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authorAlexander Kabui2021-03-13 13:04:33 +0300
committerGitHub2021-03-13 13:04:33 +0300
commit236ca06dc4c84baecb7b090b8724db997a5d988a (patch)
tree7fce724ae007dacfe3cf0f7511756b6064026ea3
parent7f9a293929be021eb73aec35defe254351557dcb (diff)
downloadgenenetwork3-236ca06dc4c84baecb7b090b8724db997a5d988a.tar.gz
Correlation api (#2)
* add file for correlation api * register initial correlation api * add correlation package * add function for getting page data * delete loading page api * modify code for correlation * add tests folder for correlations * fix error in correlation api * add tests for correlation * add tests for correlation loading data * add module for correlation computations * modify api to return json when computing correlation * add tests for computing correlation * modify code for loading correlation data * modify tests for correlation computation * test loading correlation data using api endpoint * add tests for asserting error in creating Correlation object * add do correlation method * add dummy tests for do_correlation method * delete unused modules * add tests for creating trait and dataset * add intergration test for correlation api * add tests for correlation api * edit docorrelation method * modify integration tests for correlation api * modify tests for show_corr_results * add create dataset function * pep8 formatting and fix return value for api * add more test data for doing correlation * modify tests for correlation * pep8 formatting * add getting formatted corr type method * import json library add process samples method for correlation * fix issue with sample_vals key_error * create utility module for correlation * refactor endpoint for /corr_compute * add test and mocks for compute_correlation function * add compute correlation function and pep8 formatting * move get genofile samplelist to utility module * refactor code for CorrelationResults object * pep8 formatting for module * remove CorrelationResults from Api * add base package initialize data_set module with create_dataset,redis and Dataset_Getter * set dataset_structure if redis is empty * add callable for DatsetType * add set_dataset_key method If name is not in the object's dataset dictionary * add Dataset object and MrnaAssayDataSet * add db_tools * add mysql client * add DatasetGroup object * add species module * get mapping method * import helper functions and new dataset * add connection to db before request * add helper functions * add logger module * add get_group_samplelists module * add logger for debug * add code for adding sample_data * pep8 formatting * Add chunks module * add correlation helper module * add get_sample_r_and_p_values method add get_header_fields function * add generate corr json method * add function to retrieve_trait_info * remove comments and clean up code in show_corr_results * remove comments and clean up code for data_set module * pep8 formatting for helper_functions module * pep8 formatting for trait module * add module for species * add Temp Dataset Object * add Phenotype Dataset * add Genotype Dataset * add rettrieve sample_sample_data method * add webqtlUtil module * add do lit correlation for all traits * add webqtlCaseData:Settings not ported * return the_trait for create trait method * add correlation_test json data * add tests fore show corr results * add dictfier package * add tests for show_corr_results * add assertion for trait_id * refactor code for show_corr_results * add test file for compute_corr intergration tests * add scipy dependency * refactor show_corr_results object add do lit correlation for trait_list * add hmac module * add bunch module:Dictionary using object notation * add correlation functions * add rpy2 dependency * add hmac module * add MrnaAssayTissueData object and get_symbol_values_pairs function * add config module * add get json_results method * pep8 formatting remove comments * add config file * add db package * refactor correlatio compuatation module * add do tissue correlation for trait list * add do lit correlation for all traits * add do tissue correlation for all traits * add do_bicor for bicor method * raise error for when initital start vars is None * add support for both form and json data when for correlation input * remove print statement and pep8 formatting * add default settings file * add tools module for locate_ignore_error * refactor code remove comments for trait module * Add new test data for computing correlation * pep8 formatting and use pickle * refactor function for filtering form/json data * remove unused imports * remove mock functions in correlation_utility module * refactor tests for compute correlation and pep8 formatting * add tests for show_correlation results * modify tests for show_corr_results * add json files for tests * pep8 formatting for show_corr_results * Todo:Lint base files * pylint for intergration tests * add test module for test_corr_helpers * Add test chunk module * lint utility package * refactoring and pep8 formatting * implement simple metric for correlation * add hmac utility file * add correlation prefix * fix merge conflict * minor fixes for endpoints * import:python-scipy,python-sqlalchemy from guix * add python mysqlclient * remove pkg-resources from requirements * add python-rpy3 from guix * refactor code for species module * pep8 formatting and refactor code * add tests for genereating correlation results * lint correlation functions * fix failing tests for show_corr_results * add new correlation test data fix errors * fix issues related to getting group samplelists * refactor intergration tests for correlation * add todo for refactoring_wanted_inputs * replace custom Attribute setter with SimpleNamespace * comparison of sample r correlation results btwn genenenetwork2 and genenetwork3 * delete AttributeSetter * test request for /api/correlation/compute_correlation took 18.55710196495056 Seconds * refactor tests and show_correlation results * remove unneccessary comments and print statements * edit requirement txt file * api/correlation took 114.29814600944519 Seconds for correlation resullts:20000 - corr-type:lit - corr-method:pearson corr-dataset:corr_dataset:HC_M2_0606_P * capture SQL_URI and GENENETWORK FILES path * pep8 formatting edit && remove print statements * delete filter_input function update test and data for correlation * add docstring for required correlation_input * /api/correlation took 12.905632972717285 Seconds * pearson * lit *dataset:HX_M2_0606_P trait_id :1444666 p_range:(lower->-0.60,uppper->0.74) corr_return_results: 100 * update integration and unittest for correlation * add simple markdown docs for correlation * update docs * add tests and catch for invalid correlation_input * minor fix for api * Remove jupyter from deps * guix.scm: Remove duplicate entry * guix.scm: Add extra action items as comments * Trim requirements.txt file Co-authored-by: BonfaceKilz <me@bonfacemunyoki.com>
-rw-r--r--default_settings.py18
-rw-r--r--docs/correlation.md42
-rw-r--r--gn3/api/correlation.py68
-rw-r--r--gn3/app.py8
-rw-r--r--gn3/base/__init__.py0
-rw-r--r--gn3/base/data_set.py886
-rw-r--r--gn3/base/mrna_assay_tissue_data.py94
-rw-r--r--gn3/base/species.py64
-rw-r--r--gn3/base/trait.py366
-rw-r--r--gn3/base/webqtlCaseData.py84
-rw-r--r--gn3/config.py16
-rw-r--r--gn3/correlation/__init__.py0
-rw-r--r--gn3/correlation/correlation_computations.py32
-rw-r--r--gn3/correlation/correlation_functions.py96
-rw-r--r--gn3/correlation/correlation_utility.py22
-rw-r--r--gn3/correlation/show_corr_results.py735
-rw-r--r--gn3/db/__init__.py0
-rw-r--r--gn3/db/calls.py51
-rw-r--r--gn3/db/webqtlDatabaseFunction.py52
-rw-r--r--gn3/utility/__init__.py0
-rw-r--r--gn3/utility/bunch.py16
-rw-r--r--gn3/utility/chunks.py32
-rw-r--r--gn3/utility/corr_result_helpers.py45
-rw-r--r--gn3/utility/db_tools.py19
-rw-r--r--gn3/utility/get_group_samplelists.py47
-rw-r--r--gn3/utility/helper_functions.py24
-rw-r--r--gn3/utility/hmac.py50
-rw-r--r--gn3/utility/logger.py163
-rw-r--r--gn3/utility/species.py71
-rw-r--r--gn3/utility/tools.py37
-rw-r--r--gn3/utility/webqtlUtil.py66
-rw-r--r--guix.scm11
-rw-r--r--requirements.txt18
-rw-r--r--tests/integration/correlation_data.json18
-rw-r--r--tests/integration/expected_corr_results.json1902
-rw-r--r--tests/integration/test_correlation.py57
-rw-r--r--tests/unit/correlation/__init__.py0
-rw-r--r--tests/unit/correlation/correlation_test_data.json18
-rw-r--r--tests/unit/correlation/dataset.json64
-rw-r--r--tests/unit/correlation/expected_correlation_results.json1902
-rw-r--r--tests/unit/correlation/group_data_test.json214
-rw-r--r--tests/unit/correlation/my_results.json388
-rw-r--r--tests/unit/correlation/test_correlation_computations.py65
-rw-r--r--tests/unit/correlation/test_show_corr_results.py226
-rw-r--r--tests/unit/utility/__init__.py0
-rw-r--r--tests/unit/utility/test_chunks.py19
-rw-r--r--tests/unit/utility/test_corr_result_helpers.py35
-rw-r--r--tests/unit/utility/test_hmac.py51
48 files changed, 8174 insertions, 18 deletions
diff --git a/default_settings.py b/default_settings.py
new file mode 100644
index 0000000..9cdc665
--- /dev/null
+++ b/default_settings.py
@@ -0,0 +1,18 @@
+"""module contains default settings for genenetwork"""
+import os
+
+
+USE_REDIS = True
+
+GN2_BASE_URL = "https://genenetwork.org/"
+
+
+HOME = os.environ['HOME']
+
+# SQL_URI = "mysql://gn2:mysql_password@localhost/db_webqtl_s"
+
+SQL_URI = os.environ.get("SQL_URI","mysql+pymysql://kabui:1234@localhost/db_webqtl")
+
+SECRET_HMAC_CODE = '\x08\xdf\xfa\x93N\x80\xd9\\H@\\\x9f`\x98d^\xb4a;\xc6OM\x946a\xbc\xfc\x80:*\xebc'
+
+GENENETWORK_FILES = os.environ.get("GENENETWORK_FILES",HOME+"/data/genotype_files")
diff --git a/docs/correlation.md b/docs/correlation.md
new file mode 100644
index 0000000..bd1b278
--- /dev/null
+++ b/docs/correlation.md
@@ -0,0 +1,42 @@
+### endpoint for correlation endpoint
+
+- The endpoint for correlation is
+```python
+
+ /api/correlation/compute/corr_compute
+```
+
+
+**To be noted before spinning the server for correlation computation\which can be set for example env
+SQL_URI=mysql://user:password@localhost/db_webqtl and also to GENENETWORK_FILES default is HOME+"/data/genotype_files**
+
+(required input data *should be in json format*)
+- "primary_samples": "",
+- "trait_id"
+- "dataset"
+- "sample_vals"
+- "corr_type"
+- "corr_dataset"
+- "corr_return_results"
+- "corr_samples_group"
+- "corr_sample_method"
+- "min_expr"
+- "location_type"
+- "loc_chr"
+- "min_loc_mb"
+- "max_loc_mb"
+- "p_range_lower"
+- "p_range_upper"
+
+- example
+
+```bash
+curl -X POST -H "Content-Type: application/json" \
+ -d '{"primary_samles":"",trait_id:"","dataset":"","sample_vals":"","corr_type":"",corr_sample_group:"",corr_sample_method:""}' \
+ localhost:5000/api/correlation/correlation_compute
+
+ ```
+
+
+- output data is correlation_json
+
diff --git a/gn3/api/correlation.py b/gn3/api/correlation.py
new file mode 100644
index 0000000..4e3e07e
--- /dev/null
+++ b/gn3/api/correlation.py
@@ -0,0 +1,68 @@
+"""Endpoints for computing correlation"""
+import pickle
+import time
+from flask import Blueprint
+from flask import jsonify
+from flask import request
+from flask import g
+from flask import after_this_request
+from default_settings import SQL_URI
+
+# import pymysql
+
+from sqlalchemy import create_engine
