perhaps even precision) medicine and how to get there using genetic reference populations such as the BXD and Collaborative Cross families.] 
-PDF version
-
-
      
-
      
-
-
-
    4. 
-Pinelli M, Carissimo A, Cutillo L, Lai CH, Mutarelli M, Moretti MN, Singh MV, Karali M, Carrella D, Pizzo M, Russo F, Ferrari S, Ponzin D, Angelini C, Banfi S, di Bernardo D  (2016) An atlas of gene expression and gene co-regulation in the human retina.Nucleic Acids Res. 2016 Jul 8;44(12):5773-84
-RETINA database, and see the PDF. These data are now in GeneNetwork (Species = Human, Group = Retina).
-
-
-
      
-
      
-
-
-
    5. 
-Williams EG, Wu Y, Pooja J, Dubuis S, Blattmann P, Argmann CA, Houten SM, Amariuta T, Wolski W, Zamboni N, Aebersold R, Auwerx J (2016) Systems proteomics and trans-omic integration illuminate new mechanisms in mitochondrial function. Science 352(6291):aad0189
-HTML version, PDF
-
-
      
-
      
-
-
-
-
    6. 	
-Sloan Z, Arends D, Broman KW, Centeno A, Furlotte N, Nijveen H, Yan L, Zhou X, Williams RW, Prins P (2016) GenetNetwork: framework for web-based genetics. The Journal of Open Source Software http://joss.theoj.org/papers/10.21105/joss.00025  (http://dx.doi.org/10.21105/joss.00025)
-HTML version
-
-
      
-
      
-
-
    7. Alam G, Miller DB, O'Callaghan JP, Lu L, Williams RW, Jones BC  (2016) MPTP neurotoxicity is highly concordant between the sexes among BXD recombinant inbred mouse strains. Neurotoxicology in press
-PDF version
-
-
      
-
      
-
-
-
    8. Merkwirth C, Jovaisaite V, Durieux J, Matilainen O, Jordan SD, Quiros PM, Steffen KK, Williams EG, Mouchiroud L, Tronnes SU, Murillo V, Wolff SC, Shaw RJ, Auwerx J, Dillin A (2016) Two conserved histone demethylases regulate mitochondrial stress-induced longevity. Cell 165:1209-1223
-
      
-
      
- 
-
-
    9.  Wang X, Pandey AK, Mulligan MK, Williams EG, Mozhui K, Li Z, Jovaisaite V, Quarles LD, Xiao Z, Huang J, Capra JA, Chen Z, Taylor WL, Bastarache L, Niu X, Pollard KS, Ciobanu DC, Reznik AO, Tishkov AV, Zhulin IB, Peng J, Nelson SF, Denny JC, Auwerx J, Lu L, Williams RW (2016). Joint mouse-human phenome-wide association to test gene function and disease risk. Nature Communications 7:10464 
-[Best reference on the BXD family of strains and the wide range of phenotypes that have been generated over the past 40 years.] 
-
-PDF version and the
-
-Supplementary Tables in one Excel file
-
-
-
      
-
      
- 
-
-
    10.  Xue Y, Li J, Yan L, Lu L, Liao FF (2015) Genetic variability to diet-induced hippocampal dysfunction in BXD recombinant inbred (RI) mouse strains. Behav Brain Res 292:83-94
-
      
-
      
-
-
    11. Chaum E, Winborn CS, Bhattacharya S (2015) Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Mamm Genome 26:210-221
-
      
-
      
-
-
    12. Cheng Q, Seltzer Z, Sima C, Lakschevitz FS, Glogauer M. (2015) Quantitative Trait Loci and Candidate Genes for Neutrophil Recruitment in Sterile Inflammation Mapped in AXB-BXA Recombinant Inbred Mice. PLoS One 10:e0124117
-
      
-
      
-
-
    13. Scott RE, Ghule PN, Stein JL, Stein GS (2015) Cell cycle gene expression networks discovered using systems biology: Significance in carcinogenesis. J Cell Physiol 230:2533-42
-
      
-
      
-
-
    14.  Williams EG, Auwerx J (2015) The convergence of systems and reductionist approaches in complex trait analysis. Cell 162:23-32. 
-
      
-
      
 
-
    15. Wu Y, Williams EG, Dubuis S, Mottis A, Jovaisaite V, Houten SM, Argmann CA, Faridi P, Wolski W, Kutalik Z, Zamboni N, Auwerx J, Aebersold R (2014) Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Cell 158:1415-1430
-
      
-
      
+{% block title %}Reference{% endblock %}
 
-
    16. 
-Hayes SK, Hager R, Grencis RK (2014) Sex-dependent genetics effects on immune responses to parasitic nematodes. BMC Genomics 15:193
-
      
-
      
-
-
    17. Harenza JL, Muldoon PP, De Biasi M, Damaj MI, Miles MF (2014) Genetic variation within the Chrna7 gene modulates nicotine reward-like phenotypes in mice. Genes Brain Behav 13:213-225
-
      
-
      
-
-
-
    18. 
-Ye R, Carneiro AM, Airey D, Sanders-Bush E, Williams RW, Lu L, Wang J, Zhang B, Blakely RD (2014) Evaluation of heritable determinants of blood and brain serotonin homeostasis using recombinant inbred mice. Genes Brain Behav. 13:247-260
+{% block css %}
+
+{% endblock %}
 
-
      
-
      
-
    19. 
-Houtkooper RH, Mouchiroud L, Ryu D, Moullan N, Katsyuba E, Knott G,  Williams RW,  Auwerx J (2013) Mitonuclear protein imbalance as a conserved longevity mechanism. Nature 497:451-457  PDF version
+{% block content %}
 
-
      
-
      
+ 
      
+ Edit on Github	
+ 
      
+
+
      
+   {{ rendered_markdown|safe }}
+
+	
+  
+
      
+
+
 
 {% endblock %}
+
-- 
cgit v1.2.3


From 2681d11a9c39a3291db03521e5b7f73ed672533c Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 00:04:22 +0300
Subject: register references blueprint

---
 wqflask/wqflask/__init__.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/__init__.py b/wqflask/wqflask/__init__.py
index 874cde17..39a36fa3 100644
--- a/wqflask/wqflask/__init__.py
+++ b/wqflask/wqflask/__init__.py
@@ -7,6 +7,7 @@ from flask import g
 from flask import Flask
 from utility import formatting
 from wqflask.markdown_routes import glossary_blueprint
+from wqflask.markdown_routes import  references_blueprint
 
 app = Flask(__name__)
 
@@ -19,6 +20,7 @@ app.jinja_env.globals.update(
 
 # Registering blueprints
 app.register_blueprint(glossary_blueprint, url_prefix="/glossary")
+app.register_blueprint(references_blueprint,url_prefix="/references")
 
 @app.before_request
 def before_request():
@@ -39,4 +41,4 @@ from wqflask import db_info
 from wqflask import user_login
 from wqflask import user_session
 
-import wqflask.views
+import wqflask.views 
-- 
cgit v1.2.3


From 23d27c88bcc085bac3fb14b534cf1d4a7f11f22a Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 00:07:01 +0300
Subject: modify references links

---
 wqflask/wqflask/markdown_routes.py  | 33 ++++++++++++++++++++++++++++++++-
 wqflask/wqflask/templates/base.html |  2 +-
 wqflask/wqflask/views.py            |  5 -----
 3 files changed, 33 insertions(+), 7 deletions(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 7a9fac41..60d4374c 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -9,7 +9,10 @@ from flask import Blueprint
 from flask import render_template
 
 glossary_blueprint = Blueprint('glossary_blueprint', __name__)
-
+references_blueprint=Blueprint("references_blueprint",__name__)
+environments_blueprint=Blueprint("environments_blueprint",__name__)
+links_blueprint=Blueprint("links_blueprint",__name__)
+policies_blueprint=Blueprint("policies_blueprint",__name__)
 
 def render_markdown(file_name):
     """Try to fetch the file name from Github and if that fails, try to
@@ -22,6 +25,7 @@ look for it inside the file system
     if md_content.status_code == 200:
         return markdown.Markdown().convert(md_content.content.decode("utf-8"))
 
+
     # TODO: Add fallback on our git server by checking the mirror.
 
     # Content not available
@@ -32,8 +36,35 @@ look for it inside the file system
             "Please reach out to the gn2 team to have a look at this")
 
 
+
+
 @glossary_blueprint.route('/')
 def glossary():
     return render_template(
         "glossary.html",
         rendered_markdown=render_markdown("general/glossary/glossary.md")), 200
+
+
+@references_blueprint.route('/')
+def references():
+    return render_template(
+        "reference.html",
+        rendered_markdown=render_markdown("general/glossary/glossary.md")), 200
+
+
+
+@environments_blueprint.route("/")
+def references():
+    return render_template("environments.html",rendered_markdown=render_markdown("general/environments/environments.md"))
+
+
+@links_blueprint.route("/")
+def links():
+    return rendered_template("links.html",rendered_markdown=render_markdown("general/links/links.md"))
+
+
+policies_blueprint.route("/")
+def policies():
+    return rendered_template("policies.html",rendered_markdown=rendered_markdown("general/policies/policies.md"))
+
+
diff --git a/wqflask/wqflask/templates/base.html b/wqflask/wqflask/templates/base.html
index 0e572fcf..ca6c9d9b 100644
--- a/wqflask/wqflask/templates/base.html
+++ b/wqflask/wqflask/templates/base.html
@@ -64,7 +64,7 @@
                         
    20. 
                             
                             
        
-                              
      • References
        • 
+                              
      • References
        • 
                               
      • Tutorials/Primers
        • 
                               
      • Glossary of Term
        • 
                               
      • FAQ
        • 
diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py
index bf2e9026..060c5a7b 100644
--- a/wqflask/wqflask/views.py
+++ b/wqflask/wqflask/views.py
@@ -301,11 +301,6 @@ def news():
     doc = Docs("news", request.args)
     return render_template("docs.html", **doc.__dict__)
 
-@app.route("/references")
-def references():
-    doc = Docs("references", request.args)
-    # return render_template("reference.html")
-    return render_template("docs.html", **doc.__dict__)
 
 @app.route("/intro")
 def intro():
-- 
cgit v1.2.3


From d0550bfee7e4eaf986d797c9ebadc7b8bc59f891 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 00:10:33 +0300
Subject: modify github button

---
 wqflask/wqflask/templates/reference.html | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/wqflask/wqflask/templates/reference.html b/wqflask/wqflask/templates/reference.html
index 78469d39..0ba7d7d5 100644
--- a/wqflask/wqflask/templates/reference.html
+++ b/wqflask/wqflask/templates/reference.html
@@ -15,10 +15,6 @@
 
        
    {{ rendered_markdown|safe }}
 
-	
-  
 
        
 
 
 
-{% endblock %}
-
+{% endblock %}
\ No newline at end of file
-- 
cgit v1.2.3


From a0bb3633c0288e9636e8abb1185d051ac02ea446 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 12:41:14 +0300
Subject: move css to markdown and add references

---
 wqflask/wqflask/static/new/css/markdown.css | 33 +++++++++++++++++
 wqflask/wqflask/templates/reference.html    | 56 -----------------------------
 wqflask/wqflask/templates/references.html   | 30 ++++++++++++++++
 3 files changed, 63 insertions(+), 56 deletions(-)
 delete mode 100644 wqflask/wqflask/templates/reference.html
 create mode 100644 wqflask/wqflask/templates/references.html

diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
index 91167908..d8dd4776 100644
--- a/wqflask/wqflask/static/new/css/markdown.css
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -11,3 +11,36 @@
     margin-right: auto;
     margin-left: auto;
 }
+#markdown {
+	background: red;
+}
+
+
+
+	.github-btn{
+		margin:20px 10px;
+		display: flex;
+		width: 90vw;
+		justify-content: flex-end;
+	}
+	.github-btn a{
+		display: inline-block;
+		align-self:center;
+		text-decoration: none;
+		transition-property:all;
+		transition-duration:0.5s;
+		padding:8px 12px;
+		color:#fff;
+		/*background: #27407a;*/
+		background: blue;
+		border-radius: 20px;
+		background: red;
+		/*text-align: center;*/
+
+
+	}
+	.github-btn a:hover{
+
+		background: #333;
+
+	}
diff --git a/wqflask/wqflask/templates/reference.html b/wqflask/wqflask/templates/reference.html
deleted file mode 100644
index e04deed5..00000000
--- a/wqflask/wqflask/templates/reference.html
+++ /dev/null
@@ -1,56 +0,0 @@
-{% extends "base.html" %}
-
-{% block title %}Reference{% endblock %}
-
-{% block css %}
-
-{% endblock %}
-
-{% block content %}
-
- 
        
- Edit on Github	
- 
        
-
-
        
-   {{ rendered_markdown|safe }}
-
-
        
-
-
-
-{% endblock %}
\ No newline at end of file
diff --git a/wqflask/wqflask/templates/references.html b/wqflask/wqflask/templates/references.html
new file mode 100644
index 00000000..5e86d95b
--- /dev/null
+++ b/wqflask/wqflask/templates/references.html
@@ -0,0 +1,30 @@
+{% extends "base.html" %}
+
+{% block title %}Reference{% endblock %}
+
+{% block css %}
+
+{% endblock %}
+
+{% block content %}
+
+ 
        
+ Edit on Github	
+ 
        
+
+
        
+   {{ rendered_markdown|safe }}
+
+
        
+
+
+
+{% endblock %}
\ No newline at end of file
-- 
cgit v1.2.3


From 98b9fa88028704d504d3186e0dc032152b535181 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 13:53:47 +0300
Subject: delete templates/links page

---
 wqflask/wqflask/templates/links.html | 451 -----------------------------------
 1 file changed, 451 deletions(-)
 delete mode 100644 wqflask/wqflask/templates/links.html

diff --git a/wqflask/wqflask/templates/links.html b/wqflask/wqflask/templates/links.html
deleted file mode 100644
index 6ff7fc6c..00000000
--- a/wqflask/wqflask/templates/links.html
+++ /dev/null
@@ -1,451 +0,0 @@
-{% extends "base.html" %}
-{% block title %}Links{% endblock %}
-{% block content %}
-
-
-		
-		
-		
        
-
-
-
        Links for Exploring Networks of Genes and Phenotypes 
        
-
-
-
        GeneNetwork and WebQTL have integrated links to the following resources
        
-
        
-
        GN PARTNERS, DEVELOPMENT SITES, AND MIRRORS
        
-
-
        
-
-
        
-
-
        KEY RESOURCES CONNECTED WITH GENENETWORK
        
-
        
-
-
        	
-
-
        
-
-
        Resources for Analysis of Single Genes, SNPs, mRNAs, and Proteins
        
-
        
-
        
-Harvester retrieves summary data on any one of 57,000 proteins from several bioinformatic resources.
-[Added Dec 22, 2004; last site review Sept 19, 2008 by RWW.]
-
        
-
-
        
-GSCAN: the Oxford Wellcome Trust Genome Viewer retrieves mapping data for many mouse experimental mapping populations including the Heterogenous Stock, the Pre-Collaborative Cross mice, and Mouse Diversity Panel.
-[Added Oct 13, 2010; last site review Oct 13, 2010 by RWW.]
-
        
-
-
-
-
-
        
-NIF: The Neuroscience Information Framework retrieves summary from a wide variety of neuroscience and  bioinformatic resources. 
-[Added Dec 3, 2009; last site review Dec 3, 2009 by RWW.]
-
        
-
        
-Genomics of aging resources. A collection of databases and tools designed to help researchers understand the genetics of human ageing through a combination of functional genomics and evolutionary biology. 
-[Added Feb 28, 2010; last site review Feb 28, 2010 by RWW.]
-
        
-
        
-Human Disease Gene Database from Cardiff (requires log in)
-[Added Nov 5, 2009; last site review Nov 5, 2009 by RWW.]
-
        
-
        
-SNPnexus database of human SNPs is designed to simplify and assist in the selection of functionally relevant SNPs for large-scale genotyping studies of multifactorial disorders.[Added Jan 15, 2010; last site review Jan 15, 2010 by RWW.]
-
        
-
        
-SNPedia is a wiki investigating human genetics. We share information about the effects of variations in DNA, citing peer-reviewed scientific publications. It is used by Promethease to analyze and help explain your DNA.[Added Jan 15, 2010; last site review Jan 15, 2010 by RWW.]
-
        
-
        
-Genoglyphix Browser includes extensive data on human copy number variants as well as maps of low copy number repeat regions. 
-[Added Nov 5, 2009; last site review Nov 5, 2009 by RWW.]
-
        
-
        
-Human Protein Reference DB: a manually curated resource with data on 20,000 proteins. Very effective interface and rich data.
-[Added Oct 30, 2005; last site review Sept 19, 2008 by RWW.]
-
        	
-
        
-x:map is a terrific visual display tool for exploring Affymetrix Exon array data sets for mouse and human transcriptomes. 
-[Added April 18, 2008; last site review Sept 19, 2008 by RWW.]
-
        	
-
        
-iHOP retrieves PubMed sentences that report interactions between a reference gene and associate genes and proteins. It allows the assembly of complex graphs that plot the literature interactions of genes. Effective interface for humans and machines. 
-[Added Dec 25, 2004; last site review Sept 19, 2008 by RWW.]
-
        	
-
        
-Human Protein Atlas displays expression and localization of proteins in a large variety of normal human tissues and cancer cells as high resolution images of immunohistochemically stained tissues and cell lines. 
-[Added Sept 22, 2007; last site review Sept 22, 2007 by RWW.]
-
        	
-
        
-GoPubMed is a simple tool that searches PubMed and sorts the results by GO and MeSH terms. 
-[Added July 5, 2007 by RWW; last site review Sept 19, 2008 by RWW.]
-
        	
-
        
-AceView and the Alternative Splicing and Transcript Diversity database provide excellent resources for systematic information about the many alternative transcripts produced from single genes.
-[Added Jan 1, 2005; last site review Sept 19, 2008 by RWW.]
-
        	
-
-
        
-MGI and RGD are reference sites for mouse and rat genetics, respectively. 
-[Added Dec 22, 2004; last site review Sept 19, 2008 by RWW.]
-
        
-
-	
-
        
-GenAtlas provides summary data for approximately 19300 human genes and has a useful link that will fetch 10 Kb of upstream sequence for promoter analysis. 
-[Added Jan 9, 2005; last site review Sept 19, 2008 by RWW.]
-
        
-
        
-PromoLign aligns homologous regions of mouse and human promoters and highlights SNPs and transcription factor binding sites. Check the quick tutorial to see how to extract key data. This site requires an SVG plugin that may not be supported by some browsers and operating systems.
-[Added May 10, 2005; FAILED: last site review Sept 19, 2008 by RWW.]
-
        
-
        
-CGAP SNP Viewer allows users to view SNPs in the context of transcripts, ORFs and protein motifs for either human or mouse genes. Try the Ahr gene in mouse as an example. 
-[Added April 10, 2006; last site review Sept 19, 2008 by RWW.]
-
        
-
        
-Synapse Database (SynDB) is a comprehensive database of genes and proteins associated with the neuronal or neuromuscular synapse. Many Trait Data and Analysis pages provide links to SynDB. 
-[Added May 29, 2005; last site review Sept 19, 2008 by RWW.]
-
        
-
        
-Synapse Database at University of Pennsylvania is a comprehensive database of roughly 200 genes and proteins associated with the synapse. 
-[Added Nov 26, 2006; last site review Sept 19, 2008 by RWW.]
-
        
-
        
-Singapore Bio Databases and Tool. 
-[Added Dec 22, 2004; Dragon Genome Explorer site FAILED last site review, changed link; Sept 19, 2008 by RWW.]
-
        
-
        
- MutDB and SNPs3D provide great data on functional SNPs in human genes.  To analyze the functional impact of non-synonymous SNPs you will also find SNP Analyzer useful because it evaluates SNP impact in terms of the whole protein structural context.
-[Added Dec 22, 2004; last site review Sept 19, 2008 by RWW.]
-
        	
-
        
-Alternative Splicing Project provides great summaries and output graphs on splice variants in human, mouse, and Drosophila.
-[Added Nov 8, 2005; last site review Sept 19, 2008 by RWW.]
-
        	
-
        
-
-
        
-	
-
        Resources on Imprinting and Parental Origin Effects
        
-
        
-
        
-Geneimprint is a portal into the burgeoning field of genomic imprinting, collecting relevant articles and reviews, press reports, video and audio lectures, and genetic information   
-[Added June 23, 2010 by RWW; last site review June 23, 2010 by RWW.]
-
        	
-
        
-Catalogue of Parent of Origin Effects provides a list of imprinted and putatively imprinted genes with commentary by Ian Morison (University of Otago, New Zealand). Database was last updated in 2008.   
-[Added June 23, 2010 by RWW; last site review June 23, 2010 by RWW.]
-
        	
-
        
-
-
        	
-
-
        Resources for the Spatial Analysis of Gene and Protein Expression
        
-
        
-
        
-UBC Bioinformatic and Gene Expression Links is a very extensive and well curated collection of on-line resources for the analysis of biological data sets.   
-[Added May 24, 2007 by RWW; last site review Sept 19, 2008 by RWW.]
-
        	
-
        
-The mamep GeneExpression Links image database of whole-mounted in situ hybridization of mid-gestation mouse embryos. Try entering the symbol Ptch1.  
-[Added May 28, 2007 by RWW.]
-
        	
-
        
-The Genes to Cognition databases have a focus on proteins expressed in several key cellular compartments related to synpase function.  
-[Added Aug 9, 2007 by RWW.]
-
        
-
-
        
-Cerebellar Development Transcriptome Database. Expression data for the mouse cerebellum, both microarray and in situ. 
-[Added Sept 1, 2010; last site review Sept 1, 2010 by RWW.]
-
        
-
-	
-
        
-Several excellent resources can be used to explore patterns of gene expression primarily in C57BL/6J mice. This strain is one of the parents of the BXD, AXB/BXA, BXH, and CXB genetic reference populations that are key resources in the Gene Network and its companion site, the  Mouse Brain Library.
-
          
-
        • BGEM and GENSAT provide images of gene expression in brains of embryos, neonates, and adult mice (roughly 2008 genes as of July 2005).  
-
          
-
          
-
        • GenePaint and GeneAtlas are companion sites that also provide expression data in embryos, neonates, and adults at high spatial resolution. GeneAtlas has excellent but slow image searching and matching capabilities.
-
          
-
          
-
        • Allen Brain Atlas has expression data for ~12000 transcripts (adult males in the sagittal plane).
-
          
-
          
-
        • EMAP (Edinburgh Mouse Atlas Project) provides data on expression of ~800 genes during development (in situ, immunohistochemistry, and reporter knock-in expression patterns). Most data are from wholemounts between Theiler stages 11 and 20 (embryonic days E7 to E13). EMAP can be used as a Java WebStart application.
-
          
-
          
-
        • Mouse Atlas of Gene Expression is a massive SAGE library. The Atlas has quantified the normal state for many tissues by determining the number and identity of genes expressed throughout development. The scope of the project encompasses multiple stages of development of C57BL/6J mouse, from the single cell zygote to the adult, and includes an extensive initial collection of 200 tissues. DiscoverySpace is a WebStart application for use with The Mouse Atlas of Gene Expression.
-
          
-
          
-
        • Mahoney Center maintains a rich image collection for ~1000 transcription factors expressed in brain (developmental stages, coronal plane).
-[Added Dec 22, 2004; sites reviewed last on Sept 26, 2005 by RWW.]
-
          
-
          
-
        
-
        	
-
        	
-
-
        
-
-
        Resources for the Analysis of Sets and Networks of Transcripts, Genes, Proteins, and SNPs
        
-
        
-
        
-GeneMANIA helps you predict the function of your favourite genes and gene sets. Powerful and fast computational methods and a great use of Cytoscape Web. (2010 PDF).
-[Added July 1, 2010; last site review Aug 8, 2010 by RWW.]
-
        
-
        
-ToppGene Suite A one-stop portal for gene list enrichment analysis and candidate gene prioritization based on functional annotations and protein interactions network 
-[Added Jan 16, 2010; last site review Jan 16, 2010 by RWW.]
-
        
-
        
-WikiPathways (WGCNA) is an open, public platform dedicated to the curation of biological pathways by and for the scientific community. 
-[Added Nov 12, 2009; last site review Nov 12, 2009 by RWW.]
-
        
-
        
-Pathway Commons (WGCNA) is a search tool to find  and visualize public biological pathway information. This site collates from several major sites.
-[Added Nov 12, 2009; last site review Nov 12, 2009 by RWW.]
-
        
-
        
-Sanger Mouse Genome Project SNP Finder provides access to SNP and indels generated by sequencing 17 strains of mice (plus C57BL/6J). Marvelous.
-[Added Nov 18, 2009; last site review Nov 18, 2009 by RWW.]
-
        
-
        
-Weighted Gene Coexpression Network Analysis (WGCNA) is a collection of R functions to perform weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. See the recent paper on WGCNA. 
-[Added Aug 21, 2009; last site review Aug 21, 2009 by RWW.]
-
        
-
        
-GenomeNet is a terrific site for the analysis of molecular networks. Download the very effective KegArray 0.2.6beta package (May 2005) for exploratory data analysis of microarray data set. This package is as good as most commerical software and includes with built-in linkage to the KEGG databases. Versions are available for Mac OS X and Windows.   
-[Added Jan 3, 2005; last site review Aug 5, 2005 by RWW.]
-
        	
-
        
-Expression Profiler at http://ep.ebi.ac.uk/ is a set of tools for clustering, analysis and visualization of gene expression and other genomic data. Tools in the Expression Profiler allow you to perform cluster analysis, pattern discovery, pattern visualization, study and search Gene Ontology categories, generate sequence logos, extract regulatory sequences, study protein interactions, as well as to link analysis results to external tools and databases.
-[Added May 20, 2008; last site review May 20, 2008 by RWW.]
-
        	
-
        
-BioGRID: the Biological General Repository for Interaction Datasets is a freely accessible database of protein and genetic interactions from Mt. Sinai, Toronto. 
-[Added July 28, 2007; last site review July 28, 2007 by RWW.]
-
        	
-
        
-CisMols Analyzer at Cincinnati Children's Hospital (Aronow and colleagues) is a server and database for the analysis of cis element co-occurences in the promoters of a list of genes. The PolyDoms Analyzer is a tool for scanning through gene lists for those members of a pathway, ontolog, or disease that contain potentially harmful protein-coding SNPs. GenomeTraFaC is a comparative genomics-based resource for initial characterization of gene models and the identification of putative cis-regulatory regions of RefSeq Gene Orthologs.
-[Added Sept 23, 2006; last site review Sept 23, 2006 by RWW.]
-
        	
-
        
-PolymiRTS database that searches for microRNA (miRNA) targets in transcripts that overlap SNPs.  This database will also search for genes with associated phenotype variants that may have variants in miRNA target sequence (Yan Cui, Lei Bao  and colleagues). 
- [Added Sept 23, 2006; last site review Sept 23, 2006 by RWW.]
-
        	