+from gn3.correlation.correlation_computations import compute_correlation
+
+
+correlation = Blueprint("correlation", __name__)
+
+
+
+# xtodo implement neat db setup
+@correlation.before_request
+def connect_db():
+ """add connection to db method"""
+ print("@app.before_request connect_db")
+ db_connection = getattr(g, '_database', None)
+ if db_connection is None:
+ print("Get new database connector")
+ g.db = g._database = create_engine(SQL_URI, encoding="latin1")
+
+ g.initial_time = time.time()
+
+
+@correlation.after_request
+def after_request_func(response):
+ final_time = time.time() - g.initial_time
+ print(f"This request for Correlation took {final_time} Seconds")
+
+ g.initial_time = None
+
+ return response
+
+
+
+
+@correlation.route("/corr_compute", methods=["POST"])
+def corr_compute_page():
+ """api for doing correlation"""
+
+ # todo accepts both form and json data
+
+ correlation_input = request.json
+
+ if correlation_input is None:
+ return jsonify({"error": str("Bad request")}),400
+
+
+
+ try:
+ corr_results = compute_correlation(correlation_input_data=correlation_input)
+
+
+ except Exception as error: # pylint: disable=broad-except
+ return jsonify({"error": str(error)})
+
+ return {
+ "correlation_results":corr_results
+ } \ No newline at end of file
diff --git a/gn3/app.py b/gn3/app.py
index 33f53f9..c912ed9 100644
--- a/gn3/app.py
+++ b/gn3/app.py
@@ -4,9 +4,10 @@ import os
from typing import Dict
from typing import Union
from flask import Flask
-
+from gn3.config import get_config
from gn3.api.gemma import gemma
from gn3.api.general import general
+from gn3.api.correlation import correlation
def create_app(config: Union[Dict, str, None] = None) -> Flask:
@@ -15,6 +16,10 @@ def create_app(config: Union[Dict, str, None] = None) -> Flask:
# Load default configuration
app.config.from_object("gn3.settings")
+ my_config = get_config()
+
+ app.config.from_object(my_config["dev"])
+
# Load environment configuration
if "GN3_CONF" in os.environ:
app.config.from_envvar('GN3_CONF')
@@ -27,4 +32,5 @@ def create_app(config: Union[Dict, str, None] = None) -> Flask:
app.config.from_pyfile(config)
app.register_blueprint(general, url_prefix="/api/")
app.register_blueprint(gemma, url_prefix="/api/gemma")
+ app.register_blueprint(correlation,url_prefix="/api/correlation")
return app
diff --git a/gn3/base/__init__.py b/gn3/base/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/gn3/base/__init__.py
diff --git a/gn3/base/data_set.py b/gn3/base/data_set.py
new file mode 100644
index 0000000..e61e4eb
--- /dev/null
+++ b/gn3/base/data_set.py
@@ -0,0 +1,886 @@
+
+import json
+import math
+import collections
+import requests
+from redis import Redis
+from flask import g
+from gn3.utility.db_tools import escape
+from gn3.utility.db_tools import mescape
+from gn3.utility.db_tools import create_in_clause
+from gn3.utility.tools import locate_ignore_error
+from gn3.db.calls import fetch1
+from gn3.db.calls import fetchone
+from gn3.db.webqtlDatabaseFunction import retrieve_species
+from gn3.utility import chunks
+
+from gn3.utility import get_group_samplelists
+from gn3.base.species import TheSpecies
+r = Redis()
+
+# should probably move this to its own configuration files
+
+USE_REDIS = True
+
+# todo move to config file
+GN2_BASE_URL = "https://genenetwork.org/"
+
+DS_NAME_MAP = {}
+
+# pylint: disable-all
+#todo file not linted
+# pylint: disable=C0103
+
+
+
+def create_dataset(dataset_name, dataset_type=None, get_samplelist=True, group_name=None):
+
+ if dataset_name == "Temp":
+ dataset_type = "Temp"
+
+ if dataset_type is None:
+ dataset_type = Dataset_Getter(dataset_name)
+ dataset_ob = DS_NAME_MAP[dataset_type]
+ dataset_class = globals()[dataset_ob]
+
+ if dataset_type == "Temp":
+ results = dataset_class(dataset_name, get_samplelist, group_name)
+
+ else:
+ results = dataset_class(dataset_name, get_samplelist)
+
+ return results
+
+
+class DatasetType:
+ def __init__(self, redis_instance):
+ self.redis_instance = redis_instance
+ self.datasets = {}
+
+ data = self.redis_instance.get("dataset_structure")
+ if data:
+ self.datasets = json.loads(data)
+
+ else:
+
+ try:
+
+ data = json.loads(requests.get(
+ GN2_BASE_URL + "/api/v_pre1/gen_dropdown", timeout=5).content)
+
+ # todo:Refactor code below n^4 loop
+
+ for species in data["datasets"]:
+ for group in data["datasets"][species]:
+ for dataset_type in data['datasets'][species][group]:
+ for dataset in data['datasets'][species][group][dataset_type]:
+
+ short_dataset_name = dataset[1]
+ if dataset_type == "Phenotypes":
+ new_type = "Publish"
+
+ elif dataset_type == "Genotypes":
+ new_type = "Geno"
+ else:
+ new_type = "ProbeSet"
+
+ self.datasets[short_dataset_name] = new_type
+
+ except Exception as e:
+ raise e
+
+ self.redis_instance.set(
+ "dataset_structure", json.dumps(self.datasets))
+
+ def set_dataset_key(self, t, name):
+ """If name is not in the object's dataset dictionary, set it, and update
+ dataset_structure in Redis
+
+ args:
+ t: Type of dataset structure which can be: 'mrna_expr', 'pheno',
+ 'other_pheno', 'geno'
+ name: The name of the key to inserted in the datasets dictionary
+
+ """
+
+ sql_query_mapping = {
+ 'mrna_expr': ("""SELECT ProbeSetFreeze.Id FROM """ +
+ """ProbeSetFreeze WHERE ProbeSetFreeze.Name = "{}" """),
+ 'pheno': ("""SELECT InfoFiles.GN_AccesionId """ +
+ """FROM InfoFiles, PublishFreeze, InbredSet """ +
+ """WHERE InbredSet.Name = '{}' AND """ +
+ """PublishFreeze.InbredSetId = InbredSet.Id AND """ +
+ """InfoFiles.InfoPageName = PublishFreeze.Name"""),
+ 'other_pheno': ("""SELECT PublishFreeze.Name """ +
+ """FROM PublishFreeze, InbredSet """ +
+ """WHERE InbredSet.Name = '{}' AND """ +
+ """PublishFreeze.InbredSetId = InbredSet.Id"""),
+ 'geno': ("""SELECT GenoFreeze.Id FROM GenoFreeze WHERE """ +
+ """GenoFreeze.Name = "{}" """)
+ }
+
+ dataset_name_mapping = {
+ "mrna_expr": "ProbeSet",
+ "pheno": "Publish",
+ "other_pheno": "Publish",
+ "geno": "Geno",
+ }
+
+ group_name = name
+ if t in ['pheno', 'other_pheno']:
+ group_name = name.replace("Publish", "")
+
+ results = g.db.execute(
+ sql_query_mapping[t].format(group_name)).fetchone()
+ if results:
+ self.datasets[name] = dataset_name_mapping[t]
+ self.redis_instance.set(
+ "dataset_structure", json.dumps(self.datasets))
+
+ return True
+
+ return None
+
+ def __call__(self, name):
+ if name not in self.datasets:
+ for t in ["mrna_expr", "pheno", "other_pheno", "geno"]:
+
+ if(self.set_dataset_key(t, name)):
+ # This has side-effects, with the end result being a truth-y value
+ break
+
+ return self.datasets.get(name, None)
+
+
+# Do the intensive work at startup one time only
+# could replace the code below
+Dataset_Getter = DatasetType(r)
+
+
+class DatasetGroup:
+ """
+ Each group has multiple datasets; each species has multiple groups.
+
+ For example, Mouse has multiple groups (BXD, BXA, etc), and each group
+ has multiple datasets associated with it.
+
+ """
+
+ def __init__(self, dataset, name=None):
+ """This sets self.group and self.group_id"""
+ if name == None:
+ self.name, self.id, self.genetic_type = fetchone(
+ dataset.query_for_group)
+
+ else:
+ self.name, self.id, self.genetic_type = fetchone(
+ "SELECT InbredSet.Name, InbredSet.Id, InbredSet.GeneticType FROM InbredSet where Name='%s'" % name)
+
+ if self.name == 'BXD300':
+ self.name = "BXD"
+
+ self.f1list = None
+
+ self.parlist = None
+
+ self.get_f1_parent_strains()
+
+ # remove below not used in correlation
+
+ self.mapping_id, self.mapping_names = self.get_mapping_methods()
+
+ self.species = retrieve_species(self.name)
+
+ def get_f1_parent_strains(self):
+ try:
+ # should import ParInfo
+ raise e
+ # NL, 07/27/2010. ParInfo has been moved from webqtlForm.py to webqtlUtil.py;
+ f1, f12, maternal, paternal = webqtlUtil.ParInfo[self.name]
+ except Exception as e:
+ f1 = f12 = maternal = paternal = None
+
+ if f1 and f12:
+ self.f1list = [f1, f12]
+
+ if maternal and paternal:
+ self.parlist = [maternal, paternal]
+
+ def get_mapping_methods(self):
+ mapping_id = g.db.execute(
+ "select MappingMethodId from InbredSet where Name= '%s'" % self.name).fetchone()[0]
+
+ if mapping_id == "1":
+ mapping_names = ["GEMMA", "QTLReaper", "R/qtl"]
+ elif mapping_id == "2":
+ mapping_names = ["GEMMA"]
+
+ elif mapping_id == "3":
+ mapping_names = ["R/qtl"]
+
+ elif mapping_id == "4":
+ mapping_names = ["GEMMA", "PLINK"]
+
+ else:
+ mapping_names = []
+
+ return mapping_id, mapping_names
+
+ def get_samplelist(self):
+ result = None
+ key = "samplelist:v3:" + self.name
+ if USE_REDIS:
+ result = r.get(key)
+
+ if result is not None:
+
+ self.samplelist = json.loads(result)
+
+ else:
+ # logger.debug("Cache not hit")
+ # should enable logger
+ genotype_fn = locate_ignore_error(self.name+".geno", 'genotype')
+ if genotype_fn:
+ self.samplelist = get_group_samplelists.get_samplelist(
+ "geno", genotype_fn)
+
+ else:
+ self.samplelist = None
+
+ if USE_REDIS:
+ r.set(key, json.dumps(self.samplelist))
+ r.expire(key, 60*5)
+
+
+class DataSet:
+ """
+ DataSet class defines a dataset in webqtl, can be either Microarray,
+ Published phenotype, genotype, or user input dataset(temp)
+
+ """
+
+ def __init__(self, name, get_samplelist=True, group_name=None):
+
+ assert name, "Need a name"
+ self.name = name
+ self.id = None
+ self.shortname = None
+ self.fullname = None
+ self.type = None
+ self.data_scale = None # ZS: For example log2
+
+ self.setup()
+
+ if self.type == "Temp": # Need to supply group name as input if temp trait
+ # sets self.group and self.group_id and gets genotype
+ self.group = DatasetGroup(self, name=group_name)
+ else:
+ self.check_confidentiality()
+ self.retrieve_other_names()
+ # sets self.group and self.group_id and gets genotype
+ self.group = DatasetGroup(self)
+ self.accession_id = self.get_accession_id()
+ if get_samplelist == True:
+ self.group.get_samplelist()
+ self.species = TheSpecies(self)
+
+ def get_desc(self):
+ """Gets overridden later, at least for Temp...used by trait's get_given_name"""
+ return None
+
+ # Delete this eventually
+ @property
+ def riset():
+ Weve_Renamed_This_As_Group
+
+ def get_accession_id(self):
+ if self.type == "Publish":
+ results = g.db.execute("""select InfoFiles.GN_AccesionId from InfoFiles, PublishFreeze, InbredSet where
+ InbredSet.Name = %s and
+ PublishFreeze.InbredSetId = InbredSet.Id and
+ InfoFiles.InfoPageName = PublishFreeze.Name and
+ PublishFreeze.public > 0 and
+ PublishFreeze.confidentiality < 1 order by
+ PublishFreeze.CreateTime desc""", (self.group.name)).fetchone()
+ elif self.type == "Geno":
+ results = g.db.execute("""select InfoFiles.GN_AccesionId from InfoFiles, GenoFreeze, InbredSet where
+ InbredSet.Name = %s and
+ GenoFreeze.InbredSetId = InbredSet.Id and
+ InfoFiles.InfoPageName = GenoFreeze.ShortName and
+ GenoFreeze.public > 0 and
+ GenoFreeze.confidentiality < 1 order by
+ GenoFreeze.CreateTime desc""", (self.group.name)).fetchone()
+ else:
+ results = None
+
+ if results != None:
+ return str(results[0])
+ else:
+ return "None"
+
+ def retrieve_other_names(self):
+ """This method fetches the the dataset names in search_result.
+
+ If the data set name parameter is not found in the 'Name' field of
+ the data set table, check if it is actually the FullName or
+ ShortName instead.
+
+ This is not meant to retrieve the data set info if no name at
+ all is passed.