-
        
-MSigDB: The Molecular Signature Database is part of the Broad Institute Gene Set Enrichment Analysis suite. MSigDB contains large numbers of static and partly annotated sets of genes/transcripts. Registration is not actually required to download data sets. 
- [Added Jan 18, 2007; last site review Jan 18, 2007 by RWW.]
-
        	
-
        
-C-INIA MAGIC-B microarray knowledgebase from the Department of Pharmacology, University of Colorado, Denver, part of the NIAAA INIA project. Extensive public and privated brain array data sets in a powerful analytic web environment. 
- [Added May 31, 2007 by RWW.]
-
        	
-
        
-GenMAPP 2.0 (2004), the Gene Map Annotation and Pathway Prolifer, is a free Windows application (simple registration required) with which you can visualize expression and other genomic data sets on maps of biological pathways. Very flexible suite of programs that you can also use to make custom gene annotation maps (and more).
-[Added Aug 5, 2005; last site review Aug 5, 2005 by RWW.]
-
        	
-
        
-BIND and STRING and IntAct are great sites that provide access to well curated data on protein-protein interactions. BIND and IntAct focus on experimentally verified interactions whereas STRING and preBIND incorporate inferred interaction based on other data types, including gene expression. Links to BIND and STRING have been added to the Trait Data and Analysis forms on the GeneNetwork BETA site.
-[Added Aug 21, 2005; last site reviews Aug 27, 2005 by RWW.]
-
        	
-
        
-Microarray Module Maps is a great site that databases a large number of coexpression modules defined using many cancer array studies.
-[Added Aug 26, 2005; last site review Aug 26, 2005 by RWW.]
-
        
-
        
-The Gene Ontology Consortium maintains a well annotated list of open resources for the analysis of large expression data sets and gene ontologies. Note that there are several different lists, each with valuable links.
-[Added July 15, 2005; last site review July 15, 2005 by RWW.]
-
        
-
        
-Prioritizer: Prioritizer is a stand-alone Java program that uses a functional human gene network, available at www.genenetwork.nl, to prioritize positional candidate genes that reside within susceptibility loci, by assuming that real disease genes, residing within different loci are functionally closely related within the gene network.
-
        	
-
        
-eQTL Explorer is a Java WebStart application that has also been designed for the calculation and display of QTL maps for large rat data sets, particuarly those generated using the HXB strains. Locations of QTLs for both mRNA traits and conventional physiological traits are displayed on chromosome ideograms. High precision QTL maps can also be generated. A password is required to gain access to the primary data files.
-[Added January 7, 2006; last site review Jan 7, 2006 by RWW.]
-
        	
-
        
-Clique-GO Analysis is a novel tool for extracting cliques of coregulated transcripts. The current data requires Affymetrix U74Av2 probe set IDs as input. Try "103370_at" (the gene Lin7c) as an example.
-[Added Jan 4, 2005; last site review Jan 4, 2005 by RWW.]
-
        	
-
        
-Gemma and ErmineJ are powerful resources for analysis and metaanalysis of gene expression data sets at UBC. Pavlidis and colleagues also provide updated GO data for common microarray platforms.  
-[Added Jan 4, 2005; last site review June 7, 2007 by RWW.]
-
        	
-
        
-Cytoscape is one of several SBML-compatible open source programs for visualizing molecular interaction networks and overlaying these networks with gene expression profiles and other data sets to generate and test specific hypotheses. 
-[Added Jan 5, 2005; last site review Jan 5, 2005 by RWW.]
-
        	
-
        
-Pathway Searcher provides fast access to gene/protein interaction pathways. An intuitive interface. 
-[Added Dec 30, 2004; last site review Dec 30, 2004 by RWW.]
-
        	
-
        
-Gene Sorter is a tool for generating and sorting sets of genes using a wide variety of information integrated into UCSC's Genome Brower. 
-[Added Dec 31, 2004; last site review Dec 31, 2004 by RWW.]
-
        	
-
        
-oPPOSUM is a tool for finding over-represented transcription factor binding sites in lists of mouse and human genes. It handles about 100 out of greater than 600 TFBSs. 
-[Added Jan 27, 2005; last site review Nov 21, 2005 by RWW.]
-
        	
-
        
-MOTIF and PAINT search for motifs in submitted sequences or lists of genes. Paint makes use of the TRANSFAC Pro database. 
-[Added Dec 22, 2004; last site review Dec 25, 2004 by RWW.]
-
        	
-
        
-Arrowsmith provides a fast way to evaluate known interactions or common mechanisms between two genes or proteins. It carries out a sophisticated comparison of the current PubMed database. 
-[Added Dec 22, 2004; last site review June 7, 2007 by RWW.]
-
        	
-
        
-Chilibot applies natural-language processing to the PubMed database to hunt for directed relationships among pairs or sets of genes, proteins, and keywords. [Added Dec 30, 2004; last site review Aug 13, 2009 by RWW.]
-
        	
-
        
-Mouse Imprinting Resources:
-The Harwell Mouse Imprinting Resource, 
-Duke University - Jirtle's Laboratory, 
-RIKEN Candidate Imprinted Transcript Maps, and
-Imprinted Gene Catalogue - University of Otago.
-[Added Oct 20, 2006; last site review Oct 20, 2006 by RWW.]
-
        	
-
        
-
-
        
-	
-
        Resources for the Analysis of Phenotypes in Genetic Reference Populations
        
-
        
-
        
-MBL is a extensive image database of brain sections from genetic reference populations of mice, including the BXD, AXB, CXB, BXH strains included in WebQTL. The MBL is a companion database of WebQTL. 
-[Added Dec 22, 2004; last site review Aug 6, 2005 by RWW.]
-
        	 
-
        
-MPAD Mouse Phenome Association Database v 1.0, by Eleazar Eskin and Hyun Min Kang. This resource performs genome-wide association mapping. Phenotype data sets are derived from the Mouse Phenome Project set of standard mouse strains. The permutation procedures account for the genetic relations among these strains and provide much more appropriate genome-wide significance thresholds than previous mouse association mapping methods.
-[Added Nov 19, 2006; last site review Nov 19, 2006 by RWW. Link broken June 2007 probably due to move from UCSD to UCLA; check with EE.]
-
        	 
-
        
-GScan at the Wellcome Trust, Oxford, is a sophisticated viewer and analysis tool with which to explore the genetic control of diverse phenotypes (including array data) generated using heterogeneous stock mice (Flint, Mott, and colleagues).
-[Added May 28, 2007 by RWW.]
-
        	
-
        
-Phenome Project provides access to a wide variety of phenotype data many common and wild inbred strains of mice. 
-[Added Dec 22, 2004; last site review Dec 25, 2004 by RWW.]
-
        	
-
        	
-
-
        
-
-
        QTL Mapping Resources
        
-
        
-
        
-QSB: QSB is a stand-alone JAVA program with a sophisticated GUI developed for genetical genomics or systems genetics, an emerging field that combines quantitative genetics and genomics. QSB stands for QTL mapping, Sequence polymorphism analysis (or SNP analysis) and Bayesian network analysis. QSB takes marker and array data from a segregating population as input and identifies significant QTLs and then evaluated networks of candidate genes associated with these QTLs. 
-[Added July 29, 2005; last site review Jan 7, 2006 by RWW.]
-
        	
-
        
-Positional Medline (PosMed) is a knowledge-based ranking system of candidate genes within QTL intervals for human, mouse, rat, Arabidopsis, and rice. 
-[Added Nov 4, 2009; last site review Nov 4, 2009 by RWW.]
-
        
-
        Genome - Phenome Superbrain Project  integrates various databases to build a comprehensive computerized encyclopedia of omic sciences in several species, including mouse, rat, human, and arabidopsis, etc. The goal is to evolve this intelligent system into a form of artificial intelligence that can solve a researcher's problems by exploiting a vast amount of information accumulated in documents and published data ranging from genomes to phenomes. [Added Sept 13, 2007; last site review Sept12, 2007 by RWW.]
-
        	
-
        
-QTL Reaper  is software, written in C and compiled as a Python module, for rapidly scanning microarray expression data for QTLs. It is essentially the batch-oriented version of WebQTL. It requires, as input, expression data from members of a set of recombinant inbred lines and genotype information for the same lines. It searches for an association between each expression trait and all genotypes and evaluates that association by a permutation test. For the permutation test, it performs only as many permutations as are necessary to define the empirical P-value to a reasonable precision. It also performs bootstrap resampling to estimate the confidence region for the location of a putative QTL.
-[Added Jan 27, 2005; last site review Jan 27, 2005 by KFM.]
-
        	
-
        
-QTLNetwork 2.0 is a software package ofr mapping QTLs with epistatic and GXE interaction effects in experimental populations including double-haploid, recombinant inbred, backcross, F2, IF2 and BxFy populations. The program provides graphical presentations of QTL mapping results. The software is programmed by C++ programming language under Microsoft Visual C++ 6.0 environment. It works with Microsoft Windows operating systems, including Windows 95/98, NT, 2000, XP, 2003server. A new version of QTLNetwork is under developing, and its functions will be extended to include linkage group construction and marker-assisted virtual breeding.[Added June 21, 2007.]
-
        	 
-
        
-MouseHapMap project genotypes from Mark Daly and colleagues. Approximately 140,000 SNPs across 49 strains. Updated Feb 2006.used to explore the Oxford Wellcome Heterogeneous stock QTL mapping project population. It currently includes mapping data for 100+ phenotypes typed across 2000 animals and 13,000 SNPs.
-[Added May 10, 2006; last site review May 10, 2006 by RWW.]
-
        	
-
        
-A valuable list of Software for QTL Data Analysis  and Gene Expression Microarray Software is managed by Brian Yandell at University of Wisconsin.
-[Added May 16, 2006; last site review May 16, 2006 by RWW.]
-
        	
-
        
-GSCAN DB is a browser used to explore the Oxford Wellcome Heterogeneous stock QTL mapping project population. It currently includes mapping data for 100+ phenotypes typed across 2000 animals and 13,000 SNPs.
-[Added May 10, 2006; last site review May 10, 2006 by RWW.]
-
        	
-
        
-The Wellcome Trust-CTC SNP Data Set consists of high density SNP data for approximately 490 strains of mice at 13,377 SNPs. These data fiels were processed slightly to generate many of the mouse mapping files used in WebQTL.
-[Added Sept 27, 2005; last site review Sept 27, 2005 by RWW.]
-
        	
-
        
-The NIEHS-Perlegen Mouse Strain Resequencing Project provides links to SNP data for up to 15 strains of mice. Very high density data for many chromosomes. These data are integrated to some extent in the GeneNetwork.
-[Added Sept 25, 2005; last site review Sept 25, 2005 by RWW.]
-
        	
-
        
-
-
        
-
-
        Affymetrix Array Annotation Resources
        
-
        
-
        
-Affy MOE430A and MOE430B Annotation files are explained more clearly that Affymetrix has ever done by Earl F Glynn  at the Stowers Institute. (efg@stowers-insitute.org). 
-[Added July 17, 2006; last site review June 7, 2007 by RWW. This Oct 7, 2005 file caused Grace Wheeler's Mac internet connection to break.]
-
        	
-
        
-
-
        
-
-
        Information about this HTML page: 
        
-
        
-
        This text originally generated by RWW, Dec 21, 2004. Updated by EJC, Feb 27, 2005; by RWW, July 15, Sept 25.
-
        Management of GeneNetwork access and trait pages.
-
        
-
        
-		
-		
        
-
-{% endblock %}
-- 
cgit v1.2.3


From 76ef7a0479473c0716e7178c524f4a3ae02df16c Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 14:45:49 +0300
Subject: remove static views form wqflask/view.py

---
 wqflask/wqflask/views.py | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py
index 060c5a7b..cc2dc341 100644
--- a/wqflask/wqflask/views.py
+++ b/wqflask/wqflask/views.py
@@ -307,17 +307,7 @@ def intro():
     doc = Docs("intro", request.args)
     return render_template("docs.html", **doc.__dict__)
 
-@app.route("/policies")
-def policies():
-    doc = Docs("policies", request.args)
-    #return render_template("policies.html")
-    return render_template("docs.html", **doc.__dict__)
 
-@app.route("/links")
-def links():
-    #doc = Docs("links", request.args)
-    #return render_template("docs.html", **doc.__dict__)
-    return render_template("links.html")
 
 @app.route("/tutorials")
 def tutorials():
-- 
cgit v1.2.3


From 7a669eb9b9674ebffba8917f42833c71bfe36255 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 15:01:42 +0300
Subject: add css for container

---
 wqflask/wqflask/static/new/css/markdown.css | 72 ++++++++++++++++++-----------
 1 file changed, 46 insertions(+), 26 deletions(-)

diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
index d8dd4776..ad665835 100644
--- a/wqflask/wqflask/static/new/css/markdown.css
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -2,7 +2,10 @@
     padding: 20px;
 }
 
-#markdown h2, #markdown h3, #markdown h4, #markdown h5 {
+#markdown h2,
+#markdown h3,
+#markdown h4,
+#markdown h5 {
     font-weight: bold;
 }
 
@@ -11,36 +14,53 @@
     margin-right: auto;
     margin-left: auto;
 }
-#markdown {
-	background: red;
+
+
+
+.github-btn {
+    margin: 20px 10px;
+    display: flex;
+    width: 90vw;
+    justify-content: flex-end;
 }
 
+.github-btn a {
+    display: inline-block;
+    align-self: center;
+    text-decoration: none;
+    transition-property: all;
+    transition-duration: 0.5s;
+    padding: 8px 12px;
+    color: #fff;
+    /*background: #27407a;*/
+    background: blue;
+    border-radius: 20px;
+   
+    /*text-align: center;*/
+
 
+}
 
-	.github-btn{
-		margin:20px 10px;
-		display: flex;
-		width: 90vw;
-		justify-content: flex-end;
-	}
-	.github-btn a{
-		display: inline-block;
-		align-self:center;
-		text-decoration: none;
-		transition-property:all;
-		transition-duration:0.5s;
-		padding:8px 12px;
-		color:#fff;
-		/*background: #27407a;*/
-		background: blue;
-		border-radius: 20px;
-		background: red;
-		/*text-align: center;*/
+.github-btn a:hover {
 
+    background: #333;
 
-	}
-	.github-btn a:hover{
+}
 
-		background: #333;
 
-	}
+.container {
+width:80vw;
+margin: auto;
+max-width:80vw; 
+  
+}
+
+.container p {
+    font-size: 17px;
+     word-spacing: 0.2em;
+}
+@media(max-width:650px){
+    .container{
+        width: 100vw;
+    }
+}
\ No newline at end of file
-- 
cgit v1.2.3


From 419532807eff4cd28eb011a777a3a28b1c99d90d Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 15:03:44 +0300
Subject: build url for functions

---
 wqflask/wqflask/templates/base.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/templates/base.html b/wqflask/wqflask/templates/base.html
index ca6c9d9b..fefb519c 100644
--- a/wqflask/wqflask/templates/base.html
+++ b/wqflask/wqflask/templates/base.html
@@ -68,8 +68,8 @@
                               
      • Tutorials/Primers
        • 
                               
      • Glossary of Term
        • 
                               
      • FAQ
        • 
-                              
      • Policies
        • 
-                              
      • Links
        • 
+                              
      • Policies
        • 
+                              
      • Links
        • 
                               
      • Environments
        • 
                               
      • GN1 News
        • 
                             
      
-- 
cgit v1.2.3


From 828cb4d0b327bcb441dc523f4d825f660f371e01 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 15:04:30 +0300
Subject: add policies page

---
 wqflask/wqflask/templates/policies.html | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/wqflask/wqflask/templates/policies.html b/wqflask/wqflask/templates/policies.html
index 83b6b3c0..ef481de2 100644
--- a/wqflask/wqflask/templates/policies.html
+++ b/wqflask/wqflask/templates/policies.html
@@ -1,18 +1,21 @@
 {% extends "base.html" %}
-{% block title %}Policies{% endblock %}
+
+{% block title %}Links{% endblock %}
+
+{% block css %}
+
+{% endblock %}
+
 {% block content %}
 
-
-
-
-
      
-	
      WebQTL Conditions of Use
      
-	
-	
      
-
      
+ 
      
+ Edit on Github	
+ 
      
+
+
      
+   {{ rendered_markdown|safe }}
+
+
      
 
+
 {% endblock %}
\ No newline at end of file
-- 
cgit v1.2.3


From 6ae545167f59b40ecd0cee2bebb1276b34fa7c5b Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 15:06:05 +0300
Subject: register view,policies blueprint

---
 wqflask/wqflask/__init__.py               |  4 ++++
 wqflask/wqflask/templates/references.html | 25 ++++++++++++++++++-------
 2 files changed, 22 insertions(+), 7 deletions(-)

diff --git a/wqflask/wqflask/__init__.py b/wqflask/wqflask/__init__.py
index 39a36fa3..d7da3755 100644
--- a/wqflask/wqflask/__init__.py
+++ b/wqflask/wqflask/__init__.py
@@ -8,6 +8,8 @@ from flask import Flask
 from utility import formatting
 from wqflask.markdown_routes import glossary_blueprint
 from wqflask.markdown_routes import  references_blueprint
+from wqflask.markdown_routes import  links_blueprint
+from wqflask.markdown_routes import policies_blueprint
 
 app = Flask(__name__)
 
@@ -21,6 +23,8 @@ app.jinja_env.globals.update(
 # Registering blueprints
 app.register_blueprint(glossary_blueprint, url_prefix="/glossary")
 app.register_blueprint(references_blueprint,url_prefix="/references")
+app.register_blueprint(links_blueprint,url_prefix="/links")
+app.register_blueprint(policies_blueprint,url_prefix="/policies")
 
 @app.before_request
 def before_request():
diff --git a/wqflask/wqflask/templates/references.html b/wqflask/wqflask/templates/references.html
index 5e86d95b..c10576d8 100644
--- a/wqflask/wqflask/templates/references.html
+++ b/wqflask/wqflask/templates/references.html
@@ -3,13 +3,13 @@
 {% block title %}Reference{% endblock %}
 
 {% block css %}
-
+
 {% endblock %}
 
 {% block content %}
 
  
      
- Edit on Github	
+ Edit on Github	
  
      
 
 
      
@@ -18,12 +18,23 @@
 
      
 
 
 
-- 
cgit v1.2.3


From 2a346d94a978c8a7faf0d1435220816efc3c2c64 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 15:07:42 +0300
Subject: add edit button in glossary page

---
 wqflask/wqflask/templates/glossary.html | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/templates/glossary.html b/wqflask/wqflask/templates/glossary.html
index 146c7e86..395bcfea 100644
--- a/wqflask/wqflask/templates/glossary.html
+++ b/wqflask/wqflask/templates/glossary.html
@@ -8,9 +8,11 @@
 
 {% block content %}
 
+ 
      
+ Edit on Github	
+ 
      
 
      
-    
-        [Edit on Github]
+	
     {{ rendered_markdown|safe }}
 
      
 {% endblock %}
-- 
cgit v1.2.3


From 416cb0cd272001651a3d9eb43500067ef524819d Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 15:09:37 +0300
Subject: add endpoint for polices,environments and policies

---
 wqflask/wqflask/markdown_routes.py | 51 ++++++++++++++++++++++----------------
 1 file changed, 29 insertions(+), 22 deletions(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 60d4374c..5170fcf0 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -9,23 +9,31 @@ from flask import Blueprint
 from flask import render_template
 
 glossary_blueprint = Blueprint('glossary_blueprint', __name__)
-references_blueprint=Blueprint("references_blueprint",__name__)
-environments_blueprint=Blueprint("environments_blueprint",__name__)
-links_blueprint=Blueprint("links_blueprint",__name__)
-policies_blueprint=Blueprint("policies_blueprint",__name__)
+references_blueprint = Blueprint("references_blueprint", __name__)
+environments_blueprint = Blueprint("environments_blueprint", __name__)
+links_blueprint = Blueprint("links_blueprint", __name__)
+policies_blueprint = Blueprint("policies_blueprint", __name__)
 
-def render_markdown(file_name):
+#for debug
+github_url = ("https://raw.githubusercontent.com/"
+              "genenetwork/gn-docs/master/general/glossary/glossary.md")
+
+
+
+def render_markdown(file_name,github_url="https://raw.githubusercontent.com/Alexanderlacuna/gn-docs/feature/add-markdown-pages/"):
+ 
+    md_content=requests.get(f"{github_url}{file_name}")
     """Try to fetch the file name from Github and if that fails, try to
 look for it inside the file system
-
+po
     """
-    github_url = ("https://raw.githubusercontent.com/"
-                  "genenetwork/gn-docs/master/")
-    md_content = requests.get(f"{github_url}{file_name}")
+    # github_url = ("https://raw.githubusercontent.com/"
+    #               "genenetwork/gn-docs/master/")
+    # md_content = requests.get(f"{github_url}{file_name}")
     if md_content.status_code == 200:
+        # print(md_content.content.decode("utf-8"))
         return markdown.Markdown().convert(md_content.content.decode("utf-8"))
 
-
     # TODO: Add fallback on our git server by checking the mirror.
 
     # Content not available
@@ -36,8 +44,6 @@ look for it inside the file system
             "Please reach out to the gn2 team to have a look at this")
 
 
-
-
 @glossary_blueprint.route('/')
 def glossary():
     return render_template(
@@ -48,23 +54,24 @@ def glossary():
 @references_blueprint.route('/')
 def references():
     return render_template(
-        "reference.html",
-        rendered_markdown=render_markdown("general/glossary/glossary.md")), 200
-
+        "references.html",
+        rendered_markdown=render_markdown("general/references/references.md")), 200
 
 
 @environments_blueprint.route("/")
-def references():
-    return render_template("environments.html",rendered_markdown=render_markdown("general/environments/environments.md"))
+def environments():
+    return render_template("environments.html", rendered_markdown=render_markdown("general/links/links.md"))
 
 
 @links_blueprint.route("/")
 def links():
-    return rendered_template("links.html",rendered_markdown=render_markdown("general/links/links.md"))
+    return render_template(
+        "links.html",
+        rendered_markdown=render_markdown("general/links/links.md")), 200
 
 
-policies_blueprint.route("/")
+@policies_blueprint.route("/")
 def policies():
-    return rendered_template("policies.html",rendered_markdown=rendered_markdown("general/policies/policies.md"))
-
-
+    return render_template(
+        "links.html",
+        rendered_markdown=render_markdown("general/policies/policies.md")), 200
-- 
cgit v1.2.3


From 52eea81494a507eb69751dea54f98270b53d9844 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 15:10:46 +0300
Subject: add links page

---
 wqflask/wqflask/templates/links.html | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 wqflask/wqflask/templates/links.html

diff --git a/wqflask/wqflask/templates/links.html b/wqflask/wqflask/templates/links.html
new file mode 100644
index 00000000..7b092839
--- /dev/null
+++ b/wqflask/wqflask/templates/links.html
@@ -0,0 +1,21 @@
+{% extends "base.html" %}
+
+{% block title %}Links{% endblock %}
+
+{% block css %}
+
+{% endblock %}
+
+{% block content %}
+
+ 
      
+ Edit on Github	
+ 
      
+
+
      
+   {{ rendered_markdown|safe }}
+
+
      
+
+
+{% endblock %}
\ No newline at end of file
-- 
cgit v1.2.3


From c58751a8aa7ab0277096d91bb3e50c3ef0d62aab Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 15:18:04 +0300
Subject: add re-used css to markdown.css for easy maintenance and reusability

---
 wqflask/wqflask/templates/references.html | 20 --------------------
 1 file changed, 20 deletions(-)

diff --git a/wqflask/wqflask/templates/references.html b/wqflask/wqflask/templates/references.html
index c10576d8..e05c8440 100644
--- a/wqflask/wqflask/templates/references.html
+++ b/wqflask/wqflask/templates/references.html
@@ -17,25 +17,5 @@
 
 
    
 
-
 
 {% endblock %}
\ No newline at end of file
-- 
cgit v1.2.3


From 8a0e79d3186e340bc438dd07b4c2992f81c3e6d7 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 17:09:43 +0300
Subject: add environment markdown

---
 wqflask/wqflask/templates/environment.html | 37 ++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)
 create mode 100644 wqflask/wqflask/templates/environment.html

diff --git a/wqflask/wqflask/templates/environment.html b/wqflask/wqflask/templates/environment.html
new file mode 100644
index 00000000..a331d494
--- /dev/null
+++ b/wqflask/wqflask/templates/environment.html
@@ -0,0 +1,37 @@
+{% extends "base.html" %}
+
+{% block title %}Glossary{% endblock %}
+
+{% block css %}
+
+{% endblock %}
+
+{% block content %}
+
+ 
    
+ Edit on Github	
+ 
    
+
    
+	
+   
    {{ rendered_markdown|safe }} 
    
+
    
+
+
+{% endblock %}
-- 
cgit v1.2.3


From b0c1d59d4e5a9580b4d15664a53845969db32d0a Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 17:11:55 +0300
Subject: remove environment from /wqflask/views.py

---
 wqflask/wqflask/templates/links.html | 2 +-
 wqflask/wqflask/views.py             | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/wqflask/wqflask/templates/links.html b/wqflask/wqflask/templates/links.html
index 7b092839..ef481de2 100644
--- a/wqflask/wqflask/templates/links.html
+++ b/wqflask/wqflask/templates/links.html
@@ -9,7 +9,7 @@
 {% block content %}
 
  
    
- Edit on Github	
+ Edit on Github	
  
    
 
 
    
diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py
index cc2dc341..6cdce248 100644
--- a/wqflask/wqflask/views.py
+++ b/wqflask/wqflask/views.py
@@ -321,10 +321,10 @@ def credits():
     #return render_template("docs.html", **doc.__dict__)
     return render_template("credits.html")
 
-@app.route("/environments")
-def environments():
-    doc = Docs("environments", request.args)
-    return render_template("docs.html", **doc.__dict__)
+# @app.route("/environments")
+# def environments():
+#     doc = Docs("environments", request.args)
+#     return render_template("docs.html", **doc.__dict__)
     #return render_template("environments.html", **doc.__dict__)
 
 @app.route("/update_text", methods=('POST',))
-- 
cgit v1.2.3


From d8fb319272a4833ed3045cdc5ed46e0e204f78e1 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 17:13:24 +0300
Subject: add and register environments blueprint

---
 wqflask/wqflask/__init__.py               | 2 ++
 wqflask/wqflask/templates/base.html       | 2 +-
 wqflask/wqflask/templates/references.html | 2 +-
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/__init__.py b/wqflask/wqflask/__init__.py
index d7da3755..f44b4bfd 100644
--- a/wqflask/wqflask/__init__.py
+++ b/wqflask/wqflask/__init__.py
@@ -10,6 +10,7 @@ from wqflask.markdown_routes import glossary_blueprint
 from wqflask.markdown_routes import  references_blueprint
 from wqflask.markdown_routes import  links_blueprint
 from wqflask.markdown_routes import policies_blueprint
+from wqflask.markdown_routes import  environments_blueprint
 
 app = Flask(__name__)
 