+
+ """
+
+ try:
+ if self.type == "ProbeSet":
+ query_args = tuple(escape(x) for x in (
+ self.name,
+ self.name,
+ self.name))
+
+ self.id, self.name, self.fullname, self.shortname, self.data_scale, self.tissue = fetch1("""
+ SELECT ProbeSetFreeze.Id, ProbeSetFreeze.Name, ProbeSetFreeze.FullName, ProbeSetFreeze.ShortName, ProbeSetFreeze.DataScale, Tissue.Name
+ FROM ProbeSetFreeze, ProbeFreeze, Tissue
+ WHERE ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id
+ AND ProbeFreeze.TissueId = Tissue.Id
+ AND (ProbeSetFreeze.Name = '%s' OR ProbeSetFreeze.FullName = '%s' OR ProbeSetFreeze.ShortName = '%s')
+ """ % (query_args), "/dataset/"+self.name+".json",
+ lambda r: (r["id"], r["name"], r["full_name"],
+ r["short_name"], r["data_scale"], r["tissue"])
+ )
+ else:
+ query_args = tuple(escape(x) for x in (
+ (self.type + "Freeze"),
+ self.name,
+ self.name,
+ self.name))
+
+ self.tissue = "N/A"
+ self.id, self.name, self.fullname, self.shortname = fetchone("""
+ SELECT Id, Name, FullName, ShortName
+ FROM %s
+ WHERE (Name = '%s' OR FullName = '%s' OR ShortName = '%s')
+ """ % (query_args))
+
+ except TypeError as e:
+ logger.debug(
+ "Dataset {} is not yet available in GeneNetwork.".format(self.name))
+ pass
+
+ def get_trait_data(self, sample_list=None):
+ if sample_list:
+ self.samplelist = sample_list
+ else:
+ self.samplelist = self.group.samplelist
+
+ if self.group.parlist != None and self.group.f1list != None:
+ if (self.group.parlist + self.group.f1list) in self.samplelist:
+ self.samplelist += self.group.parlist + self.group.f1list
+
+ query = """
+ SELECT Strain.Name, Strain.Id FROM Strain, Species
+ WHERE Strain.Name IN {}
+ and Strain.SpeciesId=Species.Id
+ and Species.name = '{}'
+ """.format(create_in_clause(self.samplelist), *mescape(self.group.species))
+ # logger.sql(query)
+ results = dict(g.db.execute(query).fetchall())
+ sample_ids = [results[item] for item in self.samplelist]
+
+ # MySQL limits the number of tables that can be used in a join to 61,
+ # so we break the sample ids into smaller chunks
+ # Postgres doesn't have that limit, so we can get rid of this after we transition
+ chunk_size = 50
+ number_chunks = int(math.ceil(len(sample_ids) / chunk_size))
+ trait_sample_data = []
+ for sample_ids_step in chunks.divide_into_chunks(sample_ids, number_chunks):
+ if self.type == "Publish":
+ dataset_type = "Phenotype"
+ else:
+ dataset_type = self.type
+ temp = ['T%s.value' % item for item in sample_ids_step]
+ if self.type == "Publish":
+ query = "SELECT {}XRef.Id,".format(escape(self.type))
+ else:
+ query = "SELECT {}.Name,".format(escape(dataset_type))
+ data_start_pos = 1
+ query += ', '.join(temp)
+ query += ' FROM ({}, {}XRef, {}Freeze) '.format(*mescape(dataset_type,
+ self.type,
+ self.type))
+
+ for item in sample_ids_step:
+ query += """
+ left join {}Data as T{} on T{}.Id = {}XRef.DataId
+ and T{}.StrainId={}\n
+ """.format(*mescape(self.type, item, item, self.type, item, item))
+
+ if self.type == "Publish":
+ query += """
+ WHERE {}XRef.InbredSetId = {}Freeze.InbredSetId
+ and {}Freeze.Name = '{}'
+ and {}.Id = {}XRef.{}Id
+ order by {}.Id
+ """.format(*mescape(self.type, self.type, self.type, self.name,
+ dataset_type, self.type, dataset_type, dataset_type))
+ else:
+ query += """
+ WHERE {}XRef.{}FreezeId = {}Freeze.Id
+ and {}Freeze.Name = '{}'
+ and {}.Id = {}XRef.{}Id
+ order by {}.Id
+ """.format(*mescape(self.type, self.type, self.type, self.type,
+ self.name, dataset_type, self.type, self.type, dataset_type))
+
+ results = g.db.execute(query).fetchall()
+ trait_sample_data.append(results)
+
+ trait_count = len(trait_sample_data[0])
+ self.trait_data = collections.defaultdict(list)
+
+ # put all of the separate data together into a dictionary where the keys are
+ # trait names and values are lists of sample values
+ for trait_counter in range(trait_count):
+ trait_name = trait_sample_data[0][trait_counter][0]
+ for chunk_counter in range(int(number_chunks)):
+ self.trait_data[trait_name] += (
+ trait_sample_data[chunk_counter][trait_counter][data_start_pos:])
+
+
+class MrnaAssayDataSet(DataSet):
+ '''
+ An mRNA Assay is a quantitative assessment (assay) associated with an mRNA trait
+
+ This used to be called ProbeSet, but that term only refers specifically to the Affymetrix
+ platform and is far too specific.
+
+ '''
+ DS_NAME_MAP['ProbeSet'] = 'MrnaAssayDataSet'
+
+ def setup(self):
+ # Fields in the database table
+ self.search_fields = ['Name',
+ 'Description',
+ 'Probe_Target_Description',
+ 'Symbol',
+ 'Alias',
+ 'GenbankId',
+ 'UniGeneId',
+ 'RefSeq_TranscriptId']
+
+ # Find out what display_fields is
+ self.display_fields = ['name', 'symbol',
+ 'description', 'probe_target_description',
+ 'chr', 'mb',
+ 'alias', 'geneid',
+ 'genbankid', 'unigeneid',
+ 'omim', 'refseq_transcriptid',
+ 'blatseq', 'targetseq',
+ 'chipid', 'comments',
+ 'strand_probe', 'strand_gene',
+ 'proteinid', 'uniprotid',
+ 'probe_set_target_region',
+ 'probe_set_specificity',
+ 'probe_set_blat_score',
+ 'probe_set_blat_mb_start',
+ 'probe_set_blat_mb_end',
+ 'probe_set_strand',
+ 'probe_set_note_by_rw',
+ 'flag']
+
+ # Fields displayed in the search results table header
+ self.header_fields = ['Index',
+ 'Record',
+ 'Symbol',
+ 'Description',
+ 'Location',
+ 'Mean',
+ 'Max LRS',
+ 'Max LRS Location',
+ 'Additive Effect']
+
+ # Todo: Obsolete or rename this field
+ self.type = 'ProbeSet'
+
+ self.query_for_group = '''
+ SELECT
+ InbredSet.Name, InbredSet.Id, InbredSet.GeneticType
+ FROM
+ InbredSet, ProbeSetFreeze, ProbeFreeze
+ WHERE
+ ProbeFreeze.InbredSetId = InbredSet.Id AND
+ ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId AND
+ ProbeSetFreeze.Name = "%s"
+ ''' % escape(self.name)
+
+ def check_confidentiality(self):
+ return geno_mrna_confidentiality(self)
+
+ def get_trait_info(self, trait_list=None, species=''):
+
+ # Note: setting trait_list to [] is probably not a great idea.
+ if not trait_list:
+ trait_list = []
+
+ for this_trait in trait_list:
+
+ if not this_trait.haveinfo:
+ this_trait.retrieveInfo(QTL=1)
+
+ if not this_trait.symbol:
+ this_trait.symbol = "N/A"
+
+ # XZ, 12/08/2008: description
+ # XZ, 06/05/2009: Rob asked to add probe target description
+ description_string = str(
+ str(this_trait.description).strip(codecs.BOM_UTF8), 'utf-8')
+ target_string = str(
+ str(this_trait.probe_target_description).strip(codecs.BOM_UTF8), 'utf-8')
+
+ if len(description_string) > 1 and description_string != 'None':
+ description_display = description_string
+ else:
+ description_display = this_trait.symbol
+
+ if (len(description_display) > 1 and description_display != 'N/A' and
+ len(target_string) > 1 and target_string != 'None'):
+ description_display = description_display + '; ' + target_string.strip()
+
+ # Save it for the jinja2 template
+ this_trait.description_display = description_display
+
+ if this_trait.chr and this_trait.mb:
+ this_trait.location_repr = 'Chr%s: %.6f' % (
+ this_trait.chr, float(this_trait.mb))
+
+ # Get mean expression value
+ query = (
+ """select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet
+ where ProbeSetXRef.ProbeSetFreezeId = %s and
+ ProbeSet.Id = ProbeSetXRef.ProbeSetId and
+ ProbeSet.Name = '%s'
+ """ % (escape(str(this_trait.dataset.id)),
+ escape(this_trait.name)))
+
+ #logger.debug("query is:", pf(query))
+ logger.sql(query)
+ result = g.db.execute(query).fetchone()
+
+ mean = result[0] if result else 0
+
+ if mean:
+ this_trait.mean = "%2.3f" % mean
+
+ # LRS and its location
+ this_trait.LRS_score_repr = 'N/A'
+ this_trait.LRS_location_repr = 'N/A'
+
+ # Max LRS and its Locus location
+ if this_trait.lrs and this_trait.locus:
+ query = """
+ select Geno.Chr, Geno.Mb from Geno, Species
+ where Species.Name = '{}' and
+ Geno.Name = '{}' and
+ Geno.SpeciesId = Species.Id
+ """.format(species, this_trait.locus)
+ logger.sql(query)
+ result = g.db.execute(query).fetchone()
+
+ if result:
+ lrs_chr, lrs_mb = result
+ this_trait.LRS_score_repr = '%3.1f' % this_trait.lrs
+ this_trait.LRS_location_repr = 'Chr%s: %.6f' % (
+ lrs_chr, float(lrs_mb))
+
+ return trait_list
+
+ def retrieve_sample_data(self, trait):
+ query = """
+ SELECT
+ Strain.Name, ProbeSetData.value, ProbeSetSE.error, NStrain.count, Strain.Name2
+ FROM
+ (ProbeSetData, ProbeSetFreeze, Strain, ProbeSet, ProbeSetXRef)
+ left join ProbeSetSE on
+ (ProbeSetSE.DataId = ProbeSetData.Id AND ProbeSetSE.StrainId = ProbeSetData.StrainId)
+ left join NStrain on
+ (NStrain.DataId = ProbeSetData.Id AND
+ NStrain.StrainId = ProbeSetData.StrainId)
+ WHERE
+ ProbeSet.Name = '%s' AND ProbeSetXRef.ProbeSetId = ProbeSet.Id AND
+ ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND
+ ProbeSetFreeze.Name = '%s' AND
+ ProbeSetXRef.DataId = ProbeSetData.Id AND
+ ProbeSetData.StrainId = Strain.Id
+ Order BY
+ Strain.Name
+ """ % (escape(trait), escape(self.name))
+ # logger.sql(query)
+ results = g.db.execute(query).fetchall()
+ #logger.debug("RETRIEVED RESULTS HERE:", results)
+ return results
+
+ def retrieve_genes(self, column_name):
+ query = """
+ select ProbeSet.Name, ProbeSet.%s
+ from ProbeSet,ProbeSetXRef
+ where ProbeSetXRef.ProbeSetFreezeId = %s and
+ ProbeSetXRef.ProbeSetId=ProbeSet.Id;
+ """ % (column_name, escape(str(self.id)))
+ # logger.sql(query)
+ results = g.db.execute(query).fetchall()
+
+ return dict(results)
+
+
+class TempDataSet(DataSet):
+ '''Temporary user-generated data set'''
+
+ DS_NAME_MAP['Temp'] = 'TempDataSet'
+
+ def setup(self):
+ self.search_fields = ['name',
+ 'description']
+
+ self.display_fields = ['name',
+ 'description']
+
+ self.header_fields = ['Name',
+ 'Description']
+
+ self.type = 'Temp'
+
+ # Need to double check later how these are used
+ self.id = 1
+ self.fullname = 'Temporary Storage'
+ self.shortname = 'Temp'
+
+
+class PhenotypeDataSet(DataSet):
+ DS_NAME_MAP['Publish'] = 'PhenotypeDataSet'
+
+ def setup(self):
+
+ #logger.debug("IS A PHENOTYPEDATASET")
+
+ # Fields in the database table
+ self.search_fields = ['Phenotype.Post_publication_description',
+ 'Phenotype.Pre_publication_description',
+ 'Phenotype.Pre_publication_abbreviation',
+ 'Phenotype.Post_publication_abbreviation',
+ 'PublishXRef.mean',
+ 'Phenotype.Lab_code',
+ 'Publication.PubMed_ID',
+ 'Publication.Abstract',
+ 'Publication.Title',
+ 'Publication.Authors',
+ 'PublishXRef.Id']
+
+ # Figure out what display_fields is
+ self.display_fields = ['name', 'group_code',
+ 'pubmed_id',
+ 'pre_publication_description',
+ 'post_publication_description',
+ 'original_description',
+ 'pre_publication_abbreviation',
+ 'post_publication_abbreviation',
+ 'mean',
+ 'lab_code',
+ 'submitter', 'owner',
+ 'authorized_users',
+ 'authors', 'title',
+ 'abstract', 'journal',
+ 'volume', 'pages',
+ 'month', 'year',
+ 'sequence', 'units', 'comments']
+
+ # Fields displayed in the search results table header
+ self.header_fields = ['Index',
+ 'Record',
+ 'Description',
+ 'Authors',
+ 'Year',
+ 'Max LRS',
+ 'Max LRS Location',
+ 'Additive Effect']
+
+ self.type = 'Publish'
+
+ self.query_for_group = '''
+ SELECT
+ InbredSet.Name, InbredSet.Id, InbredSet.GeneticType
+ FROM
+ InbredSet, PublishFreeze
+ WHERE
+ PublishFreeze.InbredSetId = InbredSet.Id AND
+ PublishFreeze.Name = "%s"
+ ''' % escape(self.name)
+
+ def check_confidentiality(self):