@@ -25,6 +26,7 @@ app.register_blueprint(glossary_blueprint, url_prefix="/glossary")
 app.register_blueprint(references_blueprint,url_prefix="/references")
 app.register_blueprint(links_blueprint,url_prefix="/links")
 app.register_blueprint(policies_blueprint,url_prefix="/policies")
+app.register_blueprint(environments_blueprint,url_prefix="/environments")
 
 @app.before_request
 def before_request():
diff --git a/wqflask/wqflask/templates/base.html b/wqflask/wqflask/templates/base.html
index fefb519c..8ba5d0d5 100644
--- a/wqflask/wqflask/templates/base.html
+++ b/wqflask/wqflask/templates/base.html
@@ -70,7 +70,7 @@
                               
  • FAQ
    • 
                               
  • Policies
    • 
                               
  • Links
    • 
-                              
  • Environments
    • 
+                              
  • Environments
    • 
                               
  • GN1 News
    • 
                             
                         
diff --git a/wqflask/wqflask/templates/references.html b/wqflask/wqflask/templates/references.html
index e05c8440..e2f152e3 100644
--- a/wqflask/wqflask/templates/references.html
+++ b/wqflask/wqflask/templates/references.html
@@ -9,7 +9,7 @@
 {% block content %}
 
  
    
- Edit on Github	
+ Edit on Github	
  
    
 
 
    
-- 
cgit v1.2.3


From 66f4ab80a5dbfa8634c3a5df91a59a9119f57b2c Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 17:18:35 +0300
Subject: add table extension to support conversion of markdown tables to HTML

---
 wqflask/wqflask/markdown_routes.py | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 5170fcf0..073370c7 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -20,7 +20,24 @@ github_url = ("https://raw.githubusercontent.com/"
 
 
 
+
+def render_markdown_table(file_name,github_url="https://raw.githubusercontent.com/Alexanderlacuna/gn-docs/feature/add-markdown-pages/"):
+
+    md_content=md_content=requests.get(f"{github_url}{file_name}")
+    if md_content.status_code==200:
+        return markdown.markdown(md_content.content.decode("utf-8"), extensions=['tables'])
+    return (f"\nContent for {file_name} not available. "
+            "Please check "
+            "(here to see where content exists)"
+            "[https://github.com/genenetwork/gn-docs]. "
+            "Please reach out to the gn2 team to have a look at this")
+
+
+
+
 def render_markdown(file_name,github_url="https://raw.githubusercontent.com/Alexanderlacuna/gn-docs/feature/add-markdown-pages/"):
+    github_url = ("https://raw.githubusercontent.com/"
+                  "genenetwork/gn-docs/master/")
  
     md_content=requests.get(f"{github_url}{file_name}")
     """Try to fetch the file name from Github and if that fails, try to
@@ -60,7 +77,7 @@ def references():
 
 @environments_blueprint.route("/")
 def environments():
-    return render_template("environments.html", rendered_markdown=render_markdown("general/links/links.md"))
+    return render_template("environment.html", rendered_markdown=render_markdown_table("general/environments/environments.md")),200
 
 
 @links_blueprint.route("/")
-- 
cgit v1.2.3


From 41988f8e92ea5a273e22a7176481cf2f4bd88b5b Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 17:22:44 +0300
Subject: delete environments html pagE

---
 wqflask/wqflask/templates/environments.html | 83 -----------------------------
 1 file changed, 83 deletions(-)
 delete mode 100644 wqflask/wqflask/templates/environments.html

diff --git a/wqflask/wqflask/templates/environments.html b/wqflask/wqflask/templates/environments.html
deleted file mode 100644
index 5ef91a95..00000000
--- a/wqflask/wqflask/templates/environments.html
+++ /dev/null
@@ -1,83 +0,0 @@
-{% extends "base.html" %}
-
-{% block title %}Environments{% endblock %}
-
-{% block content %}
-
    
-	
    System Properties
    
-	
-		
-			
-			
-				
-				
-				
-				
-			
-			
-			
-				
-				
-				
-				
-			
-			
-			
-				
-				
-				
-				
-			
-			
-				
-				
-				
-				
-			
-			
-				
-				
-				
-				
-			
-			
-				
-				
-				
-				
-			
-			
-				
-				
-				
-				
-			
-			
-			
-				
-				
-				
-				
-			
-			
-				
-				
-				
-				
-			
-			
-				
-				
-				
-				
-			
-			
-				
-				
-				
-				
-			
-		
-	
    Operating System
    Ubuntu12.04.5 LTS 64-bithttp://www.ubuntu.com/Free software licenses (mainly GPL)
    Database
    MySQL5.5.40http://www.mysql.com/GPL (version 2) or proprietary
    Python
    Python2.7.3http://www.python.org/Python Software Foundation License
    Flask0.9http://flask.pocoo.org/BSD
    Jinja22.6http://jinja.pocoo.org/BSD
    NumPy1.7.0http://www.numpy.org/BSD-new
    SciPy0.11.0http://www.scipy.org/BSD-new
    JavaScript / CSS
    jQuery1.10.2http://jquery.com/MIT
    Bootstrap2.3.1http://getbootstrap.com/MIT License (Apache License 2.0 prior to 3.1.0)
    DataTables1.9.4http://www.datatables.net/MIT
    CKEditor4.4.5http://ckeditor.com/GPL, LGPL and MPL
    
-
    
-{% endblock %}
-- 
cgit v1.2.3


From da73052c84d5566bf3d221d90f93688db125181d Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 18:13:00 +0300
Subject: add facilities page

---
 wqflask/wqflask/markdown_routes.py        | 10 ++++++++--
 wqflask/wqflask/templates/facilities.html | 21 +++++++++++++++++++++
 2 files changed, 29 insertions(+), 2 deletions(-)
 create mode 100644 wqflask/wqflask/templates/facilities.html

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 073370c7..7765af36 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -13,6 +13,7 @@ references_blueprint = Blueprint("references_blueprint", __name__)
 environments_blueprint = Blueprint("environments_blueprint", __name__)
 links_blueprint = Blueprint("links_blueprint", __name__)
 policies_blueprint = Blueprint("policies_blueprint", __name__)
+facilities_blueprint=Blueprint("facilities",__name__)
 
 #for debug
 github_url = ("https://raw.githubusercontent.com/"
@@ -36,8 +37,8 @@ def render_markdown_table(file_name,github_url="https://raw.githubusercontent.co
 
 
 def render_markdown(file_name,github_url="https://raw.githubusercontent.com/Alexanderlacuna/gn-docs/feature/add-markdown-pages/"):
-    github_url = ("https://raw.githubusercontent.com/"
-                  "genenetwork/gn-docs/master/")
+    # github_url = ("https://raw.githubusercontent.com/"
+    #               "genenetwork/gn-docs/master/")
  
     md_content=requests.get(f"{github_url}{file_name}")
     """Try to fetch the file name from Github and if that fails, try to
@@ -92,3 +93,8 @@ def policies():
     return render_template(
         "links.html",
         rendered_markdown=render_markdown("general/policies/policies.md")), 200
+
+
+@facilities_blueprint.route("/")
+def facilities():
+    return render_template("facilities",rendered_markdown=render_markdown("general/policies/policies.md")), 200
\ No newline at end of file
diff --git a/wqflask/wqflask/templates/facilities.html b/wqflask/wqflask/templates/facilities.html
new file mode 100644
index 00000000..5be0c96d
--- /dev/null
+++ b/wqflask/wqflask/templates/facilities.html
@@ -0,0 +1,21 @@
+{% extends "base.html" %}
+
+{% block title %}Facilities{% endblock %}
+
+{% block css %}
+
+{% endblock %}
+
+{% block content %}
+
+ 
    
+ Edit on Github	
+ 
    
+
+
    
+   {{ rendered_markdown|safe }}
+
+
    
+
+
+{% endblock %}
\ No newline at end of file
-- 
cgit v1.2.3


From 6ab58b529aca9c12bb26865a1d81a15aaad1fd5d Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 18:14:03 +0300
Subject: add and register facilities blueprint

---
 wqflask/wqflask/__init__.py         | 2 ++
 wqflask/wqflask/markdown_routes.py  | 8 ++++----
 wqflask/wqflask/templates/base.html | 1 +
 3 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/wqflask/wqflask/__init__.py b/wqflask/wqflask/__init__.py
index f44b4bfd..0b69ec40 100644
--- a/wqflask/wqflask/__init__.py
+++ b/wqflask/wqflask/__init__.py
@@ -11,6 +11,7 @@ from wqflask.markdown_routes import  references_blueprint
 from wqflask.markdown_routes import  links_blueprint
 from wqflask.markdown_routes import policies_blueprint
 from wqflask.markdown_routes import  environments_blueprint
+from wqflask.markdown_routes import  facilities_blueprint
 
 app = Flask(__name__)
 
@@ -27,6 +28,7 @@ app.register_blueprint(references_blueprint,url_prefix="/references")
 app.register_blueprint(links_blueprint,url_prefix="/links")
 app.register_blueprint(policies_blueprint,url_prefix="/policies")
 app.register_blueprint(environments_blueprint,url_prefix="/environments")
+app.register_blueprint(facilities_blueprint,url_prefix="/facilities")
 
 @app.before_request
 def before_request():
diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 7765af36..d00628b9 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -13,7 +13,7 @@ references_blueprint = Blueprint("references_blueprint", __name__)
 environments_blueprint = Blueprint("environments_blueprint", __name__)
 links_blueprint = Blueprint("links_blueprint", __name__)
 policies_blueprint = Blueprint("policies_blueprint", __name__)
-facilities_blueprint=Blueprint("facilities",__name__)
+facilities_blueprint=Blueprint("facilities_blueprint",__name__)
 
 #for debug
 github_url = ("https://raw.githubusercontent.com/"
@@ -37,8 +37,8 @@ def render_markdown_table(file_name,github_url="https://raw.githubusercontent.co
 
 
 def render_markdown(file_name,github_url="https://raw.githubusercontent.com/Alexanderlacuna/gn-docs/feature/add-markdown-pages/"):
-    # github_url = ("https://raw.githubusercontent.com/"
-    #               "genenetwork/gn-docs/master/")
+    github_url = ("https://raw.githubusercontent.com/"
+                  "genenetwork/gn-docs/master/")
  
     md_content=requests.get(f"{github_url}{file_name}")
     """Try to fetch the file name from Github and if that fails, try to
@@ -97,4 +97,4 @@ def policies():
 
 @facilities_blueprint.route("/")
 def facilities():
-    return render_template("facilities",rendered_markdown=render_markdown("general/policies/policies.md")), 200
\ No newline at end of file
+    return render_template("facilities.html",rendered_markdown=render_markdown("general/help/facilities.md")), 200
\ No newline at end of file
diff --git a/wqflask/wqflask/templates/base.html b/wqflask/wqflask/templates/base.html
index 8ba5d0d5..faa59deb 100644
--- a/wqflask/wqflask/templates/base.html
+++ b/wqflask/wqflask/templates/base.html
@@ -70,6 +70,7 @@
                               
  • FAQ
    • 
                               
  • Policies
    • 
                               
  • Links
    • 
+                              
  • Facilities
    • 
                               
  • Environments
    • 
                               
  • GN1 News
    • 
                             
-- 
cgit v1.2.3


From 4f449007d7f4afe4b141851772fa694ced4e77e3 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 21:28:37 +0300
Subject: refactor render_markdown function in wqflask/markdown_routes.py

---
 wqflask/wqflask/markdown_routes.py          | 45 +++++++----------------------
 wqflask/wqflask/static/new/css/markdown.css | 17 ++++++-----
 2 files changed, 19 insertions(+), 43 deletions(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index d00628b9..0445ded1 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -13,47 +13,22 @@ references_blueprint = Blueprint("references_blueprint", __name__)
 environments_blueprint = Blueprint("environments_blueprint", __name__)
 links_blueprint = Blueprint("links_blueprint", __name__)
 policies_blueprint = Blueprint("policies_blueprint", __name__)
-facilities_blueprint=Blueprint("facilities_blueprint",__name__)
+facilities_blueprint = Blueprint("facilities_blueprint", __name__)
 
-#for debug
-github_url = ("https://raw.githubusercontent.com/"
-              "genenetwork/gn-docs/master/general/glossary/glossary.md")
+# for debug
+# https://raw.githubusercontent.com/Alexanderlacuna/gn-docs/feature/add-markdown-pages/
 
 
-
-
-def render_markdown_table(file_name,github_url="https://raw.githubusercontent.com/Alexanderlacuna/gn-docs/feature/add-markdown-pages/"):
-
-    md_content=md_content=requests.get(f"{github_url}{file_name}")
-    if md_content.status_code==200:
-        return markdown.markdown(md_content.content.decode("utf-8"), extensions=['tables'])
-    return (f"\nContent for {file_name} not available. "
-            "Please check "
-            "(here to see where content exists)"
-            "[https://github.com/genenetwork/gn-docs]. "
-            "Please reach out to the gn2 team to have a look at this")
-
-
-
-
-def render_markdown(file_name,github_url="https://raw.githubusercontent.com/Alexanderlacuna/gn-docs/feature/add-markdown-pages/"):
+def render_markdown(file_name):
     github_url = ("https://raw.githubusercontent.com/"
                   "genenetwork/gn-docs/master/")
- 
-    md_content=requests.get(f"{github_url}{file_name}")
-    """Try to fetch the file name from Github and if that fails, try to
-look for it inside the file system
-po
-    """
-    # github_url = ("https://raw.githubusercontent.com/"
-    #               "genenetwork/gn-docs/master/")
-    # md_content = requests.get(f"{github_url}{file_name}")
+
+    md_content = requests.get(f"{github_url}{file_name}")
     if md_content.status_code == 200:
-        # print(md_content.content.decode("utf-8"))
-        return markdown.Markdown().convert(md_content.content.decode("utf-8"))
 
+        return markdown.markdown(md_content.content.decode("utf-8"), extensions=['tables'])
+        # return markdown.Markdown().convert(md_content.content.decode("utf-8"))
     # TODO: Add fallback on our git server by checking the mirror.
-
     # Content not available
     return (f"\nContent for {file_name} not available. "
             "Please check "
@@ -78,7 +53,7 @@ def references():
 
 @environments_blueprint.route("/")
 def environments():
-    return render_template("environment.html", rendered_markdown=render_markdown_table("general/environments/environments.md")),200
+    return render_template("environment.html", rendered_markdown=render_markdown_table("general/environments/environments.md")), 200
 
 
 @links_blueprint.route("/")
@@ -97,4 +72,4 @@ def policies():
 
 @facilities_blueprint.route("/")
 def facilities():
-    return render_template("facilities.html",rendered_markdown=render_markdown("general/help/facilities.md")), 200
\ No newline at end of file
+    return render_template("facilities.html", rendered_markdown=render_markdown("general/help/facilities.md")), 200
diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
index ad665835..6d76c190 100644
--- a/wqflask/wqflask/static/new/css/markdown.css
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -35,7 +35,7 @@
     /*background: #27407a;*/
     background: blue;
     border-radius: 20px;
-   
+
     /*text-align: center;*/
 
 
@@ -49,18 +49,19 @@
 
 
 .container {
-width:80vw;
-margin: auto;
-max-width:80vw; 
-  
+    width: 80vw;
+    margin: auto;
+    max-width: 80vw;
+
 }
 
 .container p {
     font-size: 17px;
-     word-spacing: 0.2em;
+    word-spacing: 0.2em;
 }
-@media(max-width:650px){
-    .container{
+
+@media(max-width:650px) {
+    .container {
         width: 100vw;
     }
 }
\ No newline at end of file
-- 
cgit v1.2.3


From 001b10077517b24e0eecc16819c77f0eb3c31dc1 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 21:33:39 +0300
Subject: PEP8 format and remove unnecessary comments

---
 wqflask/wqflask/markdown_routes.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 0445ded1..093dffeb 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -15,10 +15,6 @@ links_blueprint = Blueprint("links_blueprint", __name__)
 policies_blueprint = Blueprint("policies_blueprint", __name__)
 facilities_blueprint = Blueprint("facilities_blueprint", __name__)
 
-# for debug
-# https://raw.githubusercontent.com/Alexanderlacuna/gn-docs/feature/add-markdown-pages/
-
-
 def render_markdown(file_name):
     github_url = ("https://raw.githubusercontent.com/"
                   "genenetwork/gn-docs/master/")
-- 
cgit v1.2.3


From f900a574be30590cca0e9c962bfecf25121e4ab0 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 21:40:15 +0300
Subject: delete environment endpoint in wqflask/views.py

---
 wqflask/wqflask/views.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py
index 6cdce248..91d0842c 100644
--- a/wqflask/wqflask/views.py
+++ b/wqflask/wqflask/views.py
@@ -321,12 +321,6 @@ def credits():
     #return render_template("docs.html", **doc.__dict__)
     return render_template("credits.html")
 
-# @app.route("/environments")
-# def environments():
-#     doc = Docs("environments", request.args)
-#     return render_template("docs.html", **doc.__dict__)
-    #return render_template("environments.html", **doc.__dict__)
-
 @app.route("/update_text", methods=('POST',))
 def update_page():
     update_text(request.form)
-- 
cgit v1.2.3


From 8a74593bfda0d9e5e681046044c262713073164b Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 22:55:39 +0300
Subject: pep8 format for blue_print,url_prefix

---
 wqflask/wqflask/__init__.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/wqflask/wqflask/__init__.py b/wqflask/wqflask/__init__.py
index 0b69ec40..0564cfa7 100644
--- a/wqflask/wqflask/__init__.py
+++ b/wqflask/wqflask/__init__.py
@@ -24,11 +24,11 @@ app.jinja_env.globals.update(
 
 # Registering blueprints
 app.register_blueprint(glossary_blueprint, url_prefix="/glossary")
-app.register_blueprint(references_blueprint,url_prefix="/references")
-app.register_blueprint(links_blueprint,url_prefix="/links")
-app.register_blueprint(policies_blueprint,url_prefix="/policies")
-app.register_blueprint(environments_blueprint,url_prefix="/environments")
-app.register_blueprint(facilities_blueprint,url_prefix="/facilities")
+app.register_blueprint(references_blueprint, url_prefix="/references")
+app.register_blueprint(links_blueprint, url_prefix="/links")
+app.register_blueprint(policies_blueprint, url_prefix="/policies")
+app.register_blueprint(environments_blueprint, url_prefix="/environments")
+app.register_blueprint(facilities_blueprint, url_prefix="/facilities")
 
 @app.before_request
 def before_request():
-- 
cgit v1.2.3


From 63c90d88499c2cda1b3da0960e179856aaba05c9 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 23:00:53 +0300
Subject: add doc string for render_markdown in wqflask/markdown_routes.py

---
 wqflask/wqflask/markdown_routes.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 093dffeb..7e6c7948 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -16,6 +16,8 @@ policies_blueprint = Blueprint("policies_blueprint", __name__)
 facilities_blueprint = Blueprint("facilities_blueprint", __name__)
 
 def render_markdown(file_name):
+    """Try to fetch the file name from Github and if that fails, try to
+look for it inside the file system """
     github_url = ("https://raw.githubusercontent.com/"
                   "genenetwork/gn-docs/master/")
 
-- 
cgit v1.2.3


From b86f0f6200cb435cea91077352d5d95f610162a6 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 26 Nov 2020 23:35:00 +0300
Subject: use css block in templates and remove comments

---
 wqflask/wqflask/markdown_routes.py          | 3 ++-
 wqflask/wqflask/static/new/css/markdown.css | 7 -------
 wqflask/wqflask/templates/environment.html  | 2 --
 wqflask/wqflask/templates/facilities.html   | 3 +--
 wqflask/wqflask/templates/links.html        | 4 +---
 wqflask/wqflask/templates/policies.html     | 4 +---
 wqflask/wqflask/templates/references.html   | 3 +--
 7 files changed, 6 insertions(+), 20 deletions(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 7e6c7948..25eabe66 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -15,6 +15,7 @@ links_blueprint = Blueprint("links_blueprint", __name__)
 policies_blueprint = Blueprint("policies_blueprint", __name__)
 facilities_blueprint = Blueprint("facilities_blueprint", __name__)
 
+
 def render_markdown(file_name):
     """Try to fetch the file name from Github and if that fails, try to
 look for it inside the file system """
@@ -51,7 +52,7 @@ def references():
 
 @environments_blueprint.route("/")
 def environments():
-    return render_template("environment.html", rendered_markdown=render_markdown_table("general/environments/environments.md")), 200
+    return render_template("environment.html", rendered_markdown=render_markdown("general/environments/environments.md")), 200
 
 
 @links_blueprint.route("/")
diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
index 6d76c190..91b72cb8 100644
--- a/wqflask/wqflask/static/new/css/markdown.css
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -15,8 +15,6 @@
     margin-left: auto;
 }
 
-
-
 .github-btn {
     margin: 20px 10px;
     display: flex;
@@ -32,13 +30,8 @@
     transition-duration: 0.5s;
     padding: 8px 12px;
     color: #fff;
-    /*background: #27407a;*/
     background: blue;
     border-radius: 20px;
-
-    /*text-align: center;*/
-
-
 }
 
 .github-btn a:hover {
diff --git a/wqflask/wqflask/templates/environment.html b/wqflask/wqflask/templates/environment.html
index a331d494..f378e0b8 100644
--- a/wqflask/wqflask/templates/environment.html
+++ b/wqflask/wqflask/templates/environment.html
@@ -19,7 +19,6 @@
 
 {% endblock %}
diff --git a/wqflask/wqflask/templates/facilities.html b/wqflask/wqflask/templates/facilities.html
index 5be0c96d..63f110dd 100644
--- a/wqflask/wqflask/templates/facilities.html
+++ b/wqflask/wqflask/templates/facilities.html
@@ -3,7 +3,7 @@
 {% block title %}Facilities{% endblock %}
 
 {% block css %}
-
+
 {% endblock %}
 
 {% block content %}
@@ -17,5 +17,4 @@
 
 
    
 
-
 {% endblock %}
\ No newline at end of file
diff --git a/wqflask/wqflask/templates/links.html b/wqflask/wqflask/templates/links.html
index ef481de2..94d8eab7 100644
--- a/wqflask/wqflask/templates/links.html
+++ b/wqflask/wqflask/templates/links.html
@@ -3,7 +3,7 @@
 {% block title %}Links{% endblock %}
 
 {% block css %}
-
+
 {% endblock %}
 
 {% block content %}
@@ -16,6 +16,4 @@
    {{ rendered_markdown|safe }}
 
 
    
-
-
 {% endblock %}
\ No newline at end of file
diff --git a/wqflask/wqflask/templates/policies.html b/wqflask/wqflask/templates/policies.html
index ef481de2..94d8eab7 100644
--- a/wqflask/wqflask/templates/policies.html
+++ b/wqflask/wqflask/templates/policies.html
@@ -3,7 +3,7 @@
 {% block title %}Links{% endblock %}
 
 {% block css %}
-
+
 {% endblock %}
 
 {% block content %}
@@ -16,6 +16,4 @@
    {{ rendered_markdown|safe }}
 
 
    
-
-
 {% endblock %}
\ No newline at end of file
diff --git a/wqflask/wqflask/templates/references.html b/wqflask/wqflask/templates/references.html
index e2f152e3..8dcf4493 100644
--- a/wqflask/wqflask/templates/references.html
+++ b/wqflask/wqflask/templates/references.html
@@ -3,7 +3,7 @@
 {% block title %}Reference{% endblock %}
 
 {% block css %}
-
+
 {% endblock %}
 
 {% block content %}
@@ -17,5 +17,4 @@
 
 
    
 
-
 {% endblock %}
\ No newline at end of file
-- 
cgit v1.2.3


From cd27138b6cc963450851417ef06303027da51419 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Fri, 27 Nov 2020 00:15:18 +0300
Subject: remove commented code

---
 wqflask/wqflask/markdown_routes.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 25eabe66..601845d7 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -26,7 +26,6 @@ look for it inside the file system """
     if md_content.status_code == 200:
 
         return markdown.markdown(md_content.content.decode("utf-8"), extensions=['tables'])
-        # return markdown.Markdown().convert(md_content.content.decode("utf-8"))
     # TODO: Add fallback on our git server by checking the mirror.
     # Content not available
     return (f"\nContent for {file_name} not available. "
-- 
cgit v1.2.3


From 78fd4d4a89a02fc5233a1f5bbdfd3946ec08f3b7 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 26 Nov 2020 16:06:59 -0600
Subject: Changed the way the chromosome colors for the "color by chr"
 manhattan plot option are stored to using a list comprehension on a list of
 the just the hex strings

---
 .../marker_regression/display_mapping_results.py   | 32 ++++------------------
 1 file changed, 6 insertions(+), 26 deletions(-)

diff --git a/wqflask/wqflask/marker_regression/display_mapping_results.py b/wqflask/wqflask/marker_regression/display_mapping_results.py
index 87910401..08c2d750 100644
--- a/wqflask/wqflask/marker_regression/display_mapping_results.py
+++ b/wqflask/wqflask/marker_regression/display_mapping_results.py
@@ -75,32 +75,12 @@ MEDIUMPURPLE = ImageColor.getrgb("mediumpurple")
 # ---- END: Define common colours ---- #
 
 # ZS: List of distinct colors for manhattan plot if user selects "varied"
-DISTINCT_COLOR_LIST = [
-    ImageColor.getrgb("#FF0000"),
-    ImageColor.getrgb("#00FF00"),
-    ImageColor.getrgb("#0000FF"),
-    ImageColor.getrgb("#FFFF00"),
-    ImageColor.getrgb("#FF00FF"),
-    ImageColor.getrgb("#00FFFF"),
-    ImageColor.getrgb("#000000"),
-    ImageColor.getrgb("#800000"),
-    ImageColor.getrgb("#008000"),
-    ImageColor.getrgb("#000080"),
-    ImageColor.getrgb("#808000"),
-    ImageColor.getrgb("#800080"),
-    ImageColor.getrgb("#008080"),
-    ImageColor.getrgb("#808080"),
-    ImageColor.getrgb("#C00000"),
-    ImageColor.getrgb("#00C000"),
-    ImageColor.getrgb("#0000C0"),
-    ImageColor.getrgb("#C0C000"),
-    ImageColor.getrgb("#C000C0"),
-    ImageColor.getrgb("#00C0C0"),
-    ImageColor.getrgb("#C0C0C0"),
-    ImageColor.getrgb("#400000"),
-    ImageColor.getrgb("#004000"),
-    ImageColor.getrgb("#000040"),
-]
+COLOR_CODES = ["#FF0000", "#00FF00", "#0000FF", "#FFFF00", "#FF00FF", "#00FFFF",
+               "#000000", "#800000", "#008000", "#000080", "#808000", "#800080",
+               "#008080", "#808080", "#C00000", "#00C000", "#0000C0", "#C0C000",
+               "#C000C0", "#00C0C0", "#C0C0C0", "#400000", "#004000", "#000040"]
+
+DISTINCT_COLOR_LIST = [ImageColor.getrgb(color) for color in COLOR_CODES]
 
 # ---- FONT FILES ---- #
 VERDANA_FILE = "./wqflask/static/fonts/verdana.ttf"
-- 
cgit v1.2.3


From e396706f5c2bbb1bef21bcc8180565015aee3fb0 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Fri, 27 Nov 2020 12:59:18 +0300
Subject: add edit image