+ # (Urgently?) Need to write this
+ pass
+
+ def get_trait_info(self, trait_list, species=''):
+ for this_trait in trait_list:
+
+ if not this_trait.haveinfo:
+ this_trait.retrieve_info(get_qtl_info=True)
+
+ description = this_trait.post_publication_description
+
+ # 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 this_trait.confidential:
+ this_trait.description_display = ""
+ continue # todo for now, because no authorization features
+
+ if not webqtlUtil.has_access_to_confidentail_phenotype_trait(
+ privilege=self.privilege,
+ userName=self.userName,
+ authorized_users=this_trait.authorized_users):
+
+ description = this_trait.pre_publication_description
+
+ if len(description) > 0:
+ this_trait.description_display = description.strip()
+ else:
+ this_trait.description_display = ""
+
+ if not this_trait.year.isdigit():
+ this_trait.pubmed_text = "N/A"
+ else:
+ this_trait.pubmed_text = this_trait.year
+
+ if this_trait.pubmed_id:
+ this_trait.pubmed_link = webqtlConfig.PUBMEDLINK_URL % this_trait.pubmed_id
+
+ # LRS and its location
+ this_trait.LRS_score_repr = "N/A"
+ this_trait.LRS_location_repr = "N/A"
+
+ if this_trait.lrs:
+ query = """
+ select Geno.Chr, Geno.Mb from Geno, Species
+ where Species.Name = '%s' and
+ Geno.Name = '%s' and
+ Geno.SpeciesId = Species.Id
+ """ % (species, this_trait.locus)
+
+ result = g.db.execute(query).fetchone()
+
+ if result:
+ if result[0] and result[1]:
+ LRS_Chr = result[0]
+ LRS_Mb = result[1]
+
+ this_trait.LRS_score_repr = LRS_score_repr = '%3.1f' % this_trait.lrs
+ this_trait.LRS_location_repr = LRS_location_repr = 'Chr%s: %.6f' % (
+ LRS_Chr, float(LRS_Mb))
+
+ def retrieve_sample_data(self, trait):
+ query = """
+ SELECT
+ Strain.Name, PublishData.value, PublishSE.error, NStrain.count, Strain.Name2
+ FROM
+ (PublishData, Strain, PublishXRef, PublishFreeze)
+ left join PublishSE on
+ (PublishSE.DataId = PublishData.Id AND PublishSE.StrainId = PublishData.StrainId)
+ left join NStrain on
+ (NStrain.DataId = PublishData.Id AND
+ NStrain.StrainId = PublishData.StrainId)
+ WHERE
+ PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND
+ PublishData.Id = PublishXRef.DataId AND PublishXRef.Id = %s AND
+ PublishFreeze.Id = %s AND PublishData.StrainId = Strain.Id
+ Order BY
+ Strain.Name
+ """
+
+ results = g.db.execute(query, (trait, self.id)).fetchall()
+ return results
+
+
+class GenotypeDataSet(DataSet):
+ DS_NAME_MAP['Geno'] = 'GenotypeDataSet'
+
+ def setup(self):
+ # Fields in the database table
+ self.search_fields = ['Name',
+ 'Chr']
+
+ # Find out what display_fields is
+ self.display_fields = ['name',
+ 'chr',
+ 'mb',
+ 'source2',
+ 'sequence']
+
+ # Fields displayed in the search results table header
+ self.header_fields = ['Index',
+ 'ID',
+ 'Location']
+
+ # Todo: Obsolete or rename this field
+ self.type = 'Geno'
+
+ self.query_for_group = '''
+ SELECT
+ InbredSet.Name, InbredSet.Id, InbredSet.GeneticType
+ FROM
+ InbredSet, GenoFreeze
+ WHERE
+ GenoFreeze.InbredSetId = InbredSet.Id AND
+ GenoFreeze.Name = "%s"
+ ''' % escape(self.name)
+
+ def check_confidentiality(self):
+ return geno_mrna_confidentiality(self)
+
+ def get_trait_info(self, trait_list, species=None):
+ for this_trait in trait_list:
+ if not this_trait.haveinfo:
+ this_trait.retrieveInfo()
+
+ if this_trait.chr and this_trait.mb:
+ this_trait.location_repr = 'Chr%s: %.6f' % (
+ this_trait.chr, float(this_trait.mb))
+
+ def retrieve_sample_data(self, trait):
+ query = """
+ SELECT
+ Strain.Name, GenoData.value, GenoSE.error, "N/A", Strain.Name2
+ FROM
+ (GenoData, GenoFreeze, Strain, Geno, GenoXRef)
+ left join GenoSE on
+ (GenoSE.DataId = GenoData.Id AND GenoSE.StrainId = GenoData.StrainId)
+ WHERE
+ Geno.SpeciesId = %s AND Geno.Name = %s AND GenoXRef.GenoId = Geno.Id AND
+ GenoXRef.GenoFreezeId = GenoFreeze.Id AND
+ GenoFreeze.Name = %s AND
+ GenoXRef.DataId = GenoData.Id AND
+ GenoData.StrainId = Strain.Id
+ Order BY
+ Strain.Name
+ """
+ results = g.db.execute(query,
+ (webqtlDatabaseFunction.retrieve_species_id(self.group.name),
+ trait, self.name)).fetchall()
+ return results
+
+
+def geno_mrna_confidentiality(ob):
+ dataset_table = ob.type + "Freeze"
+ #logger.debug("dataset_table [%s]: %s" % (type(dataset_table), dataset_table))
+
+ query = '''SELECT Id, Name, FullName, confidentiality,
+ AuthorisedUsers FROM %s WHERE Name = "%s"''' % (dataset_table, ob.name)
+ #
+ result = g.db.execute(query)
+
+ (_dataset_id,
+ _name,
+ _full_name,
+ confidential,
+ _authorized_users) = result.fetchall()[0]
+
+ if confidential:
+ return True
diff --git a/gn3/base/mrna_assay_tissue_data.py b/gn3/base/mrna_assay_tissue_data.py
new file mode 100644
index 0000000..0f51ade
--- /dev/null
+++ b/gn3/base/mrna_assay_tissue_data.py
@@ -0,0 +1,94 @@
+
+# pylint: disable-all
+import collections
+
+from flask import g
+
+from gn3.utility.db_tools import create_in_clause
+from gn3.utility.db_tools import escape
+from gn3.utility.bunch import Bunch
+
+
+# from utility.logger import getLogger
+# logger = getLogger(__name__ )
+
+class MrnaAssayTissueData(object):
+
+ def __init__(self, gene_symbols=None):
+ self.gene_symbols = gene_symbols
+ if self.gene_symbols == None:
+ self.gene_symbols = []
+
+ self.data = collections.defaultdict(Bunch)
+
+ query = '''select t.Symbol, t.GeneId, t.DataId, t.Chr, t.Mb, t.description, t.Probe_Target_Description
+ from (
+ select Symbol, max(Mean) as maxmean
+ from TissueProbeSetXRef
+ where TissueProbeSetFreezeId=1 and '''
+
+ # Note that inner join is necessary in this query to get distinct record in one symbol group
+ # with highest mean value
+ # Due to the limit size of TissueProbeSetFreezeId table in DB,
+ # performance of inner join is acceptable.MrnaAssayTissueData(gene_symbols=symbol_list)
+ if len(gene_symbols) == 0:
+ query += '''Symbol!='' and Symbol Is Not Null group by Symbol)
+ as x inner join TissueProbeSetXRef as t on t.Symbol = x.Symbol
+ and t.Mean = x.maxmean;
+ '''
+ else:
+ in_clause = create_in_clause(gene_symbols)
+
+ # ZS: This was in the query, not sure why: http://docs.python.org/2/library/string.html?highlight=lower#string.lower
+ query += ''' Symbol in {} group by Symbol)
+ as x inner join TissueProbeSetXRef as t on t.Symbol = x.Symbol
+ and t.Mean = x.maxmean;
+ '''.format(in_clause)
+
+ results = g.db.execute(query).fetchall()
+
+ lower_symbols = []
+ for gene_symbol in gene_symbols:
+ if gene_symbol != None:
+ lower_symbols.append(gene_symbol.lower())
+
+ for result in results:
+ symbol = result[0]
+ if symbol.lower() in lower_symbols:
+ symbol = symbol.lower()
+
+ self.data[symbol].gene_id = result.GeneId
+ self.data[symbol].data_id = result.DataId
+ self.data[symbol].chr = result.Chr
+ self.data[symbol].mb = result.Mb
+ self.data[symbol].description = result.description
+ self.data[symbol].probe_target_description = result.Probe_Target_Description
+
+ ###########################################################################
+ # Input: cursor, symbolList (list), dataIdDict(Dict)
+ # output: symbolValuepairDict (dictionary):one dictionary of Symbol and Value Pair,
+ # key is symbol, value is one list of expression values of one probeSet;
+ # function: get one dictionary whose key is gene symbol and value is tissue expression data (list type).
+ # Attention! All keys are lower case!
+ ###########################################################################
+
+ def get_symbol_values_pairs(self):
+ id_list = [self.data[symbol].data_id for symbol in self.data]
+
+ symbol_values_dict = {}
+
+ if len(id_list) > 0:
+ query = """SELECT TissueProbeSetXRef.Symbol, TissueProbeSetData.value
+ FROM TissueProbeSetXRef, TissueProbeSetData
+ WHERE TissueProbeSetData.Id IN {} and
+ TissueProbeSetXRef.DataId = TissueProbeSetData.Id""".format(create_in_clause(id_list))
+
+ results = g.db.execute(query).fetchall()
+ for result in results:
+ if result.Symbol.lower() not in symbol_values_dict:
+ symbol_values_dict[result.Symbol.lower()] = [result.value]
+ else:
+ symbol_values_dict[result.Symbol.lower()].append(
+ result.value)
+
+ return symbol_values_dict
diff --git a/gn3/base/species.py b/gn3/base/species.py
new file mode 100644
index 0000000..9fb08fb
--- /dev/null
+++ b/gn3/base/species.py
@@ -0,0 +1,64 @@
+
+# pylint: disable-all
+import collections
+from flask import g
+from dataclasses import dataclass
+
+class TheSpecies:
+ def __init__(self, dataset=None, species_name=None):
+ if species_name is not None:
+ self.name = species_name
+
+ self.chromosomes = Chromosomes(species=self.name)
+
+ else:
+ self.dataset = dataset
+ self.chromosomes = Chromosomes(dataset=self.dataset)
+
+
+class Chromosomes:
+ def __init__(self, dataset=None, species=None):
+ self.chromosomes = collections.OrderedDict()
+
+ if species is not None:
+ query = """
+ Select
+ Chr_Length.Name, Chr_Length.OrderId, Length from Chr_Length, Species
+ where
+ Chr_Length.SpeciesId = Species.SpeciesId AND
+ Species.Name = '%s'
+ Order by OrderId
+ """ % species.capitalize()
+
+ else:
+ self.dataset = dataset
+
+ query = """
+ Select
+ Chr_Length.Name, Chr_Length.OrderId, Length from Chr_Length, InbredSet
+ where
+ Chr_Length.SpeciesId = InbredSet.SpeciesId AND
+ InbredSet.Name = '%s'
+ Order by OrderId
+ """ % self.dataset.group.name
+
+ # logger.sql(query)
+
+ results = g.db.execute(query).fetchall()
+
+ for item in results:
+ self.chromosomes[item.OrderId] = IndChromosome(
+ item.Name, item.Length)
+
+
+# @dataclass
+class IndChromosome:
+ def __init__(self,name,length):
+ self.name= name
+ self.length = length
+
+ @property
+ def mb_length(self):
+ """Chromosome length in megabases"""
+ return self.length/ 1000000
+
diff --git a/gn3/base/trait.py b/gn3/base/trait.py
new file mode 100644
index 0000000..f4be61c
--- /dev/null
+++ b/gn3/base/trait.py
@@ -0,0 +1,366 @@
+
+# pylint: disable-all
+from flask import g
+from redis import Redis
+from gn3.utility.db_tools import escape
+from gn3.base.webqtlCaseData import webqtlCaseData
+
+
+def check_resource_availability(dataset, name=None):
+
+ # todo add code for this
+ # should probably work on this has to do with authentication
+ return {'data': ['no-access', 'view'], 'metadata': ['no-access', 'view'], 'admin': ['not-admin']}
+
+
+def create_trait(**kw):
+ # work on check resource availability deals with authentication
+ assert bool(kw.get("dataset")) != bool(
+ kw.get('dataset_name')), "Needs dataset ob. or name"
+
+ assert bool(kw.get("name")), "Need trait name"
+
+ if kw.get('dataset_name'):
+ if kw.get('dataset_name') != "Temp":
+ dataset = create_dataset(kw.get('dataset_name'))
+ else:
+ dataset = kw.get('dataset')
+
+ if dataset.type == 'Publish':
+ permissions = check_resource_availability(
+ dataset, kw.get('name'))
+ else:
+ permissions = check_resource_availability(dataset)
+
+ if "view" in permissions['data']:
+ the_trait = GeneralTrait(**kw)
+ if the_trait.dataset.type != "Temp":
+ the_trait = retrieve_trait_info(
+ the_trait,
+ the_trait.dataset,
+ get_qtl_info=kw.get('get_qtl_info'))
+
+
+ return the_trait
+
+ return None
+
+
+class GeneralTrait:
+ def __init__(self, get_qtl_info=False, get_sample_info=True, **kw):
+ assert bool(kw.get('dataset')) != bool(
+ kw.get('dataset_name')), "Needs dataset ob. or name"
+ # Trait ID, ProbeSet ID, Published ID, etc.