---
 wqflask/wqflask/static/images/edit.png | Bin 0 -> 2452 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 wqflask/wqflask/static/images/edit.png

diff --git a/wqflask/wqflask/static/images/edit.png b/wqflask/wqflask/static/images/edit.png
new file mode 100644
index 00000000..571b08cd
Binary files /dev/null and b/wqflask/wqflask/static/images/edit.png differ
-- 
cgit v1.2.3


From 5c4a2727f013785c8818864c0520321ef8d60dce Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Fri, 27 Nov 2020 13:00:47 +0300
Subject: add new github button and css

---
 wqflask/wqflask/static/new/css/markdown.css | 27 +++++++++++++--------------
 wqflask/wqflask/templates/environment.html  | 11 ++++++++---
 wqflask/wqflask/templates/facilities.html   | 12 ++++++++----
 wqflask/wqflask/templates/glossary.html     | 11 ++++++++---
 wqflask/wqflask/templates/links.html        | 11 ++++++++---
 wqflask/wqflask/templates/policies.html     | 12 ++++++++----
 wqflask/wqflask/templates/references.html   | 19 +++++++++----------
 7 files changed, 62 insertions(+), 41 deletions(-)

diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
index 91b72cb8..311962c1 100644
--- a/wqflask/wqflask/static/new/css/markdown.css
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -15,29 +15,28 @@
     margin-left: auto;
 }
 
-.github-btn {
+.github-btn-container {
     margin: 20px 10px;
     display: flex;
     width: 90vw;
     justify-content: flex-end;
 }
 
-.github-btn a {
-    display: inline-block;
-    align-self: center;
-    text-decoration: none;
-    transition-property: all;
-    transition-duration: 0.5s;
-    padding: 8px 12px;
-    color: #fff;
-    background: blue;
-    border-radius: 20px;
+.github-btn {
+    border: 3px solid #357ebd;
+    background: lightgrey;
+    padding: 6px 8px;
+    border-radius: 16px;
 }
 
-.github-btn a:hover {
-
-    background: #333;
+.github-btn a {
+    font-weight: bold;
+    color: #357ebd;
+}
 
+.github-btn a img {
+    height: 40px;
+    width: 40px;
 }
 
 
diff --git a/wqflask/wqflask/templates/environment.html b/wqflask/wqflask/templates/environment.html
index f378e0b8..183518a5 100644
--- a/wqflask/wqflask/templates/environment.html
+++ b/wqflask/wqflask/templates/environment.html
@@ -8,9 +8,14 @@
 
 {% block content %}
 
- 
    
- Edit on Github	
- 
    
+ 
    
+    
    
+        
+            Edit on Github inner
+            
+        
+    
    
+
    
 
    
 	
    
    {{ rendered_markdown|safe }} 
    
diff --git a/wqflask/wqflask/templates/facilities.html b/wqflask/wqflask/templates/facilities.html
index 63f110dd..652746a7 100644
--- a/wqflask/wqflask/templates/facilities.html
+++ b/wqflask/wqflask/templates/facilities.html
@@ -8,10 +8,14 @@
 
 {% block content %}
 
- 
    
- Edit on Github	
- 
    
-
+ 
    
+    
    
+        
+            Edit on Github inner
+            
+        
+    
    
+
    
 
    
    {{ rendered_markdown|safe }}
 
diff --git a/wqflask/wqflask/templates/glossary.html b/wqflask/wqflask/templates/glossary.html
index 395bcfea..f536d977 100644
--- a/wqflask/wqflask/templates/glossary.html
+++ b/wqflask/wqflask/templates/glossary.html
@@ -8,9 +8,14 @@
 
 {% block content %}
 
- 
    
- Edit on Github	
- 
    
+
    
+    
    
+        
+            Edit on Github inner
+            
+        
+    
    
+
    
 
    
 	
     {{ rendered_markdown|safe }}
diff --git a/wqflask/wqflask/templates/links.html b/wqflask/wqflask/templates/links.html
index 94d8eab7..cf524496 100644
--- a/wqflask/wqflask/templates/links.html
+++ b/wqflask/wqflask/templates/links.html
@@ -8,9 +8,14 @@
 
 {% block content %}
 
- 
    
- Edit on Github	
- 
    
+
    
+    
    
+        
+            Edit on Github inner
+            
+        
+    
    
+
    
 
 
    
    {{ rendered_markdown|safe }}
diff --git a/wqflask/wqflask/templates/policies.html b/wqflask/wqflask/templates/policies.html
index 94d8eab7..420493ad 100644
--- a/wqflask/wqflask/templates/policies.html
+++ b/wqflask/wqflask/templates/policies.html
@@ -8,10 +8,14 @@
 
 {% block content %}
 
- 
    
- Edit on Github	
- 
    
-
+ 
    
+    
    
+        
+            Edit on Github inner
+            
+        
+    
    
+
    
 
    
    {{ rendered_markdown|safe }}
 
diff --git a/wqflask/wqflask/templates/references.html b/wqflask/wqflask/templates/references.html
index 8dcf4493..caf87244 100644
--- a/wqflask/wqflask/templates/references.html
+++ b/wqflask/wqflask/templates/references.html
@@ -1,20 +1,19 @@
 {% extends "base.html" %}
-
 {% block title %}Reference{% endblock %}
-
 {% block css %}
 
 {% endblock %}
-
 {% block content %}
-
- 
    
- Edit on Github	
- 
    
-
+
    
+    
    
+        
+            Edit on Github inner
+            
+        
+    
    
+
    
 
    
-   {{ rendered_markdown|safe }}
-
+    {{ rendered_markdown|safe }}
 
    
 
 {% endblock %}
\ No newline at end of file
-- 
cgit v1.2.3


From a4842a8d54f891dcb59d29aa53a0e442f8f0e0c2 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Fri, 27 Nov 2020 13:33:47 +0300
Subject: update to edit text in static templates

---
 wqflask/wqflask/templates/environment.html | 2 +-
 wqflask/wqflask/templates/facilities.html  | 2 +-
 wqflask/wqflask/templates/glossary.html    | 2 +-
 wqflask/wqflask/templates/links.html       | 2 +-
 wqflask/wqflask/templates/policies.html    | 2 +-
 wqflask/wqflask/templates/references.html  | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/wqflask/wqflask/templates/environment.html b/wqflask/wqflask/templates/environment.html
index 183518a5..94b31464 100644
--- a/wqflask/wqflask/templates/environment.html
+++ b/wqflask/wqflask/templates/environment.html
@@ -11,7 +11,7 @@
  
    
     
    
         
-            Edit on Github inner
+            Edit Text
             
         
     
    
diff --git a/wqflask/wqflask/templates/facilities.html b/wqflask/wqflask/templates/facilities.html
index 652746a7..a022b657 100644
--- a/wqflask/wqflask/templates/facilities.html
+++ b/wqflask/wqflask/templates/facilities.html
@@ -11,7 +11,7 @@
  
    
     
    
         
-            Edit on Github inner
+            Edit Text
             
         
     
    
diff --git a/wqflask/wqflask/templates/glossary.html b/wqflask/wqflask/templates/glossary.html
index f536d977..752c4b12 100644
--- a/wqflask/wqflask/templates/glossary.html
+++ b/wqflask/wqflask/templates/glossary.html
@@ -11,7 +11,7 @@
 
    
     
    
         
-            Edit on Github inner
+            Edit Text
             
         
     
    
diff --git a/wqflask/wqflask/templates/links.html b/wqflask/wqflask/templates/links.html
index cf524496..072e8429 100644
--- a/wqflask/wqflask/templates/links.html
+++ b/wqflask/wqflask/templates/links.html
@@ -11,7 +11,7 @@
 
    
     
    
         
-            Edit on Github inner
+            Edit Text
             
         
     
    
diff --git a/wqflask/wqflask/templates/policies.html b/wqflask/wqflask/templates/policies.html
index 420493ad..4e0985d3 100644
--- a/wqflask/wqflask/templates/policies.html
+++ b/wqflask/wqflask/templates/policies.html
@@ -11,7 +11,7 @@
  
    
     
    
         
-            Edit on Github inner
+            Edit Text
             
         
     
    
diff --git a/wqflask/wqflask/templates/references.html b/wqflask/wqflask/templates/references.html
index caf87244..f723a1e8 100644
--- a/wqflask/wqflask/templates/references.html
+++ b/wqflask/wqflask/templates/references.html
@@ -7,7 +7,7 @@
 
    
     
    
         
-            Edit on Github inner
+            Edit Text
             
         
     
    
-- 
cgit v1.2.3


From 1cabb7810a8b2ab5171e1547e154244978fb37d4 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Fri, 27 Nov 2020 14:23:21 +0300
Subject: edit  css for static page

---
 wqflask/wqflask/static/new/css/markdown.css | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
index 311962c1..dca3e31d 100644
--- a/wqflask/wqflask/static/new/css/markdown.css
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -23,13 +23,17 @@
 }
 
 .github-btn {
+    display: flex;
+    justify-content: center;
     border: 3px solid #357ebd;
     background: lightgrey;
-    padding: 6px 8px;
+    padding: 3px 4px;
+    width: 200px;
     border-radius: 16px;
 }
 
 .github-btn a {
+    align-self: center;
     font-weight: bold;
     color: #357ebd;
 }
@@ -37,6 +41,7 @@
 .github-btn a img {
     height: 40px;
     width: 40px;
+    padding-left:5px;
 }
 
 
-- 
cgit v1.2.3


From 7f35213b64707e7c61affb6fa376f561200ac1d6 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Fri, 27 Nov 2020 19:59:17 +0300
Subject: move tests to unit folder

---
 .../marker_regression/test_gemma_mapping.py        | 205 +++++++++++++++
 .../marker_regression/test_plink_mapping.py        |  84 ++++++
 .../marker_regression/test_qtlreaper_mapping.py    |  21 ++
 .../wqflask/marker_regression/test_rqtl_mapping.py |  48 ++++
 .../wqflask/marker_regression/test_run_mapping.py  | 284 +++++++++++++++++++++
 .../marker_regression/test_gemma_mapping.py        | 205 ---------------
 .../marker_regression/test_plink_mapping.py        |  84 ------
 .../marker_regression/test_qtlreaper_mapping.py    |  21 --
 .../wqflask/marker_regression/test_rqtl_mapping.py |  48 ----
 .../wqflask/marker_regression/test_run_mapping.py  | 284 ---------------------
 10 files changed, 642 insertions(+), 642 deletions(-)
 create mode 100644 wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
 create mode 100644 wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py
 create mode 100644 wqflask/tests/unit/wqflask/marker_regression/test_qtlreaper_mapping.py
 create mode 100644 wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py
 create mode 100644 wqflask/tests/unit/wqflask/marker_regression/test_run_mapping.py
 delete mode 100644 wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
 delete mode 100644 wqflask/tests/wqflask/marker_regression/test_plink_mapping.py
 delete mode 100644 wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py
 delete mode 100644 wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
 delete mode 100644 wqflask/tests/wqflask/marker_regression/test_run_mapping.py

diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
new file mode 100644
index 00000000..5b621264
--- /dev/null
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
@@ -0,0 +1,205 @@
+# test for wqflask/marker_regression/gemma_mapping.py
+import unittest
+import random
+from unittest import mock
+from wqflask.marker_regression.gemma_mapping import run_gemma
+from wqflask.marker_regression.gemma_mapping import gen_pheno_txt_file
+from wqflask.marker_regression.gemma_mapping import gen_covariates_file
+from wqflask.marker_regression.gemma_mapping import parse_gemma_output
+from wqflask.marker_regression.gemma_mapping import parse_loco_output
+
+
+class AttributeSetter:
+    def __init__(self, obj):
+        for key, val in obj.items():
+            setattr(self, key, val)
+
+
+class MockGroup(AttributeSetter):
+    def get_samplelist(self):
+        return None
+
+
+class TestGemmaMapping(unittest.TestCase):
+
+    @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output")
+    def test_run_gemma_firstrun_set_false(self, mock_parse_loco):
+        """add tests for gemma function where  first run is set to false"""
+        dataset = AttributeSetter(
+            {"group": AttributeSetter({"genofile": "genofile.geno"})})
+
+        output_file = "file1"
+        mock_parse_loco.return_value = []
+        this_trait = AttributeSetter({"name": "t1"})
+
+        result = run_gemma(this_trait=this_trait, this_dataset=dataset, samples=[], vals=[
+        ], covariates="", use_loco=True, first_run=False, output_files=output_file)
+
+        expected_results = ([], "file1")
+        self.assertEqual(expected_results, result)
+
+    @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMAOPTS", "-debug")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMA_WRAPPER_COMMAND", "ghc")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data/")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.logger")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.gen_covariates_file")
+    @mock.patch("wqflask.marker_regression.run_mapping.random.choice")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.os")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file")
+    def test_run_gemma_firstrun_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco):
+        """add tests for run_gemma where first run is set to true"""
+        chromosomes = []
+        for i in range(1, 5):
+            chromosomes.append(AttributeSetter({"name": f"CH{i}"}))
+        chromo = AttributeSetter({"chromosomes": chromosomes})
+        dataset_group = MockGroup(
+            {"name": "GP1", "genofile": "file_geno"})
+        dataset = AttributeSetter({"group": dataset_group, "name": "dataset1_name",
+                                   "species": AttributeSetter({"chromosomes": chromo})})
+        trait = AttributeSetter({"name": "trait1"})
+        samples = []
+        mock_gen_pheno_txt.return_value = None
+        mock_os.path.isfile.return_value = True
+        mock_gen_covar.return_value = None
+        mock_choice.return_value = "R"
+        mock_flat_files.return_value = "/home/genotype/bimbam"
+        mock_parse_loco.return_value = []
+        results = run_gemma(this_trait=trait, this_dataset=dataset, samples=[
+        ], vals=[], covariates="", use_loco=True)
+        system_calls = [mock.call('ghc --json -- -debug -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt -a /home/genotype/bimbam/file_snps.txt -gk > /home/user/data//gn2/GP1_K_RRRRRR.json'),
+                        mock.call('ghc --json --input /home/user/data//gn2/GP1_K_RRRRRR.json -- -debug -a /home/genotype/bimbam/file_snps.txt -lmm 2 -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt > /home/user/data//gn2/GP1_GWA_RRRRRR.json')]
+        mock_os.system.assert_has_calls(system_calls)
+        mock_gen_pheno_txt.assert_called_once()
+        mock_parse_loco.assert_called_once_with(dataset, "GP1_GWA_RRRRRR")
+        mock_os.path.isfile.assert_called_once_with(
+            ('/home/user/imgfile_output.assoc.txt'))
+        self.assertEqual(mock_logger.debug.call_count, 2)
+        self.assertEqual(mock_flat_files.call_count, 4)
+        self.assertEqual(results, ([], "GP1_GWA_RRRRRR"))
+
+    @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data")
+    def test_gen_pheno_txt_file(self):
+        """add tests for generating pheno txt file"""
+        with mock.patch("builtins.open", mock.mock_open())as mock_open:
+            gen_pheno_txt_file(this_dataset={}, genofile_name="", vals=[
+                               "x", "w", "q", "we", "R"], trait_filename="fitr.re")
+            mock_open.assert_called_once_with(
+                '/home/user/data/gn2/fitr.re.txt', 'w')
+            filehandler = mock_open()
+            values = ["x", "w", "q", "we", "R"]
+            write_calls = [mock.call('NA\n'), mock.call('w\n'), mock.call(
+                'q\n'), mock.call('we\n'), mock.call('R\n')]
+
+            filehandler.write.assert_has_calls(write_calls)
+
+    @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.create_trait")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.create_dataset")
+    def test_gen_covariates_file(self, create_dataset, create_trait, flat_files):
+        """add tests for generating covariates files"""
+        covariates = "X1:X2,Y1:Y2,M1:M3,V1:V2"
+        samplelist = ["X1", "X2", "X3", "X4"]
+        create_dataset_side_effect = []
+        create_trait_side_effect = []
+
+        for i in range(4):
+            create_dataset_side_effect.append(AttributeSetter({"name": f'name_{i}'}))
+            create_trait_side_effect.append(
+                AttributeSetter({"data": [f'data_{i}']}))
+
+        create_dataset.side_effect = create_trait_side_effect
+        create_trait.side_effect = create_trait_side_effect
+
+        group = MockGroup({"name": "group_X", "samplelist": samplelist})
+        this_dataset = AttributeSetter({"group": group})
+        flat_files.return_value = "Home/Genenetwork"
+
+        with mock.patch("builtins.open", mock.mock_open())as mock_open:
+            gen_covariates_file(this_dataset=this_dataset, covariates=covariates,
+                                samples=["x1", "x2", "X3"])
+
+            create_dataset.assert_has_calls(
+                [mock.call('X2'), mock.call('Y2'), mock.call('M3'), mock.call('V2')])
+            mock_calls = []
+            trait_names = ["X1", "Y1", "M1", "V1"]
+
+            for i, trait in enumerate(create_trait_side_effect):
+                mock_calls.append(
+                    mock.call(dataset=trait, name=trait_names[i], cellid=None))
+
+            create_trait.assert_has_calls(mock_calls)
+
+            flat_files.assert_called_once_with('mapping')
+            mock_open.assert_called_once_with(
+                'Home/Genenetwork/group_X_covariates.txt', 'w')
+            filehandler = mock_open()
+            filehandler.write.assert_has_calls([mock.call(
+                '-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('\n')])
+
+    @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img/")
+    def test_parse_gemma_output(self):
+        """add test for generating gemma output with obj returned"""
+        file = """X/Y\t gn2\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5
+X/Y\tgn2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5
+chr\tgn1\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7
+X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4
+125\tgn9\t433575\tQ\tE\tA\tP\tMMB\tCDE\t0.67
+"""
+        with mock.patch("builtins.open", mock.mock_open(read_data=file)) as mock_open:
+            results = parse_gemma_output(genofile_name="gema_file")
+            expected = [{'name': ' gn2', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'gn2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812},
+                        {'name': 'gn7', 'chr': 'X', 'Mb': 2.324424, 'p_value': 0.4, 'lod_score': 0.3979400086720376}, {'name': 'gn9', 'chr': 125, 'Mb': 0.433575, 'p_value': 0.67, 'lod_score': 0.17392519729917352}]
+            mock_open.assert_called_once_with(
+                "/home/user/img/gema_file_output.assoc.txt")
+            self.assertEqual(results, expected)
+
+    @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img")
+    def test_parse_gemma_output_with_empty_return(self):
+        """add tests for parse gemma output where nothing returned"""
+        output_file_results = """chr\t today"""
+        with mock.patch("builtins.open", mock.mock_open(read_data=output_file_results)) as mock_open:
+            results = parse_gemma_output(genofile_name="gema_file")
+            self.assertEqual(results, [])
+
+    @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.os")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.json")
+    def test_parse_loco_outputfile_found(self, mock_json, mock_os):
+        """add tests for parse loco output file found"""
+        mock_json.load.return_value = {
+            "files": [["file_name", "user", "~/file1"],
+                      ["file_name", "user", "~/file2"]]
+        }
+        return_file_1 = """X/Y\t L1\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5
+X/Y\tL2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5
+chr\tL3\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7"""
+        return_file_2 = """chr\tother\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5"""
+        mock_os.path.isfile.return_value = True
+        file_to_write = """{"files":["file_1","file_2"]}"""
+        with mock.patch("builtins.open") as mock_open:
+
+            handles = (mock.mock_open(read_data="gwas").return_value, mock.mock_open(
+                read_data=return_file_1).return_value, mock.mock_open(read_data=return_file_2).return_value)
+            mock_open.side_effect = handles
+            results = parse_loco_output(
+                this_dataset={}, gwa_output_filename=".xw/")
+            expected_results = [{'name': ' L1', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {
+                'name': 'L2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}]
+
+            self.assertEqual(expected_results, results)
+
+    @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
+    @mock.patch("wqflask.marker_regression.gemma_mapping.os")
+    def test_parse_loco_outputfile_not_found(self, mock_os):
+        """add tests for parse loco output where  output file not found"""
+
+        mock_os.path.isfile.return_value = False
+        file_to_write = """{"files":["file_1","file_2"]}"""
+
+        with mock.patch("builtins.open", mock.mock_open(read_data=file_to_write)) as mock_open:
+            results = parse_loco_output(
+                this_dataset={}, gwa_output_filename=".xw/")
+            self.assertEqual(results, [])
diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py
new file mode 100644
index 00000000..428f45b9
--- /dev/null
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py
@@ -0,0 +1,84 @@
+# test for wqflask/marker_regression/plink_mapping.py
+import unittest
+from unittest import mock
+from wqflask.marker_regression.plink_mapping import build_line_list
+from wqflask.marker_regression.plink_mapping import get_samples_from_ped_file
+from wqflask.marker_regression.plink_mapping import flat_files
+from wqflask.marker_regression.plink_mapping import gen_pheno_txt_file_plink
+from wqflask.marker_regression.plink_mapping import parse_plink_output
+
+
+class AttributeSetter:
+    def __init__(self, obj):
+        for key, val in obj.items():
+            setattr(self, key, val)
+class TestPlinkMapping(unittest.TestCase):
+
+    def test_build_line_list(self):
+        """testing for building line list"""
+        line_1 = "this is line one test"
+        irregular_line = "  this     is an, irregular line     "
+        exp_line1 = ["this", "is", "line", "one", "test"]
+
+        results = build_line_list(irregular_line)
+        self.assertEqual(exp_line1, build_line_list(line_1))
+        self.assertEqual([], build_line_list())
+        self.assertEqual(["this", "is", "an,", "irregular", "line"], results)
+
+    @mock.patch("wqflask.marker_regression.plink_mapping.flat_files")
+    def test_get_samples_from_ped_file(self, mock_flat_files):
+        """test for getting samples from ped file"""
+        dataset = AttributeSetter({"group": AttributeSetter({"name": "n_1"})})
+        file_sample = """Expected_1\tline test
+Expected_2\there
+  Expected_3\tthree"""
+        mock_flat_files.return_value = "/home/user/"
+        with mock.patch("builtins.open", mock.mock_open(read_data=file_sample)) as mock_open:
+            results = get_samples_from_ped_file(dataset)
+            mock_flat_files.assert_called_once_with("mapping")
+            mock_open.assert_called_once_with("/home/user/n_1.ped", "r")
+            self.assertEqual(
+                ["Expected_1", "Expected_2", "Expected_3"], results)
+
+    @mock.patch("wqflask.marker_regression.plink_mapping.TMPDIR", "/home/user/data/")
+    @mock.patch("wqflask.marker_regression.plink_mapping.get_samples_from_ped_file")
+    def test_gen_pheno_txt_file_plink(self, mock_samples):
+        """test for getting gen_pheno txt file"""
+        mock_samples.return_value = ["Expected_1", "Expected_2", "Expected_3"]
+
+        trait = AttributeSetter({"name": "TX"})
+        dataset = AttributeSetter({"group": AttributeSetter({"name": "n_1"})})
+        vals = ["value=K1", "value=K2", "value=K3"]
+        with mock.patch("builtins.open", mock.mock_open()) as mock_open:
+            results = gen_pheno_txt_file_plink(this_trait=trait, dataset=dataset,
+                                               vals=vals, pheno_filename="ph_file")
+            mock_open.assert_called_once_with(
+                "/home/user/data/ph_file.txt", "wb")
+            filehandler = mock_open()
+            calls_expected = [mock.call('FID\tIID\tTX\n'),
+                              mock.call('Expected_1\tExpected_1\tK1\nExpected_2\tExpected_2\tK2\nExpected_3\tExpected_3\tK3\n')]
+
+            filehandler.write.assert_has_calls(calls_expected)
+
+            filehandler.close.assert_called_once()
+
+    @mock.patch("wqflask.marker_regression.plink_mapping.TMPDIR", "/home/user/data/")
+    @mock.patch("wqflask.marker_regression.plink_mapping.build_line_list")
+    def test_parse_plink_output(self, mock_line_list):
+        """test for parsing plink output"""
+        chromosomes = [0, 34, 110, 89, 123, 23, 2]
+        species = AttributeSetter(
+            {"name": "S1", "chromosomes": AttributeSetter({"chromosomes": chromosomes})})
+
+        fake_file = """0 AACCAT T98.6  0.89\n2  AATA  B45  0.3\n121  ACG  B56.4 NA"""
+
+        mock_line_list.side_effect = [["0", "AACCAT", "T98.6", "0.89"], [
+            "2", "AATA", "B45", "0.3"], ["121", "ACG", "B56.4", "NA"]]
+        with mock.patch("builtins.open", mock.mock_open(read_data=fake_file)) as mock_open:
+            parse_results = parse_plink_output(
+                output_filename="P1_file", species=species)
+            mock_open.assert_called_once_with(
+                "/home/user/data/P1_file.qassoc", "rb")
+            expected = (2, {'AACCAT': 0.89, 'AATA': 0.3})
+
+            self.assertEqual(parse_results, expected)
diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_qtlreaper_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_qtlreaper_mapping.py
new file mode 100644
index 00000000..b47f877a
--- /dev/null
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_qtlreaper_mapping.py
@@ -0,0 +1,21 @@
+import unittest
+from unittest import  mock
+from wqflask.marker_regression.qtlreaper_mapping import  gen_pheno_txt_file
+
+#issues some methods in genofile object are not defined
+#modify samples should equal to vals
+class TestQtlReaperMapping(unittest.TestCase):
+	@mock.patch("wqflask.marker_regression.qtlreaper_mapping.TEMPDIR", "/home/user/data")
+	def  test_gen_pheno_txt_file(self):                   
+		vals=["V1","x","V4","V3","x"]
+		samples=["S1","S2","S3","S4","S5"]
+		trait_filename="trait_file"
+		with mock.patch("builtins.open", mock.mock_open())as mock_open:
+			gen_pheno_txt_file(samples=samples,vals=vals,trait_filename=trait_filename)
+			mock_open.assert_called_once_with("/home/user/data/gn2/trait_file.txt","w")
+			filehandler=mock_open()
+			write_calls= [mock.call('Trait\t'),mock.call('S1\tS3\tS4\n'),mock.call('T1\t'),mock.call('V1\tV4\tV3')]
+
+			filehandler.write.assert_has_calls(write_calls)
+
+	                                                                                                                    
diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py
new file mode 100644
index 00000000..69f53721
--- /dev/null
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py
@@ -0,0 +1,48 @@
+import unittest
+from unittest import mock
+from wqflask import app
+from wqflask.marker_regression.rqtl_mapping import get_trait_data_type
+from wqflask.marker_regression.rqtl_mapping import sanitize_rqtl_phenotype
+from wqflask.marker_regression.rqtl_mapping import sanitize_rqtl_names
+
+class TestRqtlMapping(unittest.TestCase):
+
+	def setUp(self):
+		self.app_context=app.app_context()
+		self.app_context.push()
+
+	def tearDown(self):
+		self.app_context.pop()
+
+
+	@mock.patch("wqflask.marker_regression.rqtl_mapping.g")
+	@mock.patch("wqflask.marker_regression.rqtl_mapping.logger")
+	def test_get_trait_data(self,mock_logger,mock_db):
+		"""test for getting trait data_type return True"""
+		caller_value="""SELECT value FROM TraitMetadata WHERE type='trait_data_type'"""
+		mock_db.db.execute.return_value.fetchone.return_value=["""{"type":"trait_data_type","name":"T1","traid_id":"fer434f"}"""]
+		results=get_trait_data_type("traid_id")
+		mock_db.db.execute.assert_called_with(caller_value)
+		self.assertEqual(results,"fer434f")
+
+	def test_sanitize_rqtl_phenotype(self):
+		"""test for sanitizing rqtl phenotype"""
+		vals=['f',"x","r","x","x"]
+		results=sanitize_rqtl_phenotype(vals)
+		expected_phenotype_string='c(f,NA,r,NA,NA)'
+
+		self.assertEqual(results,expected_phenotype_string)
+
+	def test_sanitize_rqtl_names(self):
+		"""test for sanitzing rqtl names"""
+		vals=['f',"x","r","x","x"]
+		expected_sanitized_name="c('f',NA,'r',NA,NA)"
+		results=sanitize_rqtl_names(vals)
+		self.assertEqual(expected_sanitized_name,results)
+
+
+		
+		
+
+
+
diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_run_mapping.py
new file mode 100644
index 00000000..4129cc0c
--- /dev/null
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_run_mapping.py
@@ -0,0 +1,284 @@
+import unittest
+import datetime
+from unittest import mock
+
+from wqflask.marker_regression.run_mapping import get_genofile_samplelist
+from wqflask.marker_regression.run_mapping import geno_db_exists
+from wqflask.marker_regression.run_mapping import write_input_for_browser
+from wqflask.marker_regression.run_mapping import export_mapping_results
+from wqflask.marker_regression.run_mapping import trim_markers_for_figure
+from wqflask.marker_regression.run_mapping import get_perm_strata
+from wqflask.marker_regression.run_mapping import get_chr_lengths
+
+
+class AttributeSetter:
+    def __init__(self, obj):
+        for k, v in obj.items():
+            setattr(self, k, v)
+
+
+class MockGroup(AttributeSetter):
+
+    def get_genofiles(self):
+        return [{"location": "~/genofiles/g1_file", "sample_list": ["S1", "S2", "S3", "S4"]}]
+
+
+class TestRunMapping(unittest.TestCase):
+    def setUp(self):
+
+        self.group = MockGroup(
+            {"genofile": "~/genofiles/g1_file", "name": "GP1_", "species": "Human"})
+        chromosomes = {
+            "3": AttributeSetter({
+                "name": "C1",
+                "length": "0.04"
+            }),
+            "4": AttributeSetter({
+                "name": "C2",
+                "length": "0.03"
+            }),
+            "5": AttributeSetter({
+                "name": "C4",
+                "length": "0.01"
+            })
+        }
+        self.dataset = AttributeSetter(
+            {"fullname": "dataser_1", "group": self.group, "type": "ProbeSet"})
+
+        self.chromosomes = AttributeSetter({"chromosomes": chromosomes})
+        self.trait = AttributeSetter(
+            {"symbol": "IGFI", "chr": "X1", "mb": 123313})
+
+    def tearDown(self):
+        self.dataset = AttributeSetter(
+            {"group": {"location": "~/genofiles/g1_file"}})
+
+    def test_get_genofile_samplelist(self):
+
+        results_1 = get_genofile_samplelist(self.dataset)
+        self.assertEqual(results_1, ["S1", "S2", "S3", "S4"])
+        self.group.genofile = "~/genofiles/g2_file"
+        result_2 = get_genofile_samplelist(self.dataset)
+        self.assertEqual(result_2, [])
+
+    @mock.patch("wqflask.marker_regression.run_mapping.data_set")
+    def test_if_geno_db_exists(self, mock_data_set):
+        mock_data_set.create_dataset.side_effect = [
+            AttributeSetter({}), Exception()]
+        results_no_error = geno_db_exists(self.dataset)
+        results_with_error = geno_db_exists(self.dataset)
+
+        self.assertEqual(mock_data_set.create_dataset.call_count, 2)
+        self.assertEqual(results_with_error, "False")
+        self.assertEqual(results_no_error, "True")
+
+    def test_trim_markers_for_figure(self):
+
+        markers = [{
+            "name": "MK1",
+            "chr": "C1",
+            "cM": "1",
+            "Mb": "12000",
+            "genotypes": [],
+            "dominance":"TT",
+            "additive":"VA",
+            "lod_score":0.5
+        },
+            {
+            "name": "MK2",
+            "chr": "C2",
+            "cM": "15",
+            "Mb": "10000",
+            "genotypes": [],
+            "lod_score":0.7
+        },
+            {
+            "name": "MK1",
+            "chr": "C3",
+            "cM": "45",
+            "Mb": "1",
+            "genotypes": [],
+            "dominance":"Tt",
+            "additive":"VE",
+            "lod_score":1
+        }]
+
+        marker_2 = [{
+            "name": "MK1",
+            "chr": "C1",
+            "cM": "1",
+            "Mb": "12000",
+            "genotypes": [],
+            "dominance":"TT",
+            "additive":"VA",
+            "p_wald":4.6
+        }]
+        results = trim_markers_for_figure(markers)
+        result_2 = trim_markers_for_figure(marker_2)
+        expected = [
+            {
+                "name": "MK1",
+                "chr": "C1",
+                "cM": "1",
+                "Mb": "12000",
+                "genotypes": [],
+                "dominance":"TT",
+                "additive":"VA",
+                "lod_score":0.5
+            },
+            {
+                "name": "MK1",
+                "chr": "C3",
+                "cM": "45",
+                "Mb": "1",
+                "genotypes": [],
+                "dominance":"Tt",
+                "additive":"VE",
+                "lod_score":1
+            }
+
+        ]
+        self.assertEqual(results, expected)
+        self.assertEqual(result_2, marker_2)
+
+    def test_export_mapping_results(self):
+        """test for exporting mapping results"""
+        datetime_mock = mock.Mock(wraps=datetime.datetime)
+        datetime_mock.now.return_value = datetime.datetime(
+            2019, 9, 1, 10, 12, 12)
+
+        markers = [{
+            "name": "MK1",
+            "chr": "C1",
+            "cM": "1",
+            "Mb": "12000",
+            "genotypes": [],
+            "dominance":"TT",
+            "additive":"VA",
+            "lod_score":3
+        },
+            {
+            "name": "MK2",
+            "chr": "C2",
+            "cM": "15",
+            "Mb": "10000",
+            "genotypes": [],
+            "lod_score":7
+        },
+            {
+            "name": "MK1",
+            "chr": "C3",
+            "cM": "45",
+            "Mb": "1",
+            "genotypes": [],
+            "dominance":"Tt",
+            "additive":"VE",
+            "lod_score":7
+        }]
+
+        with mock.patch("builtins.open", mock.mock_open()) as mock_open:
+
+            with mock.patch("wqflask.marker_regression.run_mapping.datetime.datetime", new=datetime_mock):
+                export_mapping_results(dataset=self.dataset, trait=self.trait, markers=markers,
+                                       results_path="~/results", mapping_scale="physic", score_type="-log(p)")
+
+                write_calls = [
+                    mock.call('Time/Date: 09/01/19 / 10:12:12\n'),
+                    mock.call('Population: Human GP1_\n'), mock.call(
+                        'Data Set: dataser_1\n'),
+                    mock.call('Gene Symbol: IGFI\n'), mock.call(
+                        'Location: X1 @ 123313 Mb\n'),
+                    mock.call('\n'), mock.call('Name,Chr,'),
+                    mock.call('Mb,-log(p)'), mock.call('Cm,-log(p)'),
+                    mock.call(',Additive'), mock.call(',Dominance'),
+                    mock.call('\n'), mock.call('MK1,C1,'),
+                    mock.call('12000,'), mock.call('1,'),
+                    mock.call('3'), mock.call(',VA'),
+                    mock.call(',TT'), mock.call('\n'),
+                    mock.call('MK2,C2,'), mock.call('10000,'),
+                    mock.call('15,'), mock.call('7'),
+                    mock.call('\n'), mock.call('MK1,C3,'),
+                    mock.call('1,'), mock.call('45,'),
+                    mock.call('7'), mock.call(',VE'),
+                    mock.call(',Tt')
+
+                ]
+                mock_open.assert_called_once_with("~/results", "w+")
+                filehandler = mock_open()
+                filehandler.write.assert_has_calls(write_calls)
+
+    @mock.patch("wqflask.marker_regression.run_mapping.random.choice")
+    def test_write_input_for_browser(self, mock_choice):
+        """test for writing input for browser"""
+        mock_choice.side_effect = ["F", "i", "l", "e", "s", "x"]
+        with mock.patch("builtins.open", mock.mock_open()) as mock_open:
+            expected = ['GP1__Filesx_GWAS', 'GP1__Filesx_ANNOT']
+
+            results = write_input_for_browser(
+                this_dataset=self.dataset, gwas_results={}, annotations={})
+            self.assertEqual(results, expected)
+
+    def test_get_perm_strata(self):
+        categorical_vars = ["C1", "C2", "W1"]
+        used_samples = ["S1", "S2"]
+        sample_list = AttributeSetter({"sample_attribute_values": {
+            "S1": {
+                "C1": "c1_value",
+                "C2": "c2_value",
+                "W1": "w1_value"
+
+            },
+            "S2": {
+                "W1": "w2_value",
+                "W2": "w2_value"
+
+            },
+            "S3": {
+
+                "C1": "c1_value",
+                "C2": "c2_value"
+
+            },
+
+        }})
+
+        results = get_perm_strata(this_trait={}, sample_list=sample_list,
+                                  categorical_vars=categorical_vars, used_samples=used_samples)
+        self.assertEqual(results, [2, 1])
+
+    def test_get_chr_length(self):
+        """test for getting chromosome length"""
+        chromosomes = AttributeSetter({"chromosomes": self.chromosomes})
+        dataset = AttributeSetter({"species": chromosomes})
+        results = get_chr_lengths(
+            mapping_scale="physic", mapping_method="reaper", dataset=dataset, qtl_results=[])
+        chr_lengths = []
+        for key, chromo in self.chromosomes.chromosomes.items():
+            chr_lengths.append({"chr": chromo.name, "size": chromo.length})
+
+        self.assertEqual(chr_lengths, results)
+
+        qtl_results = [{
+            "chr": "16",
+            "cM": "0.2"
+        },
+            {
+            "chr": "12",
+            "cM": "0.5"
+        },
+            {
+            "chr": "18",
+            "cM": "0.1"
+        },
+            {
+            "chr": "22",
+            "cM": "0.4"
+        },
+        ]
+
+        result_with_other_mapping_scale = get_chr_lengths(
+            mapping_scale="other", mapping_method="reaper", dataset=dataset, qtl_results=qtl_results)
+        expected_value = [{'chr': '1', 'size': '0'}, {
+            'chr': '16', 'size': '500000.0'}, {'chr': '18', 'size': '400000.0'}]
+
+        self.assertEqual(result_with_other_mapping_scale, expected_value)
diff --git a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
deleted file mode 100644
index 4fafd95a..00000000
--- a/wqflask/tests/wqflask/marker_regression/test_gemma_mapping.py
+++ /dev/null
@@ -1,205 +0,0 @@
-# test for wqflask/marker_regression/gemma_mapping.py
-import unittest
-import random
-from unittest import mock
-from wqflask.marker_regression.gemma_mapping import run_gemma
-from wqflask.marker_regression.gemma_mapping import gen_pheno_txt_file
-from wqflask.marker_regression.gemma_mapping import gen_covariates_file
-from wqflask.marker_regression.gemma_mapping import parse_gemma_output
-from wqflask.marker_regression.gemma_mapping import parse_loco_output
-
-
-class AttributeSetter:
-    def __init__(self, obj):
-        for key, val in obj.items():
-            setattr(self, key, val)
-
-
-class MockDatasetGroup(AttributeSetter):
-    def get_samplelist(self):
-        return None
-
-
-class TestGemmaMapping(unittest.TestCase):
-
-    @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output")
-    def test_run_gemma_firstrun_set_false(self, mock_parse_loco):
-        """add tests for gemma function where  first run is set to false"""
-        dataset = AttributeSetter(
-            {"group": AttributeSetter({"genofile": "genofile.geno"})})
-
-        output_file = "file1"
-        mock_parse_loco.return_value = []
-        this_trait = AttributeSetter({"name": "t1"})
-
-        result = run_gemma(this_trait=this_trait, this_dataset=dataset, samples=[], vals=[
-        ], covariates="", use_loco=True, first_run=False, output_files=output_file)
-
-        expected_results = ([], "file1")
-        self.assertEqual(expected_results, result)
-
-    @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMAOPTS", "-debug")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMA_WRAPPER_COMMAND", "ghc")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data/")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.logger")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.gen_covariates_file")
-    @mock.patch("wqflask.marker_regression.run_mapping.random.choice")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.os")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file")
-    def test_run_gemma_firstrun_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco):
-        """add tests for run_gemma where first run is set to true"""
-        chromosomes = []
-        for i in range(1, 5):
-            chromosomes.append(AttributeSetter({"name": f"CH{i}"}))
-        chromo = AttributeSetter({"chromosomes": chromosomes})
-        dataset_group = MockDatasetGroup(
-            {"name": "GP1", "genofile": "file_geno"})
-        dataset = AttributeSetter({"group": dataset_group, "name": "dataset1_name",
-                                   "species": AttributeSetter({"chromosomes": chromo})})
-        trait = AttributeSetter({"name": "trait1"})
-        samples = []
-        mock_gen_pheno_txt.return_value = None
-        mock_os.path.isfile.return_value = True
-        mock_gen_covar.return_value = None
-        mock_choice.return_value = "R"
-        mock_flat_files.return_value = "/home/genotype/bimbam"
-        mock_parse_loco.return_value = []
-        results = run_gemma(this_trait=trait, this_dataset=dataset, samples=[
-        ], vals=[], covariates="", use_loco=True)
-        system_calls = [mock.call('ghc --json -- -debug -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt -a /home/genotype/bimbam/file_snps.txt -gk > /home/user/data//gn2/GP1_K_RRRRRR.json'),
-                        mock.call('ghc --json --input /home/user/data//gn2/GP1_K_RRRRRR.json -- -debug -a /home/genotype/bimbam/file_snps.txt -lmm 2 -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt > /home/user/data//gn2/GP1_GWA_RRRRRR.json')]
-        mock_os.system.assert_has_calls(system_calls)
-        mock_gen_pheno_txt.assert_called_once()
-        mock_parse_loco.assert_called_once_with(dataset, "GP1_GWA_RRRRRR")
-        mock_os.path.isfile.assert_called_once_with(
-            ('/home/user/imgfile_output.assoc.txt'))
-        self.assertEqual(mock_logger.debug.call_count, 2)
-        self.assertEqual(mock_flat_files.call_count, 4)
-        self.assertEqual(results, ([], "GP1_GWA_RRRRRR"))
-
-    @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data")
-    def test_gen_pheno_txt_file(self):
-        """add tests for generating pheno txt file"""
-        with mock.patch("builtins.open", mock.mock_open())as mock_open:
-            gen_pheno_txt_file(this_dataset={}, genofile_name="", vals=[
-                               "x", "w", "q", "we", "R"], trait_filename="fitr.re")
-            mock_open.assert_called_once_with(
-                '/home/user/data/gn2/fitr.re.txt', 'w')
-            filehandler = mock_open()
-            values = ["x", "w", "q", "we", "R"]
-            write_calls = [mock.call('NA\n'), mock.call('w\n'), mock.call(
-                'q\n'), mock.call('we\n'), mock.call('R\n')]
-
-            filehandler.write.assert_has_calls(write_calls)
-
-    @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.create_trait")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.create_dataset")
-    def test_gen_covariates_file(self, create_dataset, create_trait, flat_files):
-        """add tests for generating covariates files"""
-        covariates = "X1:X2,Y1:Y2,M1:M3,V1:V2"
-        samplelist = ["X1", "X2", "X3", "X4"]
-        create_dataset_side_effect = []
-        create_trait_side_effect = []
-
-        for i in range(4):
-            create_dataset_side_effect.append(AttributeSetter({"name": f'name_{i}'}))
-            create_trait_side_effect.append(
-                AttributeSetter({"data": [f'data_{i}']}))
-
-        create_dataset.side_effect = create_trait_side_effect
-        create_trait.side_effect = create_trait_side_effect
-
-        group = MockDatasetGroup({"name": "group_X", "samplelist": samplelist})
-        this_dataset = AttributeSetter({"group": group})
-        flat_files.return_value = "Home/Genenetwork"
-
-        with mock.patch("builtins.open", mock.mock_open())as mock_open:
-            gen_covariates_file(this_dataset=this_dataset, covariates=covariates,
-                                samples=["x1", "x2", "X3"])
-
-            create_dataset.assert_has_calls(
-                [mock.call('X2'), mock.call('Y2'), mock.call('M3'), mock.call('V2')])
-            mock_calls = []
-            trait_names = ["X1", "Y1", "M1", "V1"]
-
-            for i, trait in enumerate(create_trait_side_effect):
-                mock_calls.append(
-                    mock.call(dataset=trait, name=trait_names[i], cellid=None))
-
-            create_trait.assert_has_calls(mock_calls)
-
-            flat_files.assert_called_once_with('mapping')
-            mock_open.assert_called_once_with(
-                'Home/Genenetwork/group_X_covariates.txt', 'w')
-            filehandler = mock_open()
-            filehandler.write.assert_has_calls([mock.call(
-                '-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('\n')])
-
-    @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img/")
-    def test_parse_gemma_output(self):
-        """add test for generating gemma output with obj returned"""
-        file = """X/Y\t gn2\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5
-X/Y\tgn2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5
-chr\tgn1\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7
-X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4
-125\tgn9\t433575\tQ\tE\tA\tP\tMMB\tCDE\t0.67
-"""
-        with mock.patch("builtins.open", mock.mock_open(read_data=file)) as mock_open:
-            results = parse_gemma_output(genofile_name="gema_file")
-            expected = [{'name': ' gn2', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'gn2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812},
-                        {'name': 'gn7', 'chr': 'X', 'Mb': 2.324424, 'p_value': 0.4, 'lod_score': 0.3979400086720376}, {'name': 'gn9', 'chr': 125, 'Mb': 0.433575, 'p_value': 0.67, 'lod_score': 0.17392519729917352}]
-            mock_open.assert_called_once_with(
-                "/home/user/img/gema_file_output.assoc.txt")
-            self.assertEqual(results, expected)
-
-    @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img")
-    def test_parse_gemma_output_with_empty_return(self):
-        """add tests for parse gemma output where nothing returned"""
-        output_file_results = """chr\t today"""
-        with mock.patch("builtins.open", mock.mock_open(read_data=output_file_results)) as mock_open:
-            results = parse_gemma_output(genofile_name="gema_file")
-            self.assertEqual(results, [])
-
-    @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.os")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.json")
-    def test_parse_loco_outputfile_found(self, mock_json, mock_os):
-        """add tests for parse loco output file found"""
-        mock_json.load.return_value = {
-            "files": [["file_name", "user", "~/file1"],
-                      ["file_name", "user", "~/file2"]]
-        }
-        return_file_1 = """X/Y\t L1\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5
-X/Y\tL2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5
-chr\tL3\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7"""
-        return_file_2 = """chr\tother\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5"""
-        mock_os.path.isfile.return_value = True
-        file_to_write = """{"files":["file_1","file_2"]}"""
-        with mock.patch("builtins.open") as mock_open:
-
-            handles = (mock.mock_open(read_data="gwas").return_value, mock.mock_open(
-                read_data=return_file_1).return_value, mock.mock_open(read_data=return_file_2).return_value)
-            mock_open.side_effect = handles
-            results = parse_loco_output(
-                this_dataset={}, gwa_output_filename=".xw/")
-            expected_results = [{'name': ' L1', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {
-                'name': 'L2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}]
-
-            self.assertEqual(expected_results, results)
-
-    @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.os")
-    def test_parse_loco_outputfile_not_found(self, mock_os):
-        """add tests for parse loco output where  output file not found"""
-
-        mock_os.path.isfile.return_value = False
-        file_to_write = """{"files":["file_1","file_2"]}"""
-
-        with mock.patch("builtins.open", mock.mock_open(read_data=file_to_write)) as mock_open:
-            results = parse_loco_output(
-                this_dataset={}, gwa_output_filename=".xw/")
-            self.assertEqual(results, [])
diff --git a/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py b/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py
deleted file mode 100644
index 428f45b9..00000000
--- a/wqflask/tests/wqflask/marker_regression/test_plink_mapping.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# test for wqflask/marker_regression/plink_mapping.py
-import unittest
-from unittest import mock
-from wqflask.marker_regression.plink_mapping import build_line_list
-from wqflask.marker_regression.plink_mapping import get_samples_from_ped_file
-from wqflask.marker_regression.plink_mapping import flat_files
-from wqflask.marker_regression.plink_mapping import gen_pheno_txt_file_plink
-from wqflask.marker_regression.plink_mapping import parse_plink_output
-
-
-class AttributeSetter:
-    def __init__(self, obj):
-        for key, val in obj.items():
-            setattr(self, key, val)
-class TestPlinkMapping(unittest.TestCase):
-
-    def test_build_line_list(self):
-        """testing for building line list"""
-        line_1 = "this is line one test"
-        irregular_line = "  this     is an, irregular line     "
-        exp_line1 = ["this", "is", "line", "one", "test"]
-
-        results = build_line_list(irregular_line)
-        self.assertEqual(exp_line1, build_line_list(line_1))
-        self.assertEqual([], build_line_list())
-        self.assertEqual(["this", "is", "an,", "irregular", "line"], results)
-
-    @mock.patch("wqflask.marker_regression.plink_mapping.flat_files")
-    def test_get_samples_from_ped_file(self, mock_flat_files):
-        """test for getting samples from ped file"""
-        dataset = AttributeSetter({"group": AttributeSetter({"name": "n_1"})})
-        file_sample = """Expected_1\tline test
-Expected_2\there
-  Expected_3\tthree"""
-        mock_flat_files.return_value = "/home/user/"
-        with mock.patch("builtins.open", mock.mock_open(read_data=file_sample)) as mock_open:
-            results = get_samples_from_ped_file(dataset)
-            mock_flat_files.assert_called_once_with("mapping")
-            mock_open.assert_called_once_with("/home/user/n_1.ped", "r")
-            self.assertEqual(
-                ["Expected_1", "Expected_2", "Expected_3"], results)
-
-    @mock.patch("wqflask.marker_regression.plink_mapping.TMPDIR", "/home/user/data/")
-    @mock.patch("wqflask.marker_regression.plink_mapping.get_samples_from_ped_file")
-    def test_gen_pheno_txt_file_plink(self, mock_samples):
-        """test for getting gen_pheno txt file"""
-        mock_samples.return_value = ["Expected_1", "Expected_2", "Expected_3"]
-
-        trait = AttributeSetter({"name": "TX"})
-        dataset = AttributeSetter({"group": AttributeSetter({"name": "n_1"})})
-        vals = ["value=K1", "value=K2", "value=K3"]
-        with mock.patch("builtins.open", mock.mock_open()) as mock_open:
-            results = gen_pheno_txt_file_plink(this_trait=trait, dataset=dataset,
-                                               vals=vals, pheno_filename="ph_file")
-            mock_open.assert_called_once_with(
-                "/home/user/data/ph_file.txt", "wb")
-            filehandler = mock_open()
-            calls_expected = [mock.call('FID\tIID\tTX\n'),
-                              mock.call('Expected_1\tExpected_1\tK1\nExpected_2\tExpected_2\tK2\nExpected_3\tExpected_3\tK3\n')]
-
-            filehandler.write.assert_has_calls(calls_expected)
-
-            filehandler.close.assert_called_once()
-
-    @mock.patch("wqflask.marker_regression.plink_mapping.TMPDIR", "/home/user/data/")
-    @mock.patch("wqflask.marker_regression.plink_mapping.build_line_list")
-    def test_parse_plink_output(self, mock_line_list):
-        """test for parsing plink output"""
-        chromosomes = [0, 34, 110, 89, 123, 23, 2]
-        species = AttributeSetter(
-            {"name": "S1", "chromosomes": AttributeSetter({"chromosomes": chromosomes})})
-
-        fake_file = """0 AACCAT T98.6  0.89\n2  AATA  B45  0.3\n121  ACG  B56.4 NA"""
-
-        mock_line_list.side_effect = [["0", "AACCAT", "T98.6", "0.89"], [
-            "2", "AATA", "B45", "0.3"], ["121", "ACG", "B56.4", "NA"]]
-        with mock.patch("builtins.open", mock.mock_open(read_data=fake_file)) as mock_open:
-            parse_results = parse_plink_output(
-                output_filename="P1_file", species=species)
-            mock_open.assert_called_once_with(
-                "/home/user/data/P1_file.qassoc", "rb")
-            expected = (2, {'AACCAT': 0.89, 'AATA': 0.3})
-
-            self.assertEqual(parse_results, expected)
diff --git a/wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py b/wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py
deleted file mode 100644
index b47f877a..00000000
--- a/wqflask/tests/wqflask/marker_regression/test_qtlreaper_mapping.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import unittest
-from unittest import  mock
-from wqflask.marker_regression.qtlreaper_mapping import  gen_pheno_txt_file
-
-#issues some methods in genofile object are not defined
-#modify samples should equal to vals
-class TestQtlReaperMapping(unittest.TestCase):
-	@mock.patch("wqflask.marker_regression.qtlreaper_mapping.TEMPDIR", "/home/user/data")
-	def  test_gen_pheno_txt_file(self):                   
-		vals=["V1","x","V4","V3","x"]
-		samples=["S1","S2","S3","S4","S5"]
-		trait_filename="trait_file"
-		with mock.patch("builtins.open", mock.mock_open())as mock_open:
-			gen_pheno_txt_file(samples=samples,vals=vals,trait_filename=trait_filename)
-			mock_open.assert_called_once_with("/home/user/data/gn2/trait_file.txt","w")
-			filehandler=mock_open()
-			write_calls= [mock.call('Trait\t'),mock.call('S1\tS3\tS4\n'),mock.call('T1\t'),mock.call('V1\tV4\tV3')]
-
-			filehandler.write.assert_has_calls(write_calls)
-
-	                                                                                                                    
diff --git a/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py b/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
deleted file mode 100644
index 69f53721..00000000
--- a/wqflask/tests/wqflask/marker_regression/test_rqtl_mapping.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import unittest
-from unittest import mock
-from wqflask import app
-from wqflask.marker_regression.rqtl_mapping import get_trait_data_type
-from wqflask.marker_regression.rqtl_mapping import sanitize_rqtl_phenotype
-from wqflask.marker_regression.rqtl_mapping import sanitize_rqtl_names
-
-class TestRqtlMapping(unittest.TestCase):
-
-	def setUp(self):
-		self.app_context=app.app_context()
-		self.app_context.push()
-
-	def tearDown(self):
-		self.app_context.pop()
-
-
-	@mock.patch("wqflask.marker_regression.rqtl_mapping.g")
-	@mock.patch("wqflask.marker_regression.rqtl_mapping.logger")
-	def test_get_trait_data(self,mock_logger,mock_db):
-		"""test for getting trait data_type return True"""
-		caller_value="""SELECT value FROM TraitMetadata WHERE type='trait_data_type'"""
-		mock_db.db.execute.return_value.fetchone.return_value=["""{"type":"trait_data_type","name":"T1","traid_id":"fer434f"}"""]
-		results=get_trait_data_type("traid_id")
-		mock_db.db.execute.assert_called_with(caller_value)
-		self.assertEqual(results,"fer434f")
-
-	def test_sanitize_rqtl_phenotype(self):
-		"""test for sanitizing rqtl phenotype"""
-		vals=['f',"x","r","x","x"]
-		results=sanitize_rqtl_phenotype(vals)
-		expected_phenotype_string='c(f,NA,r,NA,NA)'
-
-		self.assertEqual(results,expected_phenotype_string)
-
-	def test_sanitize_rqtl_names(self):
-		"""test for sanitzing rqtl names"""
-		vals=['f',"x","r","x","x"]
-		expected_sanitized_name="c('f',NA,'r',NA,NA)"
-		results=sanitize_rqtl_names(vals)
-		self.assertEqual(expected_sanitized_name,results)
-
-
-		
-		
-
-
-
diff --git a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
deleted file mode 100644
index a134f551..00000000
--- a/wqflask/tests/wqflask/marker_regression/test_run_mapping.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import unittest
-import datetime
-from unittest import mock
-
-from wqflask.marker_regression.run_mapping import get_genofile_samplelist
-from wqflask.marker_regression.run_mapping import geno_db_exists
-from wqflask.marker_regression.run_mapping import write_input_for_browser
-from wqflask.marker_regression.run_mapping import export_mapping_results
-from wqflask.marker_regression.run_mapping import trim_markers_for_figure
-from wqflask.marker_regression.run_mapping import get_perm_strata
-from wqflask.marker_regression.run_mapping import get_chr_lengths
-
-
-class AttributeSetter:
-    def __init__(self, obj):
-        for k, v in obj.items():
-            setattr(self, k, v)
-
-
-class MockDataSetGroup(AttributeSetter):
-
-    def get_genofiles(self):
-        return [{"location": "~/genofiles/g1_file", "sample_list": ["S1", "S2", "S3", "S4"]}]
-
-
-class TestRunMapping(unittest.TestCase):
-    def setUp(self):
-
-        self.group = MockDataSetGroup(
-            {"genofile": "~/genofiles/g1_file", "name": "GP1_", "species": "Human"})
-        chromosomes = {
-            "3": AttributeSetter({
-                "name": "C1",
-                "length": "0.04"
-            }),
-            "4": AttributeSetter({
-                "name": "C2",
-                "length": "0.03"
-            }),
-            "5": AttributeSetter({
-                "name": "C4",
-                "length": "0.01"
-            })
-        }
-        self.dataset = AttributeSetter(
-            {"fullname": "dataser_1", "group": self.group, "type": "ProbeSet"})
-
-        self.chromosomes = AttributeSetter({"chromosomes": chromosomes})
-        self.trait = AttributeSetter(
-            {"symbol": "IGFI", "chr": "X1", "mb": 123313})
-
-    def tearDown(self):
-        self.dataset = AttributeSetter(
-            {"group": {"location": "~/genofiles/g1_file"}})
-
-    def test_get_genofile_samplelist(self):
-
-        results_1 = get_genofile_samplelist(self.dataset)
-        self.assertEqual(results_1, ["S1", "S2", "S3", "S4"])
-        self.group.genofile = "~/genofiles/g2_file"
-        result_2 = get_genofile_samplelist(self.dataset)
-        self.assertEqual(result_2, [])
-
-    @mock.patch("wqflask.marker_regression.run_mapping.data_set")
-    def test_if_geno_db_exists(self, mock_data_set):
-        mock_data_set.create_dataset.side_effect = [
-            AttributeSetter({}), Exception()]
-        results_no_error = geno_db_exists(self.dataset)
-        results_with_error = geno_db_exists(self.dataset)
-
-        self.assertEqual(mock_data_set.create_dataset.call_count, 2)
-        self.assertEqual(results_with_error, "False")
-        self.assertEqual(results_no_error, "True")
-
-    def test_trim_markers_for_figure(self):
-
-        markers = [{
-            "name": "MK1",
-            "chr": "C1",
-            "cM": "1",
-            "Mb": "12000",
-            "genotypes": [],
-            "dominance":"TT",
-            "additive":"VA",
-            "lod_score":0.5
-        },
-            {
-            "name": "MK2",
-            "chr": "C2",
-            "cM": "15",
-            "Mb": "10000",
-            "genotypes": [],
-            "lod_score":0.7
-        },
-            {
-            "name": "MK1",
-            "chr": "C3",
-            "cM": "45",
-            "Mb": "1",
-            "genotypes": [],
-            "dominance":"Tt",
-            "additive":"VE",
-            "lod_score":1
-        }]
-
-        marker_2 = [{
-            "name": "MK1",
-            "chr": "C1",
-            "cM": "1",
-            "Mb": "12000",
-            "genotypes": [],
-            "dominance":"TT",
-            "additive":"VA",
-            "p_wald":4.6
-        }]
-        results = trim_markers_for_figure(markers)
-        result_2 = trim_markers_for_figure(marker_2)
-        expected = [
-            {
-                "name": "MK1",
-                "chr": "C1",
-                "cM": "1",
-                "Mb": "12000",
-                "genotypes": [],
-                "dominance":"TT",
-                "additive":"VA",
-                "lod_score":0.5
-            },
-            {
-                "name": "MK1",
-                "chr": "C3",
-                "cM": "45",
-                "Mb": "1",
-                "genotypes": [],
-                "dominance":"Tt",
-                "additive":"VE",
-                "lod_score":1
-            }
-
-        ]
-        self.assertEqual(results, expected)
-        self.assertEqual(result_2, marker_2)
-
-    def test_export_mapping_results(self):
-        """test for exporting mapping results"""
-        datetime_mock = mock.Mock(wraps=datetime.datetime)
-        datetime_mock.now.return_value = datetime.datetime(
-            2019, 9, 1, 10, 12, 12)
-
-        markers = [{
-            "name": "MK1",
-            "chr": "C1",
-            "cM": "1",
-            "Mb": "12000",
-            "genotypes": [],
-            "dominance":"TT",
-            "additive":"VA",
-            "lod_score":3
-        },
-            {
-            "name": "MK2",
-            "chr": "C2",
-            "cM": "15",
-            "Mb": "10000",
-            "genotypes": [],
-            "lod_score":7
-        },
-            {
-            "name": "MK1",
-            "chr": "C3",
-            "cM": "45",
-            "Mb": "1",
-            "genotypes": [],
-            "dominance":"Tt",
-            "additive":"VE",
-            "lod_score":7
-        }]
-
-        with mock.patch("builtins.open", mock.mock_open()) as mock_open:
-
-            with mock.patch("wqflask.marker_regression.run_mapping.datetime.datetime", new=datetime_mock):
-                export_mapping_results(dataset=self.dataset, trait=self.trait, markers=markers,
-                                       results_path="~/results", mapping_scale="physic", score_type="-log(p)")
-
-                write_calls = [
-                    mock.call('Time/Date: 09/01/19 / 10:12:12\n'),
-                    mock.call('Population: Human GP1_\n'), mock.call(
-                        'Data Set: dataser_1\n'),
-                    mock.call('Gene Symbol: IGFI\n'), mock.call(
-                        'Location: X1 @ 123313 Mb\n'),
-                    mock.call('\n'), mock.call('Name,Chr,'),
-                    mock.call('Mb,-log(p)'), mock.call('Cm,-log(p)'),
-                    mock.call(',Additive'), mock.call(',Dominance'),
-                    mock.call('\n'), mock.call('MK1,C1,'),
-                    mock.call('12000,'), mock.call('1,'),
-                    mock.call('3'), mock.call(',VA'),
-                    mock.call(',TT'), mock.call('\n'),
-                    mock.call('MK2,C2,'), mock.call('10000,'),
-                    mock.call('15,'), mock.call('7'),
-                    mock.call('\n'), mock.call('MK1,C3,'),
-                    mock.call('1,'), mock.call('45,'),
-                    mock.call('7'), mock.call(',VE'),
-                    mock.call(',Tt')
-
-                ]
-                mock_open.assert_called_once_with("~/results", "w+")
-                filehandler = mock_open()
-                filehandler.write.assert_has_calls(write_calls)
-
-    @mock.patch("wqflask.marker_regression.run_mapping.random.choice")
-    def test_write_input_for_browser(self, mock_choice):
-        """test for writing input for browser"""
-        mock_choice.side_effect = ["F", "i", "l", "e", "s", "x"]
-        with mock.patch("builtins.open", mock.mock_open()) as mock_open:
-            expected = ['GP1__Filesx_GWAS', 'GP1__Filesx_ANNOT']
-
-            results = write_input_for_browser(
-                this_dataset=self.dataset, gwas_results={}, annotations={})
-            self.assertEqual(results, expected)
-
-    def test_get_perm_strata(self):
-        categorical_vars = ["C1", "C2", "W1"]
-        used_samples = ["S1", "S2"]
-        sample_list = AttributeSetter({"sample_attribute_values": {
-            "S1": {
-                "C1": "c1_value",
-                "C2": "c2_value",
-                "W1": "w1_value"
-
-            },
-            "S2": {
-                "W1": "w2_value",
-                "W2": "w2_value"
-
-            },
-            "S3": {
-
-                "C1": "c1_value",
-                "C2": "c2_value"
-
-            },
-
-        }})
-
-        results = get_perm_strata(this_trait={}, sample_list=sample_list,
-                                  categorical_vars=categorical_vars, used_samples=used_samples)
-        self.assertEqual(results, [2, 1])
-
-    def test_get_chr_length(self):
-        """test for getting chromosome length"""
-        chromosomes = AttributeSetter({"chromosomes": self.chromosomes})
-        dataset = AttributeSetter({"species": chromosomes})
-        results = get_chr_lengths(
-            mapping_scale="physic", mapping_method="reaper", dataset=dataset, qtl_results=[])
-        chr_lengths = []
-        for key, chromo in self.chromosomes.chromosomes.items():
-            chr_lengths.append({"chr": chromo.name, "size": chromo.length})
-
-        self.assertEqual(chr_lengths, results)
-
-        qtl_results = [{
-            "chr": "16",
-            "cM": "0.2"
-        },
-            {
-            "chr": "12",
-            "cM": "0.5"
-        },
-            {
-            "chr": "18",
-            "cM": "0.1"
-        },
-            {
-            "chr": "22",
-            "cM": "0.4"
-        },
-        ]
-
-        result_with_other_mapping_scale = get_chr_lengths(
-            mapping_scale="other", mapping_method="reaper", dataset=dataset, qtl_results=qtl_results)
-        expected_value = [{'chr': '1', 'size': '0'}, {
-            'chr': '16', 'size': '500000.0'}, {'chr': '18', 'size': '400000.0'}]
-
-        self.assertEqual(result_with_other_mapping_scale, expected_value)
-- 
cgit v1.2.3