+ self.name = kw.get('name')
+ if kw.get('dataset_name'):
+ if kw.get('dataset_name') == "Temp":
+ temp_group = self.name.split("_")[2]
+ self.dataset = create_dataset(
+ dataset_name="Temp",
+ dataset_type="Temp",
+ group_name=temp_group)
+
+ else:
+ self.dataset = create_dataset(kw.get('dataset_name'))
+
+ else:
+ self.dataset = kw.get("dataset")
+
+ self.cellid = kw.get('cellid')
+ self.identification = kw.get('identification', 'un-named trait')
+ self.haveinfo = kw.get('haveinfo', False)
+ self.sequence = kw.get('sequence')
+ self.data = kw.get('data', {})
+ self.view = True
+
+ # Sets defaults
+ self.locus = None
+ self.lrs = None
+ self.pvalue = None
+ self.mean = None
+ self.additive = None
+ self.num_overlap = None
+ self.strand_probe = None
+ self.symbol = None
+ self.display_name = self.name
+ self.LRS_score_repr = "N/A"
+ self.LRS_location_repr = "N/A"
+
+ if kw.get('fullname'):
+ name2 = value.split("::")
+ if len(name2) == 2:
+ self.dataset, self.name = name2
+
+ elif len(name2) == 3:
+ self.dataset, self.name, self.cellid = name2
+
+ # Todo: These two lines are necessary most of the time, but
+ # perhaps not all of the time So we could add a simple if
+ # statement to short-circuit this if necessary
+ if get_sample_info is not False:
+ self = retrieve_sample_data(self, self.dataset)
+
+
+def retrieve_sample_data(trait, dataset, samplelist=None):
+ if samplelist is None:
+ samplelist = []
+
+ if dataset.type == "Temp":
+ results = Redis.get(trait.name).split()
+
+ else:
+ results = dataset.retrieve_sample_data(trait.name)
+
+ # Todo: is this necessary? If not remove
+ trait.data.clear()
+
+ if results:
+ if dataset.type == "Temp":
+ all_samples_ordered = dataset.group.all_samples_ordered()
+ for i, item in enumerate(results):
+ try:
+ trait.data[all_samples_ordered[i]] = webqtlCaseData(
+ all_samples_ordered[i], float(item))
+
+ except Exception as e:
+ pass
+
+
+ else:
+ for item in results:
+ name, value, variance, num_cases, name2 = item
+ if not samplelist or (samplelist and name in samplelist):
+ trait.data[name] = webqtlCaseData(*item)
+
+ return trait
+
+def retrieve_trait_info(trait, dataset, get_qtl_info=False):
+ assert dataset, "Dataset doesn't exist"
+
+ the_url = None
+ # some code should be added added here
+
+ try:
+ response = requests.get(the_url).content
+ trait_info = json.loads(response)
+ except: # ZS: I'm assuming the trait is viewable if the try fails for some reason; it should never reach this point unless the user has privileges, since that's dealt with in create_trait
+ if dataset.type == 'Publish':
+ query = """
+ SELECT
+ PublishXRef.Id, InbredSet.InbredSetCode, Publication.PubMed_ID,
+ CAST(Phenotype.Pre_publication_description AS BINARY),
+ CAST(Phenotype.Post_publication_description AS BINARY),
+ CAST(Phenotype.Original_description AS BINARY),
+ CAST(Phenotype.Pre_publication_abbreviation AS BINARY),
+ CAST(Phenotype.Post_publication_abbreviation AS BINARY), PublishXRef.mean,
+ Phenotype.Lab_code, Phenotype.Submitter, Phenotype.Owner, Phenotype.Authorized_Users,
+ CAST(Publication.Authors AS BINARY), CAST(Publication.Title AS BINARY), CAST(Publication.Abstract AS BINARY),
+ CAST(Publication.Journal AS BINARY), Publication.Volume, Publication.Pages,
+ Publication.Month, Publication.Year, PublishXRef.Sequence,
+ Phenotype.Units, PublishXRef.comments
+ FROM
+ PublishXRef, Publication, Phenotype, PublishFreeze, InbredSet
+ WHERE
+ PublishXRef.Id = %s AND
+ Phenotype.Id = PublishXRef.PhenotypeId AND
+ Publication.Id = PublishXRef.PublicationId AND
+ PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND
+ PublishXRef.InbredSetId = InbredSet.Id AND
+ PublishFreeze.Id = %s
+ """ % (trait.name, dataset.id)
+
+ trait_info = g.db.execute(query).fetchone()
+
+ # XZ, 05/08/2009: Xiaodong add this block to use ProbeSet.Id to find the probeset instead of just using ProbeSet.Name
+ # XZ, 05/08/2009: to avoid the problem of same probeset name from different platforms.
+ elif dataset.type == 'ProbeSet':
+ display_fields_string = ', ProbeSet.'.join(dataset.display_fields)
+ display_fields_string = 'ProbeSet.' + display_fields_string
+ query = """
+ SELECT %s
+ FROM ProbeSet, ProbeSetFreeze, ProbeSetXRef
+ WHERE
+ ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND
+ ProbeSetXRef.ProbeSetId = ProbeSet.Id AND
+ ProbeSetFreeze.Name = '%s' AND
+ ProbeSet.Name = '%s'
+ """ % (escape(display_fields_string),
+ escape(dataset.name),
+ escape(str(trait.name)))
+
+ trait_info = g.db.execute(query).fetchone()
+ # XZ, 05/08/2009: We also should use Geno.Id to find marker instead of just using Geno.Name
+ # to avoid the problem of same marker name from different species.
+ elif dataset.type == 'Geno':
+ display_fields_string = ',Geno.'.join(dataset.display_fields)
+ display_fields_string = 'Geno.' + display_fields_string
+ query = """
+ SELECT %s
+ FROM Geno, GenoFreeze, GenoXRef
+ WHERE
+ GenoXRef.GenoFreezeId = GenoFreeze.Id AND
+ GenoXRef.GenoId = Geno.Id AND
+ GenoFreeze.Name = '%s' AND
+ Geno.Name = '%s'
+ """ % (escape(display_fields_string),
+ escape(dataset.name),
+ escape(trait.name))
+
+ trait_info = g.db.execute(query).fetchone()
+ else: # Temp type
+ query = """SELECT %s FROM %s WHERE Name = %s"""
+
+ trait_info = g.db.execute(query,
+ ','.join(dataset.display_fields),
+ dataset.type, trait.name).fetchone()
+
+ if trait_info:
+ trait.haveinfo = True
+ for i, field in enumerate(dataset.display_fields):
+ holder = trait_info[i]
+ if isinstance(holder, bytes):
+ holder = holder.decode("utf-8", errors="ignore")
+ setattr(trait, field, holder)
+
+ if dataset.type == 'Publish':
+ if trait.group_code:
+ trait.display_name = trait.group_code + "_" + str(trait.name)
+
+ trait.confidential = 0
+ if trait.pre_publication_description and not trait.pubmed_id:
+ trait.confidential = 1
+
+ description = trait.post_publication_description
+
+ # 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
+ trait.description_display = ""
+ if not trait.pubmed_id:
+ trait.abbreviation = trait.pre_publication_abbreviation
+ trait.description_display = trait.pre_publication_description
+ else:
+ trait.abbreviation = trait.post_publication_abbreviation
+ if description:
+ trait.description_display = description.strip()
+
+ if not trait.year.isdigit():
+ trait.pubmed_text = "N/A"
+ else:
+ trait.pubmed_text = trait.year
+
+ # moved to config
+
+ PUBMEDLINK_URL = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=%s&dopt=Abstract"
+
+ if trait.pubmed_id:
+ trait.pubmed_link = PUBMEDLINK_URL % trait.pubmed_id
+
+ if dataset.type == 'ProbeSet' and dataset.group:
+ description_string = trait.description
+ target_string = trait.probe_target_description
+
+ if str(description_string or "") != "" and description_string != 'None':
+ description_display = description_string
+ else:
+ description_display = trait.symbol
+
+ if (str(description_display or "") != "" and
+ description_display != 'N/A' and
+ str(target_string or "") != "" and target_string != 'None'):
+ description_display = description_display + '; ' + target_string.strip()
+
+ # Save it for the jinja2 template
+ trait.description_display = description_display
+
+ trait.location_repr = 'N/A'
+ if trait.chr and trait.mb:
+ trait.location_repr = 'Chr%s: %.6f' % (
+ trait.chr, float(trait.mb))
+
+ elif dataset.type == "Geno":
+ trait.location_repr = 'N/A'
+ if trait.chr and trait.mb:
+ trait.location_repr = 'Chr%s: %.6f' % (
+ trait.chr, float(trait.mb))
+
+ if get_qtl_info:
+ # LRS and its location
+ trait.LRS_score_repr = "N/A"
+ trait.LRS_location_repr = "N/A"
+ trait.locus = trait.locus_chr = trait.locus_mb = trait.lrs = trait.pvalue = trait.additive = ""
+ if dataset.type == 'ProbeSet' and not trait.cellid:
+ trait.mean = ""
+ query = """
+ SELECT
+ ProbeSetXRef.Locus, ProbeSetXRef.LRS, ProbeSetXRef.pValue, ProbeSetXRef.mean, ProbeSetXRef.additive
+ FROM
+ ProbeSetXRef, ProbeSet
+ WHERE
+ ProbeSetXRef.ProbeSetId = ProbeSet.Id AND
+ ProbeSet.Name = "{}" AND
+ ProbeSetXRef.ProbeSetFreezeId ={}
+ """.format(trait.name, dataset.id)
+
+ trait_qtl = g.db.execute(query).fetchone()
+ if trait_qtl:
+ trait.locus, trait.lrs, trait.pvalue, trait.mean, trait.additive = trait_qtl
+ if trait.locus:
+ query = """
+ select Geno.Chr, Geno.Mb from Geno, Species
+ where Species.Name = '{}' and
+ Geno.Name = '{}' and
+ Geno.SpeciesId = Species.Id
+ """.format(dataset.group.species, trait.locus)
+
+ result = g.db.execute(query).fetchone()
+ if result:
+ trait.locus_chr = result[0]
+ trait.locus_mb = result[1]
+ else:
+ trait.locus = trait.locus_chr = trait.locus_mb = trait.additive = ""
+ else:
+ trait.locus = trait.locus_chr = trait.locus_mb = trait.additive = ""
+
+ if dataset.type == 'Publish':
+ query = """
+ SELECT
+ PublishXRef.Locus, PublishXRef.LRS, PublishXRef.additive
+ FROM
+ PublishXRef, PublishFreeze
+ WHERE
+ PublishXRef.Id = %s AND
+ PublishXRef.InbredSetId = PublishFreeze.InbredSetId AND
+ PublishFreeze.Id =%s
+ """ % (trait.name, dataset.id)
+
+ trait_qtl = g.db.execute(query).fetchone()
+ if trait_qtl:
+ trait.locus, trait.lrs, trait.additive = trait_qtl
+ if trait.locus:
+ query = """
+ select Geno.Chr, Geno.Mb from Geno, Species
+ where Species.Name = '{}' and
+ Geno.Name = '{}' and
+ Geno.SpeciesId = Species.Id
+ """.format(dataset.group.species, trait.locus)
+
+ result = g.db.execute(query).fetchone()
+ if result:
+ trait.locus_chr = result[0]
+ trait.locus_mb = result[1]
+ else:
+ trait.locus = trait.locus_chr = trait.locus_mb = trait.additive = ""
+ else:
+ trait.locus = trait.locus_chr = trait.locus_mb = trait.additive = ""
+ else:
+ trait.locus = trait.lrs = trait.additive = ""
+ if (dataset.type == 'Publish' or dataset.type == "ProbeSet") and str(trait.locus_chr or "") != "" and str(trait.locus_mb or "") != "":
+ trait.LRS_location_repr = LRS_location_repr = 'Chr%s: %.6f' % (
+ trait.locus_chr, float(trait.locus_mb))
+ if str(trait.lrs or "") != "":
+ trait.LRS_score_repr = LRS_score_repr = '%3.1f' % trait.lrs
+ else:
+ raise KeyError(repr(trait.name) +
+ ' information is not found in the database.')