From c96c1db5660367f1f86cfa76309c87866e79bf79 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Sat, 28 Nov 2020 16:23:53 +0300
Subject: modify test for plink mapping and  rqtl_mapping

---
 wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py | 3 ++-
 wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py  | 4 ++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py
index 428f45b9..5eec93f1 100644
--- a/wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py
@@ -14,8 +14,9 @@ class AttributeSetter:
             setattr(self, key, val)
 class TestPlinkMapping(unittest.TestCase):
 
+
     def test_build_line_list(self):
-        """testing for building line list"""
+        """test for building line list"""
         line_1 = "this is line one test"
         irregular_line = "  this     is an, irregular line     "
         exp_line1 = ["this", "is", "line", "one", "test"]
diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py
index 69f53721..c585f1df 100644
--- a/wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py
@@ -19,10 +19,10 @@ class TestRqtlMapping(unittest.TestCase):
 	@mock.patch("wqflask.marker_regression.rqtl_mapping.logger")
 	def test_get_trait_data(self,mock_logger,mock_db):
 		"""test for getting trait data_type return True"""
-		caller_value="""SELECT value FROM TraitMetadata WHERE type='trait_data_type'"""
+		query_value="""SELECT value FROM TraitMetadata WHERE type='trait_data_type'"""
 		mock_db.db.execute.return_value.fetchone.return_value=["""{"type":"trait_data_type","name":"T1","traid_id":"fer434f"}"""]
 		results=get_trait_data_type("traid_id")
-		mock_db.db.execute.assert_called_with(caller_value)
+		mock_db.db.execute.assert_called_with(query_value)
 		self.assertEqual(results,"fer434f")
 
 	def test_sanitize_rqtl_phenotype(self):
-- 
cgit v1.2.3


From 719ce04cefe84e62662bca1404776a3f55251cc5 Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Tue, 1 Dec 2020 23:33:29 +0300
Subject: Remove stale comment

---
 wqflask/wqflask/markdown_routes.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 601845d7..1e077dd1 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -26,8 +26,6 @@ look for it inside the file system """
     if md_content.status_code == 200:
 
         return markdown.markdown(md_content.content.decode("utf-8"), extensions=['tables'])
-    # TODO: Add fallback on our git server by checking the mirror.
-    # Content not available
     return (f"\nContent for {file_name} not available. "
             "Please check "
             "(here to see where content exists)"
-- 
cgit v1.2.3


From 6a2fb6cced98d79cd1ccbe6490a59393486fdf92 Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Tue, 1 Dec 2020 23:34:36 +0300
Subject: Add new function to fetch md file from sys paths

* wqflask/wqflask/markdown_routes.py: New imports.
(get_file_from_python_search_path): New function.
---
 wqflask/wqflask/markdown_routes.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 1e077dd1..e43b9860 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -4,6 +4,8 @@ Render pages from github, or if they are unavailable, look for it else where
 """
 import requests
 import markdown
+import os
+import sys
 
 from flask import Blueprint
 from flask import render_template
@@ -33,6 +35,14 @@ look for it inside the file system """
             "Please reach out to the gn2 team to have a look at this")
 
 
+def get_file_from_python_search_path(pathname_suffix):
+    cands = [os.path.join(d, pathname_suffix) for d in sys.path]
+    try:
+        return list(filter(os.path.exists, cands))[0]
+    except IndexError:
+        return None
+
+
 @glossary_blueprint.route('/')
 def glossary():
     return render_template(
-- 
cgit v1.2.3


From e75056aa81c9d66379a3b4823ded69f0781c5374 Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Tue, 1 Dec 2020 23:36:10 +0300
Subject: Fetch file from systempath when loading "/environments" route

* wqflask/wqflask/markdown_routes.py:
(render_markdown): Extend function to check whether "file_name" is to
be fetched remotely or locally in syspath.
(environments): Extend route to fetch from syspath if dependency file
exists there; otherwise fetch remotely from Github as a fallback.
---
 wqflask/wqflask/markdown_routes.py | 29 ++++++++++++++++++++++++++---
 1 file changed, 26 insertions(+), 3 deletions(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index e43b9860..59a465e7 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -18,16 +18,25 @@ policies_blueprint = Blueprint("policies_blueprint", __name__)
 facilities_blueprint = Blueprint("facilities_blueprint", __name__)
 
 
-def render_markdown(file_name):
+def render_markdown(file_name, is_remote_file=True):
     """Try to fetch the file name from Github and if that fails, try to
 look for it inside the file system """
     github_url = ("https://raw.githubusercontent.com/"
                   "genenetwork/gn-docs/master/")
 
+    if not is_remote_file:
+        text = ""
+        with open(file_name, "r", encoding="utf-8") as input_file:
+            text = input_file.read()
+        return markdown.markdown(text,
+                                 extensions=['tables'])
+
     md_content = requests.get(f"{github_url}{file_name}")
+
     if md_content.status_code == 200:
+        return markdown.markdown(md_content.content.decode("utf-8"),
+                                 extensions=['tables'])
 
-        return markdown.markdown(md_content.content.decode("utf-8"), extensions=['tables'])
     return (f"\nContent for {file_name} not available. "
             "Please check "
             "(here to see where content exists)"
@@ -59,7 +68,21 @@ def references():
 
 @environments_blueprint.route("/")
 def environments():
-    return render_template("environment.html", rendered_markdown=render_markdown("general/environments/environments.md")), 200
+    md_file = get_file_from_python_search_path("wqflask/DEPENDENCIES.md")
+    if md_file is not None:
+        return (
+            render_template("environment.html",
+                            rendered_markdown=render_markdown(
+                                md_file,
+                                is_remote_file=False)),
+            200
+        )
+    # Fallback: Fetch file from server
+    return (render_template(
+        "environment.html",
+        rendered_markdown=render_markdown(
+            "general/environments/environments.md")),
+            200)
 
 
 @links_blueprint.route("/")
-- 
cgit v1.2.3


From 668d6a97356030e0286365dad53c8d191c193290 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 2 Dec 2020 14:16:08 -0600
Subject: Made changes that should fix Heatmap errors + removed a console.log
 in panelutil.js

---
 wqflask/wqflask/static/new/javascript/lodheatmap.js | 6 ++++--
 wqflask/wqflask/static/new/javascript/panelutil.js  | 1 -
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/wqflask/wqflask/static/new/javascript/lodheatmap.js b/wqflask/wqflask/static/new/javascript/lodheatmap.js
index 965a1d53..b82c95ad 100644
--- a/wqflask/wqflask/static/new/javascript/lodheatmap.js
+++ b/wqflask/wqflask/static/new/javascript/lodheatmap.js
@@ -44,7 +44,9 @@ lodheatmap = function() {
       _ref = data.chrnames;
       for (_i = 0, _len = _ref.length; _i < _len; _i++) {
         chr = _ref[_i];
-        xLR[chr[0]] = getLeftRight(data.posByChr[chr[0]]);
+        if (data.posByChr[chr[0]].length > 0){
+          xLR[chr[0]] = getLeftRight(data.posByChr[chr[0]]);
+        }
       }
       zmin = 0;
       zmax = 0;
@@ -144,7 +146,7 @@ lodheatmap = function() {
       }).attr("stroke", "none").attr("stroke-width", "1").on("mouseover.paneltip", function(d) {
         yaxis.select("text#yaxis" + d.lodindex).attr("opacity", 1);
         d3.select(this).attr("stroke", "black");
-        return celltip.show(d);
+        return celltip.show(d, this);
       }).on("mouseout.paneltip", function(d) {
         yaxis.select("text#yaxis" + d.lodindex).attr("opacity", 0);
         d3.select(this).attr("stroke", "none");
diff --git a/wqflask/wqflask/static/new/javascript/panelutil.js b/wqflask/wqflask/static/new/javascript/panelutil.js
index 3c715c81..ea55a7cf 100644
--- a/wqflask/wqflask/static/new/javascript/panelutil.js
+++ b/wqflask/wqflask/static/new/javascript/panelutil.js
@@ -159,7 +159,6 @@ chrscales = function(data, width, chrGap, leftMargin, pad4heatmap, mappingScale)
 
     if (mappingScale == "morgan") {
         max_pos = d3.max(data.posByChr[chr[0]])
-        console.log("max_pos:", max_pos)
         data.xscale[chr[0]] = d3.scale.linear().domain([chrStart[i], max_pos]).range([data.chrStart[i], data.chrEnd[i]]);
     }
     else {
-- 
cgit v1.2.3


From c0fa6e81a0c93bb83e0753732cdcf05fb3290769 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 2 Dec 2020 14:25:08 -0600
Subject: Added index to search result export

---
 wqflask/wqflask/export_traits.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/export_traits.py b/wqflask/wqflask/export_traits.py
index 3a886537..47a2000f 100644
--- a/wqflask/wqflask/export_traits.py
+++ b/wqflask/wqflask/export_traits.py
@@ -54,7 +54,7 @@ def export_search_results_csv(targs):
         trait_ob = retrieve_trait_info(trait_ob, trait_ob.dataset, get_qtl_info=True)
         trait_list.append(trait_ob)
 
-    table_headers = ['Species', 'Group', 'Dataset', 'Record ID', 'Symbol', 'Description', 'ProbeTarget', 'PubMed_ID', 'Chr', 'Mb', 'Alias', 'Gene_ID', 'Homologene_ID', 'UniGene_ID', 'Strand_Probe', 'Probe_set_specificity', 'Probe_set_BLAT_score', 'Probe_set_BLAT_Mb_start', 'Probe_set_BLAT_Mb_end', 'QTL_Chr', 'QTL_Mb', 'Locus_at_Peak', 'Max_LRS', 'P_value_of_MAX', 'Mean_Expression']
+    table_headers = ['Index', 'Species', 'Group', 'Dataset', 'Record ID', 'Symbol', 'Description', 'ProbeTarget', 'PubMed_ID', 'Chr', 'Mb', 'Alias', 'Gene_ID', 'Homologene_ID', 'UniGene_ID', 'Strand_Probe', 'Probe_set_specificity', 'Probe_set_BLAT_score', 'Probe_set_BLAT_Mb_start', 'Probe_set_BLAT_Mb_end', 'QTL_Chr', 'QTL_Mb', 'Locus_at_Peak', 'Max_LRS', 'P_value_of_MAX', 'Mean_Expression']
 
     traits_by_group = sort_traits_by_group(trait_list)
 
@@ -77,7 +77,7 @@ def export_search_results_csv(targs):
 
         csv_rows.append(full_headers)
 
-        for trait in group_traits:
+        for i, trait in enumerate(group_traits):
             if getattr(trait, "symbol", None):
                 trait_symbol = getattr(trait, "symbol")
             elif getattr(trait, "abbreviation", None):
@@ -85,6 +85,7 @@ def export_search_results_csv(targs):
             else:
                 trait_symbol = "N/A"
             row_contents = [
+                i + 1,
                 trait.dataset.group.species,
                 trait.dataset.group.name,
                 trait.dataset.name,
-- 
cgit v1.2.3


From 429602ad29c0f04fc8142dd48bc3c75c1412be59 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 2 Dec 2020 14:27:39 -0600
Subject: Added trait URLs to search page export

---
 wqflask/wqflask/export_traits.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/export_traits.py b/wqflask/wqflask/export_traits.py
index 47a2000f..037de5a8 100644
--- a/wqflask/wqflask/export_traits.py
+++ b/wqflask/wqflask/export_traits.py
@@ -54,7 +54,7 @@ def export_search_results_csv(targs):
         trait_ob = retrieve_trait_info(trait_ob, trait_ob.dataset, get_qtl_info=True)
         trait_list.append(trait_ob)
 
-    table_headers = ['Index', 'Species', 'Group', 'Dataset', 'Record ID', 'Symbol', 'Description', 'ProbeTarget', 'PubMed_ID', 'Chr', 'Mb', 'Alias', 'Gene_ID', 'Homologene_ID', 'UniGene_ID', 'Strand_Probe', 'Probe_set_specificity', 'Probe_set_BLAT_score', 'Probe_set_BLAT_Mb_start', 'Probe_set_BLAT_Mb_end', 'QTL_Chr', 'QTL_Mb', 'Locus_at_Peak', 'Max_LRS', 'P_value_of_MAX', 'Mean_Expression']
+    table_headers = ['Index', 'URL', 'Species', 'Group', 'Dataset', 'Record ID', 'Symbol', 'Description', 'ProbeTarget', 'PubMed_ID', 'Chr', 'Mb', 'Alias', 'Gene_ID', 'Homologene_ID', 'UniGene_ID', 'Strand_Probe', 'Probe_set_specificity', 'Probe_set_BLAT_score', 'Probe_set_BLAT_Mb_start', 'Probe_set_BLAT_Mb_end', 'QTL_Chr', 'QTL_Mb', 'Locus_at_Peak', 'Max_LRS', 'P_value_of_MAX', 'Mean_Expression']
 
     traits_by_group = sort_traits_by_group(trait_list)
 
@@ -86,6 +86,7 @@ def export_search_results_csv(targs):
                 trait_symbol = "N/A"
             row_contents = [
                 i + 1,
+                "https://genenetwork.org/show_trait?trait_id=" + trait.name + "&dataset=" + trait.dataset.name,
                 trait.dataset.group.species,
                 trait.dataset.group.name,
                 trait.dataset.name,
-- 
cgit v1.2.3


From 63ec95384208a1d994d0a381e7ea8a307ca075e2 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 2 Dec 2020 14:33:36 -0600
Subject: Updated Funding line in search result export

---
 wqflask/wqflask/export_traits.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/export_traits.py b/wqflask/wqflask/export_traits.py
index 037de5a8..a2f25f20 100644
--- a/wqflask/wqflask/export_traits.py
+++ b/wqflask/wqflask/export_traits.py
@@ -44,7 +44,7 @@ def export_search_results_csv(targs):
         if targs['filter_term'] != "None":
             metadata.append(["Search Filter Terms: " + targs['filter_term']])
     metadata.append(["Exported Row Number: " + str(len(table_rows))])
-    metadata.append(["Funding for The GeneNetwork: NIAAA (U01AA13499, U24AA13513), NIDA, NIMH, and NIAAA (P20-DA21131), NCI MMHCC (U01CA105417), and NCRR (U01NR 105417)"])
+    metadata.append(["Funding for The GeneNetwork: NIGMS (R01 GM123489, 2017-2021), NIDA (P30 DA044223, 2017-2022), NIA (R01AG043930, 2013-2018), NIAAA (U01 AA016662, U01 AA013499, U24 AA013513, U01 AA014425, 2006-2017), NIDA/NIMH/NIAAA (P20-DA 21131, 2001-2012), NCI MMHCC (U01CA105417), NCRR/BIRN (U24 RR021760)
     metadata.append([])
 
     trait_list = []
-- 
cgit v1.2.3


From 1e7a2082a84e260b61349a77de4e8b936fae0123 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Wed, 2 Dec 2020 14:34:56 -0600
Subject: Missed a syntax typo in last commit

---
 wqflask/wqflask/export_traits.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/export_traits.py b/wqflask/wqflask/export_traits.py
index a2f25f20..b14c55db 100644
--- a/wqflask/wqflask/export_traits.py
+++ b/wqflask/wqflask/export_traits.py
@@ -44,7 +44,7 @@ def export_search_results_csv(targs):
         if targs['filter_term'] != "None":
             metadata.append(["Search Filter Terms: " + targs['filter_term']])
     metadata.append(["Exported Row Number: " + str(len(table_rows))])
-    metadata.append(["Funding for The GeneNetwork: NIGMS (R01 GM123489, 2017-2021), NIDA (P30 DA044223, 2017-2022), NIA (R01AG043930, 2013-2018), NIAAA (U01 AA016662, U01 AA013499, U24 AA013513, U01 AA014425, 2006-2017), NIDA/NIMH/NIAAA (P20-DA 21131, 2001-2012), NCI MMHCC (U01CA105417), NCRR/BIRN (U24 RR021760)
+    metadata.append(["Funding for The GeneNetwork: NIGMS (R01 GM123489, 2017-2021), NIDA (P30 DA044223, 2017-2022), NIA (R01AG043930, 2013-2018), NIAAA (U01 AA016662, U01 AA013499, U24 AA013513, U01 AA014425, 2006-2017), NIDA/NIMH/NIAAA (P20-DA 21131, 2001-2012), NCI MMHCC (U01CA105417), NCRR/BIRN (U24 RR021760)"])
     metadata.append([])
 
     trait_list = []
-- 
cgit v1.2.3


From a57241a160757ac737658b33ea7cc8ad670a0e61 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 3 Dec 2020 00:01:52 +0300
Subject: add hide  empty column in table script