+ return trait
diff --git a/gn3/base/webqtlCaseData.py b/gn3/base/webqtlCaseData.py
new file mode 100644
index 0000000..8395af8
--- /dev/null
+++ b/gn3/base/webqtlCaseData.py
@@ -0,0 +1,84 @@
+# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
+#
+# This program is free software: you can redistribute it and/or modify it
+# under the terms of the GNU Affero General Public License
+# as published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero General Public License for more details.
+#
+# This program is available from Source Forge: at GeneNetwork Project
+# (sourceforge.net/projects/genenetwork/).
+#
+# Contact Drs. Robert W. Williams and Xiaodong Zhou (2010)
+# at rwilliams@uthsc.edu and xzhou15@uthsc.edu
+#
+# This module is used by GeneNetwork project (www.genenetwork.org)
+#
+# Created by GeneNetwork Core Team 2010/08/10
+
+
+# uncomment below
+
+# from utility.logger import getLogger
+# logger = getLogger(__name__)
+
+# import utility.tools
+
+# utility.tools.show_settings()
+# pylint: disable-all
+
+class webqtlCaseData:
+ """one case data in one trait"""
+
+ def __init__(self, name, value=None, variance=None, num_cases=None, name2=None):
+ self.name = name
+ self.name2 = name2 # Other name (for traits like BXD65a)
+ self.value = value # Trait Value
+ self.variance = variance # Trait Variance
+ self.num_cases = num_cases # Number of individuals/cases
+ self.extra_attributes = None
+ self.this_id = None # Set a sane default (can't be just "id" cause that's a reserved word)
+ self.outlier = None # Not set to True/False until later
+
+ def __repr__(self):
+ case_data_string = "<webqtlCaseData> "
+ if self.value is not None:
+ case_data_string += "value=%2.3f" % self.value
+ if self.variance is not None:
+ case_data_string += " variance=%2.3f" % self.variance
+ if self.num_cases:
+ case_data_string += " ndata=%s" % self.num_cases
+ if self.name:
+ case_data_string += " name=%s" % self.name
+ if self.name2:
+ case_data_string += " name2=%s" % self.name2
+ return case_data_string
+
+ @property
+ def class_outlier(self):
+ """Template helper"""
+ if self.outlier:
+ return "outlier"
+ return ""
+
+ @property
+ def display_value(self):
+ if self.value is not None:
+ return "%2.3f" % self.value
+ return "x"
+
+ @property
+ def display_variance(self):
+ if self.variance is not None:
+ return "%2.3f" % self.variance
+ return "x"
+
+ @property
+ def display_num_cases(self):
+ if self.num_cases is not None:
+ return "%s" % self.num_cases
+ return "x" \ No newline at end of file
diff --git a/gn3/config.py b/gn3/config.py
new file mode 100644
index 0000000..9c6ec34
--- /dev/null
+++ b/gn3/config.py
@@ -0,0 +1,16 @@
+class Config:
+ DEBUG = True
+ Testing = False
+
+
+class DevConfig(Config):
+ Testing = True
+ SQLALCHEMY_DATABASE_URI = "mysql://kabui:1234@localhost/test"
+ SECRET_KEY = "password"
+ SQLALCHEMY_TRACK_MODIFICATIONS = False
+
+
+def get_config():
+ return {
+ "dev": DevConfig
+ }
diff --git a/gn3/correlation/__init__.py b/gn3/correlation/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/gn3/correlation/__init__.py
diff --git a/gn3/correlation/correlation_computations.py b/gn3/correlation/correlation_computations.py
new file mode 100644
index 0000000..6a3f2bb
--- /dev/null
+++ b/gn3/correlation/correlation_computations.py
@@ -0,0 +1,32 @@
+"""module contains code for any computation in correlation"""
+
+import json
+from .show_corr_results import CorrelationResults
+
+def compute_correlation(correlation_input_data,
+ correlation_results=CorrelationResults):
+ """function that does correlation .creates Correlation results instance
+
+ correlation_input_data structure is a dict with
+
+ {
+ "trait_id":"valid trait id",
+ "dataset":"",
+ "sample_vals":{},
+ "primary_samples":"",
+ "corr_type":"",
+ corr_dataset:"",
+ "corr_return_results":"",
+
+
+ }
+
+ """
+
+ corr_object = correlation_results(
+ start_vars=correlation_input_data)
+
+ corr_results = corr_object.do_correlation(start_vars=correlation_input_data)
+ # possibility of file being so large cause of the not sure whether to return a file
+
+ return corr_results
diff --git a/gn3/correlation/correlation_functions.py b/gn3/correlation/correlation_functions.py
new file mode 100644
index 0000000..be08c96
--- /dev/null
+++ b/gn3/correlation/correlation_functions.py
@@ -0,0 +1,96 @@
+
+"""
+# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
+#
+# This program is free software: you can redistribute it and/or modify it
+# under the terms of the GNU Affero General Public License
+# as published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero General Public License for more details.
+#
+# This program is available from Source Forge: at GeneNetwork Project
+# (sourceforge.net/projects/genenetwork/).
+#
+# Contact Drs. Robert W. Williams and Xiaodong Zhou (2010)
+# at rwilliams@uthsc.edu and xzhou15@uthsc.edu
+#
+#
+#
+# This module is used by GeneNetwork project (www.genenetwork.org)
+#
+# Created by GeneNetwork Core Team 2010/08/10
+#
+# Last updated by NL 2011/03/23
+
+
+"""
+
+import rpy2.robjects
+from gn3.base.mrna_assay_tissue_data import MrnaAssayTissueData
+
+
+#####################################################################################
+# Input: primaryValue(list): one list of expression values of one probeSet,
+# targetValue(list): one list of expression values of one probeSet,
+# method(string): indicate correlation method ('pearson' or 'spearman')
+# Output: corr_result(list): first item is Correlation Value, second item is tissue number,
+# third item is PValue
+# Function: get correlation value,Tissue quantity ,p value result by using R;
+# Note : This function is special case since both primaryValue and targetValue are from
+# the same dataset. So the length of these two parameters is the same. They are pairs.
+# Also, in the datatable TissueProbeSetData, all Tissue values are loaded based on
+# the same tissue order
+#####################################################################################
+
+def cal_zero_order_corr_for_tiss(primaryValue=[], targetValue=[], method='pearson'):
+ """refer above for info on the function"""
+ # pylint: disable = E, W, R, C
+
+ #nb disabled pylint until tests are written for this function
+
+ R_primary = rpy2.robjects.FloatVector(list(range(len(primaryValue))))
+ N = len(primaryValue)
+ for i in range(len(primaryValue)):
+ R_primary[i] = primaryValue[i]
+
+ R_target = rpy2.robjects.FloatVector(list(range(len(targetValue))))
+ for i in range(len(targetValue)):
+ R_target[i] = targetValue[i]
+
+ R_corr_test = rpy2.robjects.r['cor.test']
+ if method == 'spearman':
+ R_result = R_corr_test(R_primary, R_target, method='spearman')
+ else:
+ R_result = R_corr_test(R_primary, R_target)
+
+ corr_result = []
+ corr_result.append(R_result[3][0])
+ corr_result.append(N)
+ corr_result.append(R_result[2][0])
+
+ return corr_result
+
+
+####################################################
+####################################################
+# input: cursor, symbolList (list), dataIdDict(Dict): key is symbol
+# output: SymbolValuePairDict(dictionary):one dictionary of Symbol and Value Pair.
+# key is symbol, value is one list of expression values of one probeSet.
+# function: wrapper function for getSymbolValuePairDict function
+# build gene symbol list if necessary, cut it into small lists if necessary,
+# then call getSymbolValuePairDict function and merge the results.
+###################################################
+#####################################################
+
+def get_trait_symbol_and_tissue_values(symbol_list=None):
+ """function to get trait symbol and tissues values refer above"""
+ tissue_data = MrnaAssayTissueData(gene_symbols=symbol_list)
+
+ if len(tissue_data.gene_symbols) >= 1:
+ return tissue_data.get_symbol_values_pairs()
+
+ return None
diff --git a/gn3/correlation/correlation_utility.py b/gn3/correlation/correlation_utility.py
new file mode 100644
index 0000000..7583bd7
--- /dev/null
+++ b/gn3/correlation/correlation_utility.py
@@ -0,0 +1,22 @@
+"""module contains utility functions for correlation"""
+
+
+class AttributeSetter:
+ """class for setting Attributes"""
+
+ def __init__(self, trait_obj):
+ for key, value in trait_obj.items():
+ setattr(self, key, value)
+
+ def __str__(self):
+ return self.__class__.__name__
+
+ def get_dict(self):
+ """dummy function to get dict object"""
+ return self.__dict__
+
+
+def get_genofile_samplelist(dataset):
+ """mock function to get genofile samplelist"""
+
+ return ["C57BL/6J"]
diff --git a/gn3/correlation/show_corr_results.py b/gn3/correlation/show_corr_results.py
new file mode 100644
index 0000000..55d8366
--- /dev/null
+++ b/gn3/correlation/show_corr_results.py
@@ -0,0 +1,735 @@
+"""module contains code for doing correlation"""
+
+import json
+import collections
+import numpy
+import scipy.stats
+import rpy2.robjects as ro
+from flask import g
+from gn3.base.data_set import create_dataset
+from gn3.utility.db_tools import escape
+from gn3.utility.helper_functions import get_species_dataset_trait
+from gn3.utility.corr_result_helpers import normalize_values
+from gn3.base.trait import create_trait
+from gn3.utility import hmac
+from . import correlation_functions
+
+
+class CorrelationResults:
+ """class for computing correlation"""
+ # pylint: disable=too-many-instance-attributes
+ # pylint:disable=attribute-defined-outside-init
+
+ def __init__(self, start_vars):
+ self.assertion_for_start_vars(start_vars)
+
+ @staticmethod
+ def assertion_for_start_vars(start_vars):
+ # pylint: disable = E, W, R, C
+
+ # should better ways to assert the variables
+ # example includes sample
+ assert("corr_type" in start_vars)
+ assert(isinstance(start_vars['corr_type'], str))
+ # example includes pearson
+ assert('corr_sample_method' in start_vars)
+ assert('corr_dataset' in start_vars)
+ # means the limit
+ assert('corr_return_results' in start_vars)
+
+ if "loc_chr" in start_vars:
+ assert('min_loc_mb' in start_vars)
+ assert('max_loc_mb' in start_vars)
+
+ def get_formatted_corr_type(self):
+ """method to formatt corr_types"""
+ self.formatted_corr_type = ""
+ if self.corr_type == "lit":
+ self.formatted_corr_type += "Literature Correlation "
+ elif self.corr_type == "tissue":
+ self.formatted_corr_type += "Tissue Correlation "
+ elif self.corr_type == "sample":
+ self.formatted_corr_type += "Genetic Correlation "
+
+ if self.corr_method == "pearson":
+ self.formatted_corr_type += "(Pearson's r)"
+ elif self.corr_method == "spearman":
+ self.formatted_corr_type += "(Spearman's rho)"
+ elif self.corr_method == "bicor":
+ self.formatted_corr_type += "(Biweight r)"
+
+ def process_samples(self, start_vars, sample_names, excluded_samples=None):
+ """method to process samples"""
+
+
+ if not excluded_samples:
+ excluded_samples = ()
+
+ sample_val_dict = json.loads(start_vars["sample_vals"])
+ print(sample_val_dict)
+ if sample_names is None:
+ raise NotImplementedError
+
+ for sample in sample_names:
+ if sample not in excluded_samples:
+ value = sample_val_dict[sample]
+
+ if not value.strip().lower() == "x":
+ self.sample_data[str(sample)] = float(value)
+
+ def do_tissue_correlation_for_trait_list(self, tissue_dataset_id=1):
+ """Given a list of correlation results (self.correlation_results),\
+ gets the tissue correlation value for each"""
+ # pylint: disable = E, W, R, C
+
+ # Gets tissue expression values for the primary trait
+ primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values(
+ symbol_list=[self.this_trait.symbol])
+
+ if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict:
+ primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower(
+ )]
+ gene_symbol_list = [
+ trait.symbol for trait in self.correlation_results if trait.symbol]
+
+ corr_result_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values(
+ symbol_list=gene_symbol_list)
+
+ for trait in self.correlation_results:
+ if trait.symbol and trait.symbol.lower() in corr_result_tissue_vals_dict:
+ this_trait_tissue_values = corr_result_tissue_vals_dict[trait.symbol.lower(
+ )]
+
+ result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values,
+ this_trait_tissue_values,
+ self.corr_method)
+
+ trait.tissue_corr = result[0]
+ trait.tissue_pvalue = result[2]
+
+ def do_lit_correlation_for_trait_list(self):
+ # pylint: disable = E, W, R, C
+
+ input_trait_mouse_gene_id = self.convert_to_mouse_gene_id(
+ self.dataset.group.species.lower(), self.this_trait.geneid)
+
+ for trait in self.correlation_results:
+
+ if trait.geneid:
+ trait.mouse_gene_id = self.convert_to_mouse_gene_id(
+ self.dataset.group.species.lower(), trait.geneid)
+ else:
+ trait.mouse_gene_id = None
+
+ if trait.mouse_gene_id and str(trait.mouse_gene_id).find(";") == -1:
+ result = g.db.execute(
+ """SELECT value
+ FROM LCorrRamin3
+ WHERE GeneId1='%s' and
+ GeneId2='%s'
+ """ % (escape(str(trait.mouse_gene_id)), escape(str(input_trait_mouse_gene_id)))
+ ).fetchone()
+ if not result:
+ result = g.db.execute("""SELECT value
+ FROM LCorrRamin3
+ WHERE GeneId2='%s' and
+ GeneId1='%s'
+ """ % (escape(str(trait.mouse_gene_id)), escape(str(input_trait_mouse_gene_id)))
+ ).fetchone()
+
+ if result:
+ lit_corr = result.value
+ trait.lit_corr = lit_corr
+ else:
+ trait.lit_corr = 0
+ else:
+ trait.lit_corr = 0
+
+ def do_lit_correlation_for_all_traits(self):
+ """method for lit_correlation for all traits"""
+ # pylint: disable = E, W, R, C
+ input_trait_mouse_gene_id = self.convert_to_mouse_gene_id(
+ self.dataset.group.species.lower(), self.this_trait.geneid)
+
+ lit_corr_data = {}
+ for trait, gene_id in list(self.trait_geneid_dict.items()):
+ mouse_gene_id = self.convert_to_mouse_gene_id(
+ self.dataset.group.species.lower(), gene_id)
+
+ if mouse_gene_id and str(mouse_gene_id).find(";") == -1:
+ #print("gene_symbols:", input_trait_mouse_gene_id + " / " + mouse_gene_id)
+ result = g.db.execute(
+ """SELECT value
+ FROM LCorrRamin3
+ WHERE GeneId1='%s' and
+ GeneId2='%s'
+ """ % (escape(mouse_gene_id), escape(input_trait_mouse_gene_id))
+ ).fetchone()
+ if not result:
+ result = g.db.execute("""SELECT value
+ FROM LCorrRamin3
+ WHERE GeneId2='%s' and
+ GeneId1='%s'
+ """ % (escape(mouse_gene_id), escape(input_trait_mouse_gene_id))
+ ).fetchone()
+ if result:
+ #print("result:", result)
+ lit_corr = result.value
+ lit_corr_data[trait] = [gene_id, lit_corr]
+ else:
+ lit_corr_data[trait] = [gene_id, 0]
+ else:
+ lit_corr_data[trait] = [gene_id, 0]
+
+ lit_corr_data = collections.OrderedDict(sorted(list(lit_corr_data.items()),
+ key=lambda t: -abs(t[1][1])))
+
+ return lit_corr_data
+
+ def do_tissue_correlation_for_all_traits(self, tissue_dataset_id=1):
+ # Gets tissue expression values for the primary trait
+ # pylint: disable = E, W, R, C
+ primary_trait_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values(
+ symbol_list=[self.this_trait.symbol])
+
+ if self.this_trait.symbol.lower() in primary_trait_tissue_vals_dict:
+ primary_trait_tissue_values = primary_trait_tissue_vals_dict[self.this_trait.symbol.lower(
+ )]
+
+ #print("trait_gene_symbols: ", pf(trait_gene_symbols.values()))
+ corr_result_tissue_vals_dict = correlation_functions.get_trait_symbol_and_tissue_values(
+ symbol_list=list(self.trait_symbol_dict.values()))
+
+ #print("corr_result_tissue_vals: ", pf(corr_result_tissue_vals_dict))
+
+ #print("trait_gene_symbols: ", pf(trait_gene_symbols))
+
+ tissue_corr_data = {}
+ for trait, symbol in list(self.trait_symbol_dict.items()):
+ if symbol and symbol.lower() in corr_result_tissue_vals_dict:
+ this_trait_tissue_values = corr_result_tissue_vals_dict[symbol.lower(
+ )]
+
+ result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values,
+ this_trait_tissue_values,
+ self.corr_method)
+
+ tissue_corr_data[trait] = [symbol, result[0], result[2]]
+
+ tissue_corr_data = collections.OrderedDict(sorted(list(tissue_corr_data.items()),
+ key=lambda t: -abs(t[1][1])))
+
+ def get_sample_r_and_p_values(self, trait, target_samples):
+ """Calculates the sample r (or rho) and p-value
+
+ Given a primary trait and a target trait's sample values,
+ calculates either the pearson r or spearman rho and the p-value
+ using the corresponding scipy functions.
+
+ """
+ # pylint: disable = E, W, R, C
+ self.this_trait_vals = []
+ target_vals = []
+
+ for index, sample in enumerate(self.target_dataset.samplelist):
+ if sample in self.sample_data:
+ sample_value = self.sample_data[sample]
+ target_sample_value = target_samples[index]
+ self.this_trait_vals.append(sample_value)
+ target_vals.append(target_sample_value)
+
+ self.this_trait_vals, target_vals, num_overlap = normalize_values(
+ 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:
+
+ self.correlation_data[trait] = [
+ sample_r, sample_p, num_overlap]
+
+ def convert_to_mouse_gene_id(self, species=None, gene_id=None):
+ """If the species is rat or human, translate the gene_id to the mouse geneid
+
+ If there is no input gene_id or there's no corresponding mouse gene_id, return None
+
+ """
+ if not gene_id:
+ return None
+
+ mouse_gene_id = None
+ if "species" == "mouse":
+ mouse_gene_id = gene_id
+
+ elif species == 'rat':
+ query = """SELECT mouse
+ FROM GeneIDXRef
+ WHERE rat='%s'""" % escape(gene_id)
+
+ result = g.db.execute(query).fetchone()
+ if result != None:
+ mouse_gene_id = result.mouse
+
+ elif species == "human":
+
+ query = """SELECT mouse
+ FROM GeneIDXRef
+ WHERE human='%s'""" % escape(gene_id)
+
+ result = g.db.execute(query).fetchone()
+ if result != None:
+ mouse_gene_id = result.mouse
+
+ return mouse_gene_id
+
+ def do_correlation(self, start_vars, create_dataset=create_dataset,
+ create_trait=create_trait,
+ get_species_dataset_trait=get_species_dataset_trait):
+ # pylint: disable = E, W, R, C
+ # probably refactor start_vars being passed twice
+ # this method aims to replace the do_correlation but also add dependendency injection
+ # to enable testing
+
+ # should maybe refactor below code more or less works the same
+ if start_vars["dataset"] == "Temp":
+ self.dataset = create_dataset(
+ dataset_name="Temp", dataset_type="Temp", group_name=start_vars['group'])
+
+ self.trait_id = start_vars["trait_id"]
+
+ self.this_trait = create_trait(dataset=self.dataset,
+ name=self.trait_id,
+ cellid=None)
+
+ else:
+
+ get_species_dataset_trait(self, start_vars)
+
+ corr_samples_group = start_vars['corr_samples_group']
+ self.sample_data = {}
+ self.corr_type = start_vars['corr_type']
+ self.corr_method = start_vars['corr_sample_method']
+ self.min_expr = float(
+ start_vars["min_expr"]) if start_vars["min_expr"] != "" else None
+ self.p_range_lower = float(
+ start_vars["p_range_lower"]) if start_vars["p_range_lower"] != "" else -1.0
+ self.p_range_upper = float(
+ start_vars["p_range_upper"]) if start_vars["p_range_upper"] != "" else 1.0
+
+ if ("loc_chr" in start_vars and "min_loc_mb" in start_vars and "max_loc_mb" in start_vars):
+ self.location_type = str(start_vars['location_type'])
+ self.location_chr = str(start_vars['loc_chr'])
+
+ try:
+
+ # the code is below is basically a temporary fix
+ self.min_location_mb = int(start_vars['min_loc_mb'])
+ self.max_location_mb = int(start_vars['max_loc_mb'])
+ except Exception as e:
+ self.min_location_mb = None
+ self.max_location_mb = None
+
+ else:
+ self.location_type = self.location_chr = self.min_location_mb = self.max_location_mb = None
+
+ self.get_formatted_corr_type()
+
+ self.return_number = int(start_vars['corr_return_results'])
+
+ primary_samples = self.dataset.group.samplelist
+
+
+ # The two if statements below append samples to the sample list based upon whether the user
+ # rselected Primary Samples Only, Other Samples Only, or All Samples
+
+ if self.dataset.group.parlist != None:
+ primary_samples += self.dataset.group.parlist
+
+ if self.dataset.group.f1list != None:
+
+ primary_samples += self.dataset.group.f1list
+
+ # If either BXD/whatever Only or All Samples, append all of that group's samplelist
+
+ if corr_samples_group != 'samples_other':
+
+ # print("primary samples are *****",primary_samples)
+
+ self.process_samples(start_vars, primary_samples)
+
+ if corr_samples_group != 'samples_primary':
+ if corr_samples_group == 'samples_other':
+ primary_samples = [x for x in primary_samples if x not in (
+ self.dataset.group.parlist + self.dataset.group.f1list)]
+
+ self.process_samples(start_vars, list(self.this_trait.data.keys()), primary_samples)
+
+ self.target_dataset = create_dataset(start_vars['corr_dataset'])
+ # when you add code to retrieve the trait_data for target dataset got gets very slow
+ import time
+
+ init_time = time.time()
+ self.target_dataset.get_trait_data(list(self.sample_data.keys()))
+
+ aft_time = time.time() - init_time
+
+ self.header_fields = get_header_fields(
+ self.target_dataset.type, self.corr_method)
+
+ if self.target_dataset.type == "ProbeSet":
+ self.filter_cols = [7, 6]
+
+ elif self.target_dataset.type == "Publish":
+ self.filter_cols = [6, 0]
+
+ else:
+ self.filter_cols = [4, 0]
+
+ self.correlation_results = []
+
+ self.correlation_data = {}
+
+ if self.corr_type == "tissue":
+ self.trait_symbol_dict = self.dataset.retrieve_genes("Symbol")
+
+ tissue_corr_data = self.do_tissue_correlation_for_all_traits()
+ if tissue_corr_data != None:
+ for trait in list(tissue_corr_data.keys())[:self.return_number]:
+ self.get_sample_r_and_p_values(
+ trait, self.target_dataset.trait_data[trait])
+ else:
+ for trait, values in list(self.target_dataset.trait_data.items()):
+ self.get_sample_r_and_p_values(trait, values)
+
+ elif self.corr_type == "lit":
+ self.trait_geneid_dict = self.dataset.retrieve_genes("GeneId")
+ lit_corr_data = self.do_lit_correlation_for_all_traits()
+
+ for trait in list(lit_corr_data.keys())[:self.return_number]:
+ self.get_sample_r_and_p_values(
+ trait, self.target_dataset.trait_data[trait])
+
+ elif self.corr_type == "sample":
+ for trait, values in list(self.target_dataset.trait_data.items()):
+ self.get_sample_r_and_p_values(trait, values)
+
+ self.correlation_data = collections.OrderedDict(sorted(list(self.correlation_data.items()),
+ key=lambda t: -abs(t[1][0])))
+
+ # ZS: Convert min/max chromosome to an int for the location range option
+
+ """
+ took 20.79 seconds took compute all the above majority of time taken on retrieving target dataset trait
+ info
+ """
+
+ initial_time_chr = time.time()
+
+ range_chr_as_int = None
+ for order_id, chr_info in list(self.dataset.species.chromosomes.chromosomes.items()):
+ if 'loc_chr' in start_vars:
+ if chr_info.name == self.location_chr:
+ range_chr_as_int = order_id
+
+ 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
+
+ chr_as_int = 0
+ for order_id, chr_info in list(self.dataset.species.chromosomes.chromosomes.items()):
+ if self.location_type == "highest_lod":
+ if chr_info.name == trait_object.locus_chr:
+ chr_as_int = order_id
+ else:
+ if chr_info.name == trait_object.chr:
+ chr_as_int = order_id
+
+ if (float(self.correlation_data[trait][0]) >= self.p_range_lower and
+ float(self.correlation_data[trait][0]) <= self.p_range_upper):
+
+ if (self.target_dataset.type == "ProbeSet" or self.target_dataset.type == "Publish") and bool(trait_object.mean):
+ if (self.min_expr != None) and (float(trait_object.mean) < self.min_expr):
+ continue
+