---
 .../static/new/javascript/auto_hide_column.js      | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 wqflask/wqflask/static/new/javascript/auto_hide_column.js

diff --git a/wqflask/wqflask/static/new/javascript/auto_hide_column.js b/wqflask/wqflask/static/new/javascript/auto_hide_column.js
new file mode 100644
index 00000000..652b2ad7
--- /dev/null
+++ b/wqflask/wqflask/static/new/javascript/auto_hide_column.js
@@ -0,0 +1,22 @@
+
+$(function (){
+$("table th").each(function(index,col_th){
+	var filter_counter = 0;
+	// map each column to row ie  column i to ith element
+	var column_tds = $(this).closest('table').find('tr td:nth-child(' + (index + 1) + ')')
+	column_tds.each(function(j,col_td){
+		if(this.innerHTML==''||this.innerHTML=="N/A"){
+			filter_counter+=1;
+		}
+		else{
+			// break
+			return false;
+		}
+	})
+	if (filter_counter == ($('table tr').length - 1)){
+		$(this).hide();
+        column_tds.hide();
+
+	}
+})
+})
\ No newline at end of file
-- 
cgit v1.2.3


From 8746f2e93fe889ac0ba9e801b3684764fffa2945 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 3 Dec 2020 00:04:52 +0300
Subject: add table row index

---
 wqflask/wqflask/static/new/javascript/auto_hide_column.js | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/static/new/javascript/auto_hide_column.js b/wqflask/wqflask/static/new/javascript/auto_hide_column.js
index 652b2ad7..73b639dc 100644
--- a/wqflask/wqflask/static/new/javascript/auto_hide_column.js
+++ b/wqflask/wqflask/static/new/javascript/auto_hide_column.js
@@ -1,10 +1,10 @@
 
 $(function (){
-$("table th").each(function(index,col_th){
+$("table th").each(function(th_index,col_th){
 	var filter_counter = 0;
 	// map each column to row ie  column i to ith element
 	var column_tds = $(this).closest('table').find('tr td:nth-child(' + (index + 1) + ')')
-	column_tds.each(function(j,col_td){
+	column_tds.each(function(td_index,col_td){
 		if(this.innerHTML==''||this.innerHTML=="N/A"){
 			filter_counter+=1;
 		}
-- 
cgit v1.2.3


From f199a59587bad2b20c4122562eb707931b045669 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 3 Dec 2020 00:47:54 +0300
Subject: add support for multiple tables

---
 .../static/new/javascript/auto_hide_column.js      | 37 +++++++++++-----------
 1 file changed, 18 insertions(+), 19 deletions(-)

diff --git a/wqflask/wqflask/static/new/javascript/auto_hide_column.js b/wqflask/wqflask/static/new/javascript/auto_hide_column.js
index 73b639dc..14348ccd 100644
--- a/wqflask/wqflask/static/new/javascript/auto_hide_column.js
+++ b/wqflask/wqflask/static/new/javascript/auto_hide_column.js
@@ -1,22 +1,21 @@
+$(function(){
+	$("table").each(function(table){
+		$(this).find("th").each(function(col_index,col_th){
+			var filter_counter=0
+			var col_td=$(this).closest('table').find('tr td:nth-child(' + (col_index + 1) + ')')
+			col_td.each(function(td_index,col_td){
+				if(this.innerHTML==""||this.innerHTML=="N/A"){
+					filter_counter+=1
+				}
+				else{
+					return false
+				}
+			})
+			if (filter_counter==$(this).closest("table").find("tr").length-1){
+					$(this).hide();
+                   col_td.hide();
 
-$(function (){
-$("table th").each(function(th_index,col_th){
-	var filter_counter = 0;
-	// map each column to row ie  column i to ith element
-	var column_tds = $(this).closest('table').find('tr td:nth-child(' + (index + 1) + ')')
-	column_tds.each(function(td_index,col_td){
-		if(this.innerHTML==''||this.innerHTML=="N/A"){
-			filter_counter+=1;
-		}
-		else{
-			// break
-			return false;
-		}
+			}
+		})
 	})
-	if (filter_counter == ($('table tr').length - 1)){
-		$(this).hide();
-        column_tds.hide();
-
-	}
 })
-})
\ No newline at end of file
-- 
cgit v1.2.3


From 1cf0edb0719e4cf1c8410d8a910b199bb10180ef Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Thu, 3 Dec 2020 00:12:04 +0300
Subject: Remove edit button in environments page

* wqflask/wqflask/templates/environment.html: Delete edit button. The
dependency file is autogenerated from guix.
---
 wqflask/wqflask/templates/environment.html | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/wqflask/wqflask/templates/environment.html b/wqflask/wqflask/templates/environment.html
index 94b31464..34ebf36e 100644
--- a/wqflask/wqflask/templates/environment.html
+++ b/wqflask/wqflask/templates/environment.html
@@ -8,14 +8,6 @@
 
 {% block content %}
 
- 
    
-    
    
-        
-            Edit Text
-            
-        
-    
    
-
    
 
    
 	
    
    {{ rendered_markdown|safe }} 
    
-- 
cgit v1.2.3


From bf4b901fe7ebd02416b026974ed784970d8023dd Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Thu, 3 Dec 2020 00:23:23 +0300
Subject: Move css from environment.html to markdown.css

---
 wqflask/wqflask/static/new/css/markdown.css | 19 ++++++++++++++++++-
 wqflask/wqflask/templates/environment.html  | 16 ----------------
 2 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
index dca3e31d..e10dd9d3 100644
--- a/wqflask/wqflask/static/new/css/markdown.css
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -57,8 +57,25 @@
     word-spacing: 0.2em;
 }
 
+#markdown table {
+    width: 100%;
+}
+
+#markdown table,
+#markdown td,
+#markdown th {
+    border: solid 2px black;
+}
+
+#markdown td,
+#markdown th {
+    padding-top: 8px;
+    padding-bottom: 8px;
+    text-align: center;
+}
+
 @media(max-width:650px) {
     .container {
         width: 100vw;
     }
-}
\ No newline at end of file
+}
diff --git a/wqflask/wqflask/templates/environment.html b/wqflask/wqflask/templates/environment.html
index 34ebf36e..cd30e768 100644
--- a/wqflask/wqflask/templates/environment.html
+++ b/wqflask/wqflask/templates/environment.html
@@ -13,20 +13,4 @@
    
    {{ rendered_markdown|safe }} 
    
 
    
 
-
 {% endblock %}
-- 
cgit v1.2.3


From 66fb2fa6bf83b8d6e6d1cb7e159ea806bca4aebb Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Thu, 3 Dec 2020 00:28:56 +0300
Subject: Center-align the table header and left-align the table body

---
 wqflask/wqflask/static/new/css/markdown.css | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
index e10dd9d3..0c0309fb 100644
--- a/wqflask/wqflask/static/new/css/markdown.css
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -61,6 +61,15 @@
     width: 100%;
 }
 
+#markdown td {
+    padding: 1em;
+    text-align: left;
+}
+
+#markdown th {
+    text-align: center;
+}
+
 #markdown table,
 #markdown td,
 #markdown th {
@@ -71,7 +80,6 @@
 #markdown th {
     padding-top: 8px;
     padding-bottom: 8px;
-    text-align: center;
 }
 
 @media(max-width:650px) {
-- 
cgit v1.2.3


From 953727abb0b2c9075b486e3adfb13f029ebb00b1 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Thu, 3 Dec 2020 18:46:37 +0300
Subject: add datatable filter for empty  columns

---
 .../static/new/javascript/auto_hide_column.js      | 47 +++++++++++++---------
 1 file changed, 27 insertions(+), 20 deletions(-)

diff --git a/wqflask/wqflask/static/new/javascript/auto_hide_column.js b/wqflask/wqflask/static/new/javascript/auto_hide_column.js
index 14348ccd..94bb8a90 100644
--- a/wqflask/wqflask/static/new/javascript/auto_hide_column.js
+++ b/wqflask/wqflask/static/new/javascript/auto_hide_column.js
@@ -1,21 +1,28 @@
-$(function(){
-	$("table").each(function(table){
-		$(this).find("th").each(function(col_index,col_th){
-			var filter_counter=0
-			var col_td=$(this).closest('table').find('tr td:nth-child(' + (col_index + 1) + ')')
-			col_td.each(function(td_index,col_td){
-				if(this.innerHTML==""||this.innerHTML=="N/A"){
-					filter_counter+=1
-				}
-				else{
-					return false
-				}
-			})
-			if (filter_counter==$(this).closest("table").find("tr").length-1){
-					$(this).hide();
-                   col_td.hide();
+function filterDatable(datatable){
+        let visitedColumns=[]
+        let columnCount=datatable.columns().header().length;
+        let numberOfRows=datatable.data().length;
+        for (let i=0;ivisitedColumns.indexOf(item)<0);
+        return datatable.columns(emptyColumns).visible(false);
+
+}
-- 
cgit v1.2.3


From 3147f1eec3aee3b7844a3ac88cd58fafe53207b3 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 3 Dec 2020 13:43:53 -0600
Subject: Increased correlation table vertical height to 100vh

---
 wqflask/wqflask/templates/correlation_page.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html
index 6419b185..0e623cbb 100644
--- a/wqflask/wqflask/templates/correlation_page.html
+++ b/wqflask/wqflask/templates/correlation_page.html
@@ -557,7 +557,7 @@
                 "sDom": "itir",
                 "autoWidth": true,
                 "bSortClasses": false,
-                "scrollY": "50vh",
+                "scrollY": "100vh",
                 "scroller":  true,
                 "scrollCollapse": true
             }
-- 
cgit v1.2.3


From 5506ac96077db794c945cc1325300dead8073eea Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 3 Dec 2020 13:45:21 -0600
Subject: Convert trait/dataset names to strings, since phenotype names were
 being treated as integers

---
 wqflask/wqflask/correlation/show_corr_results.py | 1 +
 wqflask/wqflask/export_traits.py                 | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index 1b801b1d..51aa1622 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -487,6 +487,7 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
         results_dict['index'] = i + 1
         results_dict['trait_id'] = trait.name
         results_dict['dataset'] = trait.dataset.name
+        results_dict['hmac'] = hmac.data_hmac('{}:{}'.format(trait.name, trait.dataset.name))
         if target_dataset.type == "ProbeSet":
             results_dict['symbol'] = trait.symbol
             results_dict['description'] = "N/A"
diff --git a/wqflask/wqflask/export_traits.py b/wqflask/wqflask/export_traits.py
index b14c55db..6fb760e0 100644
--- a/wqflask/wqflask/export_traits.py
+++ b/wqflask/wqflask/export_traits.py
@@ -86,7 +86,7 @@ def export_search_results_csv(targs):
                 trait_symbol = "N/A"
             row_contents = [
                 i + 1,
-                "https://genenetwork.org/show_trait?trait_id=" + trait.name + "&dataset=" + trait.dataset.name,
+                "https://genenetwork.org/show_trait?trait_id=" + str(trait.name) + "&dataset=" + str(trait.dataset.name),
                 trait.dataset.group.species,
                 trait.dataset.group.name,
                 trait.dataset.name,
-- 
cgit v1.2.3


From fabb29b6f47c11d3a1eeeb4cb94c8362e282dca3 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 3 Dec 2020 13:46:18 -0600
Subject: Converted the submit_traits_to_export_or_bnw function to using
 DataTables API, since the JQuery it was previously using didn't work with
 Scroller

---
 .../static/new/javascript/search_results.js        | 27 +++++++++++++---------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/wqflask/wqflask/static/new/javascript/search_results.js b/wqflask/wqflask/static/new/javascript/search_results.js
index 86660126..27d89867 100644
--- a/wqflask/wqflask/static/new/javascript/search_results.js
+++ b/wqflask/wqflask/static/new/javascript/search_results.js
@@ -157,18 +157,23 @@ $(function() {
     });
     table_dict['headers'] = headers;
 
-    rows = [];
-    trait_table.find('tbody tr').each(function (i, tr) {
-      if (trait_table.find('input[name="searchResult"]:checked').length > 0) {
-        if ($(this).find('input[name="searchResult"]').is(':checked')){
-          rows.push($(this).find('input[name="searchResult"]:checked').val())
-        }
-      }
-      else {
-        rows.push($(this).find('input[name="searchResult"]').val())
+    selected_rows = [];
+    all_rows = []; //ZS: If no rows are checked, export all
+    table_api = $('#' + table_name).DataTable();
+    check_cells = table_api.column(0).nodes().to$();
+    for (let i = 0; i < check_cells.length; i++) {
+      this_node = check_cells[i].childNodes[0];
+      all_rows.push(this_node.value)
+      if (this_node.checked){
+        selected_rows.push(this_node.value)
       }
-    });
-    table_dict['rows'] = rows;
+    }
+
+    if (selected_rows.length > 0){
+      table_dict['rows'] = selected_rows;
+    } else {
+      table_dict['rows'] = all_rows;
+    }
 
     json_table_dict = JSON.stringify(table_dict);
     $('input[name=export_data]').val(json_table_dict);
-- 
cgit v1.2.3


From f90ae0eb938b48a6467223d44816d9b6522280cf Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 3 Dec 2020 14:08:36 -0600
Subject: Fixed select_all and deselect_all to use DataTables API so they work
 with any tables that use Scroller

---
 .../static/new/javascript/search_results.js        | 35 ++++++++++++++--------
 1 file changed, 22 insertions(+), 13 deletions(-)

diff --git a/wqflask/wqflask/static/new/javascript/search_results.js b/wqflask/wqflask/static/new/javascript/search_results.js
index 27d89867..3319d2cb 100644
--- a/wqflask/wqflask/static/new/javascript/search_results.js
+++ b/wqflask/wqflask/static/new/javascript/search_results.js
@@ -3,22 +3,31 @@ $(function() {
 
   checked_traits = null;
   select_all = function() {
-    console.log("selected_all");
-    $(".trait_checkbox").each(function() {
-        $(this).prop('checked', true);
-        if (!$(this).closest('tr').hasClass('selected')) {
-            $(this).closest('tr').addClass('selected')
-        }
-    });
+    table_api = $('#trait_table').DataTable();
+
+    check_cells = table_api.column(0).nodes().to$();
+    for (let i = 0; i < check_cells.length; i++) {
+      check_cells[i].childNodes[0].checked = true;
+    }
+
+    check_rows = table_api.rows().nodes();
+    for (let i =0; i < check_rows.length; i++) {
+      check_rows[i].classList.add("selected")
+    }
   };
 
   deselect_all = function() {
-    $(".trait_checkbox").each(function() {
-        $(this).prop('checked', false);
-        if ($(this).closest('tr').hasClass('selected')) {
-            $(this).closest('tr').removeClass('selected')
-        }
-    });
+    table_api = $('#trait_table').DataTable();
+
+    check_cells = table_api.column(0).nodes().to$();
+    for (let i = 0; i < check_cells.length; i++) {
+      check_cells[i].childNodes[0].checked = false;
+    }
+
+    check_rows = table_api.rows().nodes();
+    for (let i =0; i < check_rows.length; i++) {
+      check_rows[i].classList.remove("selected")
+    }
   };
 
   invert = function() {
-- 
cgit v1.2.3


From e087a5ed176d3e935ca15c7d066b026dec653a6f Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 3 Dec 2020 14:13:55 -0600
Subject: Changed invert function to use DataTables API so it can also work
 with Scroller

---
 .../static/new/javascript/search_results.js        | 34 ++++++++++++----------
 1 file changed, 19 insertions(+), 15 deletions(-)

diff --git a/wqflask/wqflask/static/new/javascript/search_results.js b/wqflask/wqflask/static/new/javascript/search_results.js
index 3319d2cb..5e8277b3 100644
--- a/wqflask/wqflask/static/new/javascript/search_results.js
+++ b/wqflask/wqflask/static/new/javascript/search_results.js
@@ -31,21 +31,25 @@ $(function() {
   };
 
   invert = function() {
-    $(".trait_checkbox").each(function() {
-        if ($(this).prop('checked') == true) {
-            $(this).prop('checked', false)
-        }
-        else {
-            $(this).prop('checked', true)
-        }
-
-        if ($(this).closest('tr').hasClass('selected')) {
-            $(this).closest('tr').removeClass('selected')
-        }
-        else {
-            $(this).closest('tr').addClass('selected')
-        }
-    });
+    table_api = $('#trait_table').DataTable();
+
+    check_cells = table_api.column(0).nodes().to$();
+    for (let i = 0; i < check_cells.length; i++) {
+      if (check_cells[i].childNodes[0].checked){
+        check_cells[i].childNodes[0].checked = false;
+      } else {
+        check_cells[i].childNodes[0].checked = true;
+      }
+    }
+
+    check_rows = table_api.rows().nodes();
+    for (let i =0; i < check_rows.length; i++) {
+      if (check_rows[i].classList.contains("selected")){
+        check_rows[i].classList.remove("selected")
+      } else {
+        check_rows[i].classList.add("selected")
+      }
+    }
   };
 
   $('#searchbox').keyup(function(){
-- 
cgit v1.2.3


From 244317daae8fb9a242b7d62c2e8f6e3a40c4ba44 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 3 Dec 2020 14:14:09 -0600
Subject: Added Invert button to search result page

---
 wqflask/wqflask/templates/search_result_page.html | 1 +
 1 file changed, 1 insertion(+)

diff --git a/wqflask/wqflask/templates/search_result_page.html b/wqflask/wqflask/templates/search_result_page.html
index 2a8d6931..8a467bfb 100644
--- a/wqflask/wqflask/templates/search_result_page.html
+++ b/wqflask/wqflask/templates/search_result_page.html
@@ -129,6 +129,7 @@
               {% endif %}
               
               
+              
               
               
               
-- 
cgit v1.2.3


From 64bfeadac33e6d22297714544cd96ef16677fe16 Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Thu, 3 Dec 2020 23:59:14 +0300
Subject: Display d3js chord dependency diagram of gn2 dependenices

* wqflask/wqflask/markdown_routes.py: Add new bs4 import.
(references): Filter out javascript from the guix-generated d3js html
file and pass it to the jinja template.
* wqflask/wqflask/static/new/css/markdown.css: New styles for the
graph content.
* wqflask/wqflask/templates/environment.html: New graph content.
---
 wqflask/wqflask/markdown_routes.py          |  14 +++
 wqflask/wqflask/static/new/css/markdown.css |  14 +++
 wqflask/wqflask/templates/environment.html  | 134 ++++++++++++++++++++++++++++
 3 files changed, 162 insertions(+)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 59a465e7..183f4caa 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -7,6 +7,8 @@ import markdown
 import os
 import sys
 
+from bs4 import BeautifulSoup
+
 from flask import Blueprint
 from flask import render_template
 
@@ -68,10 +70,21 @@ def references():
 
 @environments_blueprint.route("/")
 def environments():
+
     md_file = get_file_from_python_search_path("wqflask/DEPENDENCIES.md")
+    svg_file = get_file_from_python_search_path(
+        "wqflask/dependency-graph.html")
+    svg_data = None
+    if svg_file:
+        with open(svg_file, 'r') as f:
+            svg_data = "".join(
+                BeautifulSoup(f.read(),
+                              'lxml').body.script.contents)
+
     if md_file is not None:
         return (
             render_template("environment.html",
+                            svg_data=svg_data,
                             rendered_markdown=render_markdown(
                                 md_file,
                                 is_remote_file=False)),
@@ -80,6 +93,7 @@ def environments():
     # Fallback: Fetch file from server
     return (render_template(
         "environment.html",
+        svg_data=None,
         rendered_markdown=render_markdown(
             "general/environments/environments.md")),
             200)
diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
index 0c0309fb..38d664e2 100644
--- a/wqflask/wqflask/static/new/css/markdown.css
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -57,6 +57,20 @@
     word-spacing: 0.2em;
 }
 
+.graph-legend h1 {
+    text-align: center;
+}
+
+.graph-legend,
+#guix-graph {
+    width: 90%;
+    margin: 10px auto;
+}
+
+#guix-graph {
+    border: solid 2px black;
+}
+
 #markdown table {
     width: 100%;
 }
diff --git a/wqflask/wqflask/templates/environment.html b/wqflask/wqflask/templates/environment.html
index cd30e768..5fe01dad 100644
--- a/wqflask/wqflask/templates/environment.html
+++ b/wqflask/wqflask/templates/environment.html
@@ -13,4 +13,138 @@
    
    {{ rendered_markdown|safe }} 
    
 
    
 
+{% if svg_data %}
+
+
    
+    
    Chord dependency Graph of Genenetwork2
    
+    Graph generated from genenetwork.scm. You can zoom in and out within the bounding box.
+
    
+
+
    
+{% endif %}
+
+{% endblock %}
+
+{% block js %}
+
+{% if svg_data %}
+
+
+{% endif %}
+
 {% endblock %}
-- 
cgit v1.2.3


From 812e9e84ca98c13ba3e378b169dce235feef5c15 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Thu, 3 Dec 2020 17:09:05 -0600
Subject: Fixed View Collection table drawCallback to correctly deal with check
 boxes and the Copy/Delete buttons and fixed an issue where extra white-space
 on either side of the checkboxes was causing some JS to not work correctly

---
 wqflask/wqflask/templates/collections/view.html | 33 ++++++++++++++-----------
 1 file changed, 18 insertions(+), 15 deletions(-)

diff --git a/wqflask/wqflask/templates/collections/view.html b/wqflask/wqflask/templates/collections/view.html
index e37f8104..ccec495b 100644
--- a/wqflask/wqflask/templates/collections/view.html
+++ b/wqflask/wqflask/templates/collections/view.html
@@ -73,7 +73,7 @@
                 
    
                     
                     
-                    
+                    
                     
                     
                     
@@ -81,7 +81,7 @@
                     
                     
                     
-                    
+                    
                     
                 
    
             
    
@@ -109,10 +109,7 @@
                     
                     {% for this_trait in trait_obs %}
                         
-                            
-                                
-                            
+                            
                             {{ loop.index }}
                             {{ this_trait.dataset.fullname }}
                             
@@ -178,15 +175,21 @@
 
     
     
-    
+    
     
     
     
@@ -259,17 +261,14 @@
         $(document).ready( function () {
             table_conf = {
                 buttons: [
-                    {
-                        extend: 'columnsToggle',
-                        columns: function( idx, data, node ) {
-                          if (idx != 0) {
-                            return true;
-                          } else {
-                            return false;
-                          }
-                        },
-                        postfixButtons: [ 'colvisRestore' ]
+                  {
+                    extend: 'csvHtml5',
+                    text: 'Download ',
+                    className: 'btn btn-default',
+                    exportOptions: {
+                      columns: 'th:not(:first-child)'
                     }
+                  }
                 ],
                 'drawCallback': function( settings ) {
                       $('#trait_table tr').off().on("click", function(event) {
@@ -544,6 +543,10 @@
 
             trait_table = $('#trait_table').DataTable(table_conf);
 
+            trait_table.buttons().container().appendTo('#export_options')
+
+            $('.buttons-csv').removeClass('dt-button')
+
             trait_table.on( 'order.dt search.dt draw.dt', function () {
                 trait_table.column(1, {search:'applied', order:'applied'}).nodes().each( function (cell, i) {
                 cell.innerHTML = i+1;
-- 
cgit v1.2.3


From 4bc3054570b1869e484d5c61ea1f64f684a654dc Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 4 Dec 2020 14:01:31 -0600
Subject: Limited the mean to 3 digits on correlation page

---
 wqflask/wqflask/correlation/show_corr_results.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index 51aa1622..0c1cea0e 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -498,7 +498,7 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
             if bool(trait.description_display):
                 results_dict['description'] = trait.description_display
             if bool(trait.mean):
-                results_dict['mean'] = float(trait.mean)
+                results_dict['mean'] = f"{float(trait.mean):.3f}"
             if trait.LRS_score_repr != "N/A":
                 results_dict['lrs_score'] = f"{float(trait.LRS_score_repr):.1f}"
             results_dict['lrs_location'] = trait.LRS_location_repr
-- 
cgit v1.2.3


From 2f96a2579c8ebcfc8ec763675b81811d7ddf4068 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 4 Dec 2020 14:01:49 -0600
Subject: Gave a margin to the More Options field so it doesn't look bad

---
 wqflask/wqflask/templates/correlation_page.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html
index 5093ebfa..58285509 100644
--- a/wqflask/wqflask/templates/correlation_page.html
+++ b/wqflask/wqflask/templates/correlation_page.html
@@ -89,7 +89,7 @@
             
    
             {% if target_dataset.type != "Publish" %}
             
    
-            
    
+            
    
               
             
    
             
    
-- 
cgit v1.2.3


From 48969878b1321e80b55e06ec9367ba428bb2fc36 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 4 Dec 2020 14:03:53 -0600
Subject: Fixed typo

---
 wqflask/wqflask/templates/correlation_page.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html
index 58285509..06ee9056 100644
--- a/wqflask/wqflask/templates/correlation_page.html
+++ b/wqflask/wqflask/templates/correlation_page.html
@@ -89,7 +89,7 @@
             
    
             {% if target_dataset.type != "Publish" %}
             
    
-            
    
+            
    
               
             
    
             
    
-- 
cgit v1.2.3


From 0dfdbcbb83ffe9b349c870d02a5812bcb296a74b Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 4 Dec 2020 14:57:02 -0600
Subject: Added option to toggle columns on show_trait page, including case
 attribute columns

---
 wqflask/wqflask/templates/show_trait.html          | 27 +++++++++++++++++++
 .../wqflask/templates/show_trait_edit_data.html    | 30 ++++++++++++++++++++++
 2 files changed, 57 insertions(+)

diff --git a/wqflask/wqflask/templates/show_trait.html b/wqflask/wqflask/templates/show_trait.html
index 7b74a3f6..3923ad2f 100644
--- a/wqflask/wqflask/templates/show_trait.html
+++ b/wqflask/wqflask/templates/show_trait.html
@@ -221,6 +221,33 @@
                   primary_table.search($(this).val()).draw();
                 } );
 
+                $('.toggle-vis').on('click', function (e) {
+                    e.preventDefault();
+
+                    function toggle_column(column) {
+                        //ZS: Toggle column visibility
+                        column.visible( ! column.visible() );
+                        if (column.visible()){
+                            $(this).removeClass("active");
+                        } else {
+                            $(this).addClass("active");
+                        }
+                    }
+
+                    // Get the column API object
+                    var target_cols = $(this).attr('data-column').split(",")
+                    for (let i = 0; i < target_cols.length; i++){
+                        console.log("THE COL:", target_cols[i])
+                        var column = primary_table.column( target_cols[i] );
+                        toggle_column(column);
+
+                        {% if sample_groups|length != 1 %}
+                        var column2 = other_table.column( target_cols[i] );
+                        toggle_column(column2);
+                        {% endif %}
+                    }
+                } );
+
                 {% if sample_groups|length != 1 %}
                 $('#other_searchbox').on( 'keyup', function () {
                   other_table.search($(this).val()).draw();
diff --git a/wqflask/wqflask/templates/show_trait_edit_data.html b/wqflask/wqflask/templates/show_trait_edit_data.html
index 2e730248..4ad11a5e 100644
--- a/wqflask/wqflask/templates/show_trait_edit_data.html
+++ b/wqflask/wqflask/templates/show_trait_edit_data.html
@@ -1,6 +1,36 @@
 
    
     {% for sample_type in sample_groups %}
       
    
+        {% if loop.index == 1 %}
+        Show/Hide Columns:
+        
    
+        
+        
+        
+        {% if sample_groups[0].se_exists %}
+        
+        {% if has_num_cases %}
+        
+        {% set attr_start_pos = 7 %}
+        {% else %}
+        {% set attr_start_pos = 6 %}
+        {% endif %}
+        {% else %}
+        {% if has_num_cases %}
+        
+        {% set attr_start_pos = 5 %}
+        {% else %}
+        {% set attr_start_pos = 4 %}
+        {% endif %}
+        {% endif %}
+        {% if sample_groups[0].attributes %}
+        {% for attribute in sample_groups[0].attributes %}
+        
+        {% endfor %}
+        {% endif %}
+        
    
+        
    
+        {% endif %}
         
    
           
         
    
-- 
cgit v1.2.3


From 75ef9f817f8b80f6d4bd732bfede2ad575a50fab Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 4 Dec 2020 15:18:18 -0600
Subject: Fixed issue where chromosome names weren't being fetched correctly,
 causing an error in GEMMA

---
 wqflask/wqflask/marker_regression/gemma_mapping.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/marker_regression/gemma_mapping.py b/wqflask/wqflask/marker_regression/gemma_mapping.py
index 02f91a32..16e639dc 100644
--- a/wqflask/wqflask/marker_regression/gemma_mapping.py
+++ b/wqflask/wqflask/marker_regression/gemma_mapping.py
@@ -31,7 +31,8 @@ def run_gemma(this_trait, this_dataset, samples, vals, covariates, use_loco, maf
       gwa_output_filename = this_dataset.group.name + "_GWA_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
 
       this_chromosomes = this_dataset.species.chromosomes.chromosomes
-      this_chromosomes_name=[chromosome.name for chromosome in this_chromosomes]
+      this_chromosomes_name=[this_chromosomes[chromosome].name for chromosome in this_chromosomes]
+
 
       chr_list_string=",".join(this_chromosomes_name)
       if covariates != "":
-- 
cgit v1.2.3


From d76bb8ed8fb7a015e7563f64b6b3d6760cf874d8 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 4 Dec 2020 15:19:55 -0600
Subject: Removed some logging statements from gemma_mapping.py

---
 wqflask/wqflask/marker_regression/gemma_mapping.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/wqflask/wqflask/marker_regression/gemma_mapping.py b/wqflask/wqflask/marker_regression/gemma_mapping.py
index 16e639dc..61e4897c 100644
--- a/wqflask/wqflask/marker_regression/gemma_mapping.py
+++ b/wqflask/wqflask/marker_regression/gemma_mapping.py
@@ -46,7 +46,7 @@ def run_gemma(this_trait, this_dataset, samples, vals, covariates, use_loco, maf
                                                                                           genofile_name,
                                                                                           TEMPDIR,
                                                                                           k_output_filename)
-          logger.debug("k_command:" + generate_k_command)
+
           os.system(generate_k_command)
 
           gemma_command = GEMMA_WRAPPER_COMMAND + ' --json --loco --input %s/gn2/%s.json -- ' % (TEMPDIR, k_output_filename) + GEMMAOPTS + ' -g %s/%s_geno.txt -p %s/gn2/%s.txt' % (flat_files('genotype/bimbam'),
@@ -78,7 +78,6 @@ def run_gemma(this_trait, this_dataset, samples, vals, covariates, use_loco, maf
                                                                                          TEMPDIR,
                                                                                          k_output_filename)
 
-          logger.debug("k_command:" + generate_k_command)
           os.system(generate_k_command)
 
           gemma_command = GEMMA_WRAPPER_COMMAND + ' --json --input %s/gn2/%s.json -- ' % (TEMPDIR, k_output_filename) + GEMMAOPTS + ' -a %s/%s_snps.txt -lmm 2 -g %s/%s_geno.txt -p %s/gn2/%s.txt' % (flat_files('genotype/bimbam'),
@@ -94,8 +93,6 @@ def run_gemma(this_trait, this_dataset, samples, vals, covariates, use_loco, maf
           else:
               gemma_command += ' > %s/gn2/%s.json' % (TEMPDIR, gwa_output_filename)
 
-
-      logger.debug("gemma_command:" + gemma_command)
       os.system(gemma_command)
     else:
       gwa_output_filename = output_files
-- 
cgit v1.2.3


From 6b885b0b4dcdfed744b1996bd767bb9f20fdf633 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Sat, 5 Dec 2020 13:35:43 +0300
Subject: modify tests for run_gemma

---
 .../unit/wqflask/marker_regression/test_gemma_mapping.py      | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
index 5b621264..3d00dd7c 100644
--- a/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
@@ -43,18 +43,18 @@ class TestGemmaMapping(unittest.TestCase):
     @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMA_WRAPPER_COMMAND", "ghc")
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data/")
     @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.logger")
     @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files")
     @mock.patch("wqflask.marker_regression.gemma_mapping.gen_covariates_file")
     @mock.patch("wqflask.marker_regression.run_mapping.random.choice")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
     @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file")
-    def test_run_gemma_firstrun_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco):
+    def test_run_gemma_firstrun_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files,mock_parse_loco):
         """add tests for run_gemma where first run is set to true"""
-        chromosomes = []
+        this_chromosomes={}
         for i in range(1, 5):
-            chromosomes.append(AttributeSetter({"name": f"CH{i}"}))
-        chromo = AttributeSetter({"chromosomes": chromosomes})
+            this_chromosomes[f'CH{i}']=(AttributeSetter({"name": f"CH{i}"}))
+        chromo = AttributeSetter({"chromosomes": this_chromosomes})
+
         dataset_group = MockGroup(
             {"name": "GP1", "genofile": "file_geno"})
         dataset = AttributeSetter({"group": dataset_group, "name": "dataset1_name",
@@ -76,7 +76,6 @@ class TestGemmaMapping(unittest.TestCase):
         mock_parse_loco.assert_called_once_with(dataset, "GP1_GWA_RRRRRR")
         mock_os.path.isfile.assert_called_once_with(
             ('/home/user/imgfile_output.assoc.txt'))
-        self.assertEqual(mock_logger.debug.call_count, 2)
         self.assertEqual(mock_flat_files.call_count, 4)
         self.assertEqual(results, ([], "GP1_GWA_RRRRRR"))
 
-- 
cgit v1.2.3


From 54fc88f3f0f4966608fccdba76b49871eef50553 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Sat, 5 Dec 2020 13:43:23 +0300
Subject: add pep8 formatting

---
 wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
index 3d00dd7c..b8c13ab4 100644
--- a/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
@@ -53,12 +53,12 @@ class TestGemmaMapping(unittest.TestCase):
         this_chromosomes={}
         for i in range(1, 5):
             this_chromosomes[f'CH{i}']=(AttributeSetter({"name": f"CH{i}"}))
-        chromo = AttributeSetter({"chromosomes": this_chromosomes})
+        chromosomes = AttributeSetter({"chromosomes": this_chromosomes})
 
         dataset_group = MockGroup(
             {"name": "GP1", "genofile": "file_geno"})
         dataset = AttributeSetter({"group": dataset_group, "name": "dataset1_name",
-                                   "species": AttributeSetter({"chromosomes": chromo})})
+                                   "species": AttributeSetter({"chromosomes": chromosomes})})
         trait = AttributeSetter({"name": "trait1"})
         samples = []
         mock_gen_pheno_txt.return_value = None
-- 
cgit v1.2.3


From a865a2d490722e866c847a996439ed31253fea8a Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Sun, 6 Dec 2020 15:03:27 +0300
Subject: Add svg graph of inputs

* wqflask/wqflask/markdown_routes.py: New import:
"send_from_directory"
(svg_graph): New function that adds route for svg_graph.
* wqflask/wqflask/static/new/css/markdown.css: New style rules for svg
graph.
* wqflask/wqflask/templates/environment.html: Add new section for
displaying svg graph.
---
 wqflask/wqflask/markdown_routes.py          | 8 ++++++++
 wqflask/wqflask/static/new/css/markdown.css | 7 ++++++-
 wqflask/wqflask/templates/environment.html  | 7 +++++++
 3 files changed, 21 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py
index 3b60bc96..ebf75807 100644
--- a/wqflask/wqflask/markdown_routes.py
+++ b/wqflask/wqflask/markdown_routes.py
@@ -9,6 +9,7 @@ import sys
 
 from bs4 import BeautifulSoup
 
+from flask import send_from_directory
 from flask import Blueprint
 from flask import render_template
 
@@ -99,6 +100,13 @@ def environments():
             200)
 
 
+@environments_blueprint.route('/svg-dependency-graph')
+def svg_graph():
+    directory, file_name, _ = get_file_from_python_search_path(
+            "wqflask/dependency-graph.svg").partition("dependency-graph.svg")
+    return send_from_directory(directory, file_name)
+
+
 @links_blueprint.route("/")
 def links():
     return render_template(
diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css
index 38d664e2..859fe7fc 100644
--- a/wqflask/wqflask/static/new/css/markdown.css
+++ b/wqflask/wqflask/static/new/css/markdown.css
@@ -62,7 +62,8 @@
 }
 
 .graph-legend,
-#guix-graph {
+#guix-graph,
+#guix-svg-graph{
     width: 90%;
     margin: 10px auto;
 }
@@ -71,6 +72,10 @@
     border: solid 2px black;
 }
 
+#guix-svg-graph img {
+    width: 100%;
+}
+
 #markdown table {
     width: 100%;
 }
diff --git a/wqflask/wqflask/templates/environment.html b/wqflask/wqflask/templates/environment.html
index 5fe01dad..85133ac3 100644
--- a/wqflask/wqflask/templates/environment.html
+++ b/wqflask/wqflask/templates/environment.html
@@ -21,6 +21,13 @@
 
    
 
 
    
+
+
+
+
    
+    
    The dependency graph is shown below
    
+    Dependency graph of the tools needed to build python3-genenetwork2
+
    
 {% endif %}
 
 {% endblock %}
-- 
cgit v1.2.3


From efb6a41e7d712407f276e21f9ede3198d7e734dc Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Sat, 5 Dec 2020 13:35:43 +0300
Subject: modify tests for run_gemma

---
 .../unit/wqflask/marker_regression/test_gemma_mapping.py      | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
index 5b621264..3d00dd7c 100644
--- a/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
@@ -43,18 +43,18 @@ class TestGemmaMapping(unittest.TestCase):
     @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMA_WRAPPER_COMMAND", "ghc")
     @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data/")
     @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output")
-    @mock.patch("wqflask.marker_regression.gemma_mapping.logger")
     @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files")
     @mock.patch("wqflask.marker_regression.gemma_mapping.gen_covariates_file")
     @mock.patch("wqflask.marker_regression.run_mapping.random.choice")
     @mock.patch("wqflask.marker_regression.gemma_mapping.os")
     @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file")
-    def test_run_gemma_firstrun_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco):
+    def test_run_gemma_firstrun_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files,mock_parse_loco):
         """add tests for run_gemma where first run is set to true"""
-        chromosomes = []
+        this_chromosomes={}
         for i in range(1, 5):
-            chromosomes.append(AttributeSetter({"name": f"CH{i}"}))
-        chromo = AttributeSetter({"chromosomes": chromosomes})
+            this_chromosomes[f'CH{i}']=(AttributeSetter({"name": f"CH{i}"}))
+        chromo = AttributeSetter({"chromosomes": this_chromosomes})
+
         dataset_group = MockGroup(
             {"name": "GP1", "genofile": "file_geno"})
         dataset = AttributeSetter({"group": dataset_group, "name": "dataset1_name",
@@ -76,7 +76,6 @@ class TestGemmaMapping(unittest.TestCase):
         mock_parse_loco.assert_called_once_with(dataset, "GP1_GWA_RRRRRR")
         mock_os.path.isfile.assert_called_once_with(
             ('/home/user/imgfile_output.assoc.txt'))
-        self.assertEqual(mock_logger.debug.call_count, 2)
         self.assertEqual(mock_flat_files.call_count, 4)
         self.assertEqual(results, ([], "GP1_GWA_RRRRRR"))
 
-- 
cgit v1.2.3


From 7e0f91dec563f08671c772e34b85e0edc14195cb Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Sat, 5 Dec 2020 13:43:23 +0300
Subject: add pep8 formatting

---
 wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
index 3d00dd7c..b8c13ab4 100644
--- a/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
+++ b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py
@@ -53,12 +53,12 @@ class TestGemmaMapping(unittest.TestCase):
         this_chromosomes={}
         for i in range(1, 5):
             this_chromosomes[f'CH{i}']=(AttributeSetter({"name": f"CH{i}"}))
-        chromo = AttributeSetter({"chromosomes": this_chromosomes})
+        chromosomes = AttributeSetter({"chromosomes": this_chromosomes})
 
         dataset_group = MockGroup(
             {"name": "GP1", "genofile": "file_geno"})
         dataset = AttributeSetter({"group": dataset_group, "name": "dataset1_name",
-                                   "species": AttributeSetter({"chromosomes": chromo})})
+                                   "species": AttributeSetter({"chromosomes": chromosomes})})
         trait = AttributeSetter({"name": "trait1"})
         samples = []
         mock_gen_pheno_txt.return_value = None
-- 
cgit v1.2.3


From 7a35308b11a004f1c0899bdb2a2b982597869808 Mon Sep 17 00:00:00 2001
From: BonfaceKilz
Date: Sun, 6 Dec 2020 19:33:33 +0300
Subject: Add descriptive paragraph for svg graph

* wqflask/wqflask/templates/environment.html: New paragraph.
---
 wqflask/wqflask/templates/environment.html | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/wqflask/wqflask/templates/environment.html b/wqflask/wqflask/templates/environment.html
index 85133ac3..89e805ce 100644
--- a/wqflask/wqflask/templates/environment.html
+++ b/wqflask/wqflask/templates/environment.html
@@ -26,6 +26,9 @@
 
 
    
     
    The dependency graph is shown below
    
+
+    
    To explore this image SVG you may want to open it in new browser page and zoom in. Or use an SVG viewing application.
    
+
     Dependency graph of the tools needed to build python3-genenetwork2
 
    
 {% endif %}
-- 
cgit v1.2.3


From 18d01454548f7fb30f6fc24d9d8fb38840e76901 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Mon, 7 Dec 2020 12:18:26 +0300
Subject: replace datatatable/jquery.js in templates

---
 wqflask/wqflask/templates/admin/create_group.html      | 4 +++-
 wqflask/wqflask/templates/admin/search_for_groups.html | 4 +++-
 wqflask/wqflask/templates/ctl_results.html             | 4 +++-
 wqflask/wqflask/templates/network_graph.html           | 4 +++-
 wqflask/wqflask/templates/pair_scan_results.html       | 4 +++-
 5 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/wqflask/wqflask/templates/admin/create_group.html b/wqflask/wqflask/templates/admin/create_group.html
index 5a6929fb..4ab70007 100644
--- a/wqflask/wqflask/templates/admin/create_group.html
+++ b/wqflask/wqflask/templates/admin/create_group.html
@@ -80,7 +80,9 @@
 {% endblock %}
 
 {% block js %}
-    
+     
+ 
+ 
     
     
 
diff --git a/wqflask/wqflask/templates/admin/search_for_groups.html b/wqflask/wqflask/templates/admin/search_for_groups.html
index f304a172..1df4d0a2 100644
--- a/wqflask/wqflask/templates/admin/search_for_groups.html
+++ b/wqflask/wqflask/templates/admin/search_for_groups.html
@@ -63,7 +63,9 @@
 {% endblock %}
 
 {% block js %}
-    
+     
+ 
+ 
     
     
 
diff --git a/wqflask/wqflask/templates/ctl_results.html b/wqflask/wqflask/templates/ctl_results.html
index e374eb4e..7ce67d0e 100644
--- a/wqflask/wqflask/templates/ctl_results.html
+++ b/wqflask/wqflask/templates/ctl_results.html
@@ -64,7 +64,9 @@
         gn2_url = "{{ gn2_url | safe }}"
     
 
-    
+     
+ 
+ 
     
     
     
diff --git a/wqflask/wqflask/templates/network_graph.html b/wqflask/wqflask/templates/network_graph.html
index be3d0402..33f0365b 100644
--- a/wqflask/wqflask/templates/network_graph.html
+++ b/wqflask/wqflask/templates/network_graph.html
@@ -140,7 +140,9 @@
         gn2_url       = "{{ gn2_url | safe }}"
     
 
-    
+     
+ 
+ 
     
     
 
diff --git a/wqflask/wqflask/templates/pair_scan_results.html b/wqflask/wqflask/templates/pair_scan_results.html
index f1646af7..14a62e0d 100644
--- a/wqflask/wqflask/templates/pair_scan_results.html
+++ b/wqflask/wqflask/templates/pair_scan_results.html
@@ -64,7 +64,9 @@
 
     
     
-    
+     
+ 
+ 
     
     
     
-- 
cgit v1.2.3


From 0319428bc26ba7d956dc7564ad69feeae7c9ef90 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Mon, 7 Dec 2020 12:23:01 +0300
Subject: replace js_alt/timeago in templates

---
 wqflask/wqflask/templates/admin/ind_user_manager.html    | 2 +-
 wqflask/wqflask/templates/collections/list.html          | 2 +-
 wqflask/wqflask/templates/collections/not_logged_in.html | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/wqflask/wqflask/templates/admin/ind_user_manager.html b/wqflask/wqflask/templates/admin/ind_user_manager.html
index e859b442..b821e5d5 100644
--- a/wqflask/wqflask/templates/admin/ind_user_manager.html
+++ b/wqflask/wqflask/templates/admin/ind_user_manager.html
@@ -103,7 +103,7 @@
 
 {% block js %}
 
-    
+    
     
diff --git a/wqflask/wqflask/templates/collections/list.html b/wqflask/wqflask/templates/collections/list.html
index 9e0c0f07..280ca6cc 100644
--- a/wqflask/wqflask/templates/collections/list.html
+++ b/wqflask/wqflask/templates/collections/list.html
@@ -66,7 +66,7 @@
 {% endblock %}
 
 {% block js %}
-    
+    
     
     
     
diff --git a/wqflask/wqflask/templates/collections/not_logged_in.html b/wqflask/wqflask/templates/collections/not_logged_in.html
index a4c8747e..49b0e07d 100644
--- a/wqflask/wqflask/templates/collections/not_logged_in.html
+++ b/wqflask/wqflask/templates/collections/not_logged_in.html
@@ -5,7 +5,7 @@
     {{ header("Not logged in") }}
 
 
-    
    
+    
    
         
    
             
    Please log in in order to use this feature.
    
         
    
@@ -16,7 +16,7 @@
 {% endblock %}
 
 {% block js %}
-    
+    
     
-- 
cgit v1.2.3


From f9fd76752944e9952a48fd2fa3718db5b11fc40e Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Mon, 7 Dec 2020 12:25:35 +0300
Subject: replace js_alt/parsley in templates

---
 wqflask/wqflask/templates/base.html | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/templates/base.html b/wqflask/wqflask/templates/base.html
index faa59deb..51a21324 100644
--- a/wqflask/wqflask/templates/base.html
+++ b/wqflask/wqflask/templates/base.html
@@ -257,7 +257,9 @@
     
     
 
-    
+    
+
+
 
     {% block js %}
     {% endblock %}
-- 
cgit v1.2.3


From 4f55eeda63106694f8fad1355b7ecef2baad257e Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Mon, 7 Dec 2020 12:27:26 +0300
Subject: replace jstat in templates

---
 wqflask/wqflask/templates/show_trait.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/templates/show_trait.html b/wqflask/wqflask/templates/show_trait.html
index 7b74a3f6..918894f5 100644
--- a/wqflask/wqflask/templates/show_trait.html
+++ b/wqflask/wqflask/templates/show_trait.html
@@ -133,7 +133,7 @@
     
     
     
-    
+    
     
     
     
-- 
cgit v1.2.3


From 51b70d89d5b085b9f81e5da2b695873ca53e54f9 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Mon, 7 Dec 2020 12:31:24 +0300
Subject: replace jszip lib in templates

---
 wqflask/wqflask/templates/collections/list.html   | 2 +-
 wqflask/wqflask/templates/collections/view.html   | 2 +-
 wqflask/wqflask/templates/correlation_page.html   | 2 +-
 wqflask/wqflask/templates/gsearch_gene.html       | 2 +-
 wqflask/wqflask/templates/gsearch_pheno.html      | 2 +-
 wqflask/wqflask/templates/search_result_page.html | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/wqflask/wqflask/templates/collections/list.html b/wqflask/wqflask/templates/collections/list.html
index 9e0c0f07..b04672ec 100644
--- a/wqflask/wqflask/templates/collections/list.html
+++ b/wqflask/wqflask/templates/collections/list.html
@@ -69,7 +69,7 @@
     
     
     
-    
+    
     
     
     
+    
     
     
     
diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html
index 06ee9056..01789d67 100644
--- a/wqflask/wqflask/templates/correlation_page.html
+++ b/wqflask/wqflask/templates/correlation_page.html
@@ -171,7 +171,7 @@
     
     
 
-    
+    
     
 
     
diff --git a/wqflask/wqflask/templates/gsearch_gene.html b/wqflask/wqflask/templates/gsearch_gene.html
index d5bc4141..6fd0abe8 100644
--- a/wqflask/wqflask/templates/gsearch_gene.html
+++ b/wqflask/wqflask/templates/gsearch_gene.html
@@ -48,7 +48,7 @@
 {% block js %}
     
     
-    
+    
     
     
     
diff --git a/wqflask/wqflask/templates/gsearch_pheno.html b/wqflask/wqflask/templates/gsearch_pheno.html
index ccd4a9d9..0dc6fe5f 100644
--- a/wqflask/wqflask/templates/gsearch_pheno.html
+++ b/wqflask/wqflask/templates/gsearch_pheno.html
@@ -48,7 +48,7 @@
 {% block js %}
     
     
-    
+    
     
     
     
diff --git a/wqflask/wqflask/templates/search_result_page.html b/wqflask/wqflask/templates/search_result_page.html
index 87c97c50..1a2d2112 100644
--- a/wqflask/wqflask/templates/search_result_page.html
+++ b/wqflask/wqflask/templates/search_result_page.html
@@ -183,7 +183,7 @@
     
 
     
-    
+    
     
     
     
-- 
cgit v1.2.3


From 1c202735fc279f42e70d2cdef6ee8b9214ca0f44 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Mon, 7 Dec 2020 12:41:44 +0300
Subject: replace colorbox jquery.colorbox  in templates

---
 wqflask/wqflask/templates/base.html | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/wqflask/wqflask/templates/base.html b/wqflask/wqflask/templates/base.html
index faa59deb..5c8343d1 100644
--- a/wqflask/wqflask/templates/base.html
+++ b/wqflask/wqflask/templates/base.html
@@ -254,7 +254,9 @@
     
     
 
-    
+    
+
+
     
 
     
-- 
cgit v1.2.3


From 5fc3f0647beafc1290ede2f5242275c2f0e54ed9 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Mon, 7 Dec 2020 12:57:10 +0300
Subject: replace jscolor lib in templates

---
 wqflask/wqflask/templates/corr_scatterplot.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/templates/corr_scatterplot.html b/wqflask/wqflask/templates/corr_scatterplot.html
index 1133fcd2..145cfcf7 100644
--- a/wqflask/wqflask/templates/corr_scatterplot.html
+++ b/wqflask/wqflask/templates/corr_scatterplot.html
@@ -349,7 +349,7 @@
     
     
     
-    
+    
     
     
     
-- 
cgit v1.2.3


From e96b568adbb0e3ac2c3e0b1ab72d0bfd354db6a7 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Mon, 7 Dec 2020 13:00:42 +0300
Subject: replace cytoscape js in templates

---
 wqflask/wqflask/templates/ctl_results.html   | 2 +-
 wqflask/wqflask/templates/network_graph.html | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/templates/ctl_results.html b/wqflask/wqflask/templates/ctl_results.html
index e374eb4e..20935612 100644
--- a/wqflask/wqflask/templates/ctl_results.html
+++ b/wqflask/wqflask/templates/ctl_results.html
@@ -67,7 +67,7 @@
     
     
     
-    
+    
     
     
     
diff --git a/wqflask/wqflask/templates/network_graph.html b/wqflask/wqflask/templates/network_graph.html
index be3d0402..017298b9 100644
--- a/wqflask/wqflask/templates/network_graph.html
+++ b/wqflask/wqflask/templates/network_graph.html
@@ -144,7 +144,7 @@
     
     
 
-    
+    
     
     
     
-- 
cgit v1.2.3


From 8666d17de2a0b9e20be82f115df9af8d5693dbae Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Mon, 7 Dec 2020 13:02:30 +0300
Subject: replace cytoscape-panzoom js in templates

---
 wqflask/wqflask/templates/ctl_results.html   | 2 +-
 wqflask/wqflask/templates/network_graph.html | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/wqflask/wqflask/templates/ctl_results.html b/wqflask/wqflask/templates/ctl_results.html
index 20935612..28d462ee 100644
--- a/wqflask/wqflask/templates/ctl_results.html
+++ b/wqflask/wqflask/templates/ctl_results.html
@@ -68,7 +68,7 @@
     
     
     
-    
+    
     
     
 
diff --git a/wqflask/wqflask/templates/network_graph.html b/wqflask/wqflask/templates/network_graph.html
index 017298b9..1c0eabee 100644
--- a/wqflask/wqflask/templates/network_graph.html
+++ b/wqflask/wqflask/templates/network_graph.html
@@ -145,7 +145,7 @@
     
 
     
-    
+    
     
     
 
-- 
cgit v1.2.3


From 5ae193d969c0e21bdd02de577d00f8c8b8ba8148 Mon Sep 17 00:00:00 2001
From: Alexander Kabui
Date: Mon, 7 Dec 2020 13:04:30 +0300
Subject: replace cytoscape panzoom css in templates

---
 wqflask/wqflask/templates/ctl_results.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/wqflask/wqflask/templates/ctl_results.html b/wqflask/wqflask/templates/ctl_results.html
index 28d462ee..17390d96 100644
--- a/wqflask/wqflask/templates/ctl_results.html
+++ b/wqflask/wqflask/templates/ctl_results.html
@@ -1,7 +1,7 @@
 {% extends "base.html" %}
 {% block css %}
     
-    
+