+ if range_chr_as_int != None and (chr_as_int != range_chr_as_int):
+ continue
+ if self.location_type == "highest_lod":
+ if (self.min_location_mb != None) and (float(trait_object.locus_mb) < float(self.min_location_mb)):
+ continue
+ if (self.max_location_mb != None) and (float(trait_object.locus_mb) > float(self.max_location_mb)):
+ continue
+ else:
+ if (self.min_location_mb != None) and (float(trait_object.mb) < float(self.min_location_mb)):
+ continue
+ if (self.max_location_mb != None) and (float(trait_object.mb) > float(self.max_location_mb)):
+ continue
+
+ (trait_object.sample_r,
+ trait_object.sample_p,
+ trait_object.num_overlap) = self.correlation_data[trait]
+
+ # Set some sane defaults
+ trait_object.tissue_corr = 0
+ trait_object.tissue_pvalue = 0
+ trait_object.lit_corr = 0
+ if self.corr_type == "tissue" and tissue_corr_data != None:
+ trait_object.tissue_corr = tissue_corr_data[trait][1]
+ trait_object.tissue_pvalue = tissue_corr_data[trait][2]
+ elif self.corr_type == "lit":
+ trait_object.lit_corr = lit_corr_data[trait][1]
+
+ self.correlation_results.append(trait_object)
+
+ """
+ above takes time with respect to size of traits i.e n=100,500,.....t_size
+ """
+
+ if self.corr_type != "lit" and self.dataset.type == "ProbeSet" and self.target_dataset.type == "ProbeSet":
+ # self.do_lit_correlation_for_trait_list()
+ self.do_lit_correlation_for_trait_list()
+
+ if self.corr_type != "tissue" and self.dataset.type == "ProbeSet" and self.target_dataset.type == "ProbeSet":
+ self.do_tissue_correlation_for_trait_list()
+ # self.do_lit_correlation_for_trait_list()
+
+ self.json_results = generate_corr_json(
+ self.correlation_results, self.this_trait, self.dataset, self.target_dataset)
+
+ # org mode by bons
+
+ # DVORAKS
+ # klavaro for touch typing
+ # archwiki for documentation
+ # exwm for window manager ->13
+
+ # will fit perfectly with genenetwork 2 with change of anything if return self
+
+ # alternative for this
+ return self.json_results
+ # return {
+ # # "Results": "succeess",
+ # # "return_number": self.return_number,
+ # # "primary_samples": primary_samples,
+ # # "time_taken": 12,
+ # # "correlation_data": self.correlation_data,
+ # "correlation_json": self.json_results
+ # }
+
+
+def do_bicor(this_trait_vals, target_trait_vals):
+ # pylint: disable = E, W, R, C
+ r_library = ro.r["library"] # Map the library function
+ r_options = ro.r["options"] # Map the options function
+
+ r_library("WGCNA")
+ r_bicor = ro.r["bicorAndPvalue"] # Map the bicorAndPvalue function
+
+ r_options(stringsAsFactors=False)
+
+ this_vals = ro.Vector(this_trait_vals)
+ target_vals = ro.Vector(target_trait_vals)
+
+ the_r, the_p, _fisher_transform, _the_t, _n_obs = [
+ numpy.asarray(x) for x in r_bicor(x=this_vals, y=target_vals)]
+
+ return the_r, the_p
+
+
+def get_header_fields(data_type, corr_method):
+ """function to get header fields when doing correlation"""
+ if data_type == "ProbeSet":
+ if corr_method == "spearman":
+
+ header_fields = ['Index',
+ 'Record',
+ 'Symbol',
+ 'Description',
+ 'Location',
+ 'Mean',
+ 'Sample rho',
+ 'N',
+ 'Sample p(rho)',
+ 'Lit rho',
+ 'Tissue rho',
+ 'Tissue p(rho)',
+ 'Max LRS',
+ 'Max LRS Location',
+ 'Additive Effect']
+
+ else:
+ header_fields = ['Index',
+ 'Record',
+ 'Abbreviation',
+ 'Description',
+ 'Mean',
+ 'Authors',
+ 'Year',
+ 'Sample r',
+ 'N',
+ 'Sample p(r)',
+ 'Max LRS',
+ 'Max LRS Location',
+ 'Additive Effect']
+
+ elif data_type == "Publish":
+ if corr_method == "spearman":
+
+ header_fields = ['Index',
+ 'Record',
+ 'Abbreviation',
+ 'Description',
+ 'Mean',
+ 'Authors',
+ 'Year',
+ 'Sample rho',
+ 'N',
+ 'Sample p(rho)',
+ 'Max LRS',
+ 'Max LRS Location',
+ 'Additive Effect']
+
+ else:
+ header_fields = ['Index',
+ 'Record',
+ 'Abbreviation',
+ 'Description',
+ 'Mean',
+ 'Authors',
+ 'Year',
+ 'Sample r',
+ 'N',
+ 'Sample p(r)',
+ 'Max LRS',
+ 'Max LRS Location',
+ 'Additive Effect']
+
+ else:
+ if corr_method == "spearman":
+ header_fields = ['Index',
+ 'ID',
+ 'Location',
+ 'Sample rho',
+ 'N',
+ 'Sample p(rho)']
+
+ else:
+ header_fields = ['Index',
+ 'ID',
+ 'Location',
+ 'Sample r',
+ 'N',
+ 'Sample p(r)']
+
+ return header_fields
+
+
+def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_api=False):
+ """function to generate corr json data"""
+ #todo refactor this function
+ results_list = []
+ for i, trait in enumerate(corr_results):
+ if trait.view == False:
+ continue
+ results_dict = {}
+ 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"
+ results_dict['location'] = trait.location_repr
+ results_dict['mean'] = "N/A"
+ results_dict['additive'] = "N/A"
+ if bool(trait.description_display):
+ results_dict['description'] = trait.description_display
+ if bool(trait.mean):
+ results_dict['mean'] = f"{float(trait.mean):.3f}"
+ try:
+ results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}"
+ except:
+ results_dict['lod_score'] = "N/A"
+ results_dict['lrs_location'] = trait.LRS_location_repr
+ if bool(trait.additive):
+ 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'] = 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'] = f"{float(trait.lit_corr):.3f}"
+ if bool(trait.tissue_corr):
+ 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"
+ results_dict['mean'] = "N/A"
+ 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['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.mean):
+ results_dict['mean'] = f"{float(trait.mean):.3f}"
+ 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
+ try:
+ results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}"
+ except:
+ results_dict['lod_score'] = "N/A"
+ results_dict['lrs_location'] = trait.LRS_location_repr
+ if bool(trait.additive):
+ 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'] = f"{float(trait.sample_p):.3e}"
+ else:
+ results_dict['location'] = trait.location_repr
+ results_dict['sample_r'] = f"{float(trait.sample_r):.3f}"
+ results_dict['num_overlap'] = trait.num_overlap
+ results_dict['sample_p'] = f"{float(trait.sample_p):.3e}"
+
+ results_list.append(results_dict)
+
+ return json.dumps(results_list)
diff --git a/gn3/db/__init__.py b/gn3/db/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/gn3/db/__init__.py
diff --git a/gn3/db/calls.py b/gn3/db/calls.py
new file mode 100644
index 0000000..547bccf
--- /dev/null
+++ b/gn3/db/calls.py
@@ -0,0 +1,51 @@
+"""module contains calls method for db"""
+import json
+import urllib
+from flask import g
+from gn3.utility.logger import getLogger
+logger = getLogger(__name__)
+# should probably put this is env
+USE_GN_SERVER = False
+LOG_SQL = False
+
+GN_SERVER_URL = None
+
+
+def fetch1(query, path=None, func=None):
+ """fetch1 method"""
+ if USE_GN_SERVER and path:
+ result = gn_server(path)
+ if func is not None:
+ res2 = func(result)
+
+ else:
+ res2 = result
+
+ if LOG_SQL:
+ pass
+ # should probably and logger
+ # logger.debug("Replaced SQL call", query)
+
+ # logger.debug(path,res2)
+ return res2
+
+ return fetchone(query)
+
+
+def gn_server(path):
+ """Return JSON record by calling GN_SERVER
+
+ """
+ res = urllib.request.urlopen(GN_SERVER_URL+path)
+ rest = res.read()
+ res2 = json.loads(rest)
+ return res2
+
+
+def fetchone(query):
+ """method to fetchone item from db"""
+ def helper(query):
+ res = g.db.execute(query)
+ return res.fetchone()
+
+ return logger.sql(query, helper)
diff --git a/gn3/db/webqtlDatabaseFunction.py b/gn3/db/webqtlDatabaseFunction.py
new file mode 100644
index 0000000..9e9982b
--- /dev/null
+++ b/gn3/db/webqtlDatabaseFunction.py
@@ -0,0 +1,52 @@
+"""
+# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
+#
+# This program is free software: you can redistribute it and/or modify it
+# under the terms of the GNU Affero General Public License
+# as published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero General Public License for more details.
+#
+# This program is available from Source Forge: at GeneNetwork Project
+# (sourceforge.net/projects/genenetwork/).
+#
+# Contact Drs. Robert W. Williams and Xiaodong Zhou (2010)
+# at rwilliams@uthsc.edu and xzhou15@uthsc.edu
+#
+#
+#
+# This module is used by GeneNetwork project (www.genenetwork.org)
+"""
+
+from gn3.db.calls import fetch1
+
+from gn3.utility.logger import getLogger
+logger = getLogger(__name__)
+
+###########################################################################
+# output: cursor instance
+# function: connect to database and return cursor instance
+###########################################################################
+
+
+def retrieve_species(group):
+ """Get the species of a group (e.g. returns string "mouse" on "BXD"
+
+ """
+ result = fetch1("select Species.Name from Species, InbredSet where InbredSet.Name = '%s' and InbredSet.SpeciesId = Species.Id" % (
+ group), "/cross/"+group+".json", lambda r: (r["species"],))[0]
+ # logger.debug("retrieve_species result:", result)
+ return result
+
+
+def retrieve_species_id(group):
+ """retrieve species id method"""
+
+ result = fetch1("select SpeciesId from InbredSet where Name = '%s'" % (
+ group), "/cross/"+group+".json", lambda r: (r["species_id"],))[0]
+ logger.debug("retrieve_species_id result:", result)
+ return result
diff --git a/gn3/utility/__init__.py b/gn3/utility/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/gn3/utility/__init__.py
diff --git a/gn3/utility/bunch.py b/gn3/utility/bunch.py
new file mode 100644
index 0000000..c1fd907
--- /dev/null
+++ b/gn3/utility/bunch.py
@@ -0,0 +1,16 @@
+"""module contains Bunch class a dictionary like with object notation """
+
+from pprint import pformat as pf
+
+
+class Bunch:
+ """Like a dictionary but using object notation"""
+
+ def __init__(self, **kw):
+ self.__dict__ = kw
+
+ def __repr__(self):
+ return pf(self.__dict__)
+
+ def __str__(self):
+ return self.__class__.__name__
diff --git a/gn3/utility/chunks.py b/gn3/utility/chunks.py
new file mode 100644
index 0000000..fa27a39
--- /dev/null
+++ b/gn3/utility/chunks.py
@@ -0,0 +1,32 @@
+"""module for chunks functions"""
+
+import math
+
+
+def divide_into_chunks(the_list, number_chunks):
+ """Divides a list into approximately number_chunks smaller lists
+
+ >>> divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 3)
+ [[1, 2, 7], [3, 22, 8], [5, 22, 333]]
+ >>> divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 4)
+ [[1, 2, 7], [3, 22, 8], [5, 22, 333]]
+ >>> divide_into_chunks([1, 2, 7, 3, 22, 8, 5, 22, 333], 5)
+ [[1, 2], [7, 3], [22, 8], [5, 22], [333]]
+ >>>
+
+ """
+ length = len(the_list)
+
+ if length == 0:
+ return [[]]
+
+ if length <= number_chunks:
+ number_chunks = length
+
+ chunksize = int(math.ceil(length / number_chunks))
+
+ chunks = []
+ for counter in range(0, length, chunksize):
+ chunks.append(the_list[counter:counter+chunksize])
+
+ return chunks
diff --git a/gn3/utility/corr_result_helpers.py b/gn3/utility/corr_result_helpers.py
new file mode 100644
index 0000000..a68308e
--- /dev/null
+++ b/gn3/utility/corr_result_helpers.py
@@ -0,0 +1,45 @@
+"""module contains helper function for corr results"""
+
+#pylint:disable=C0103
+#above disable snake_case for variable tod refactor
+def normalize_values(a_values, b_values):
+ """
+ Trim two lists of values to contain only the values they both share
+
+ Given two lists of sample values, trim each list so that it contains
+ only the samples that contain a value in both lists. Also returns
+ the number of such samples.
+
+ >>> 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)
+
+ """
+ a_new = []
+ b_new = []
+ for a, b in zip(a_values, b_values):
+ if (a and b is not None):
+ a_new.append(a)
+ b_new.append(b)
+ return a_new, b_new, len(a_new)
+
+
+def common_keys(a_samples, b_samples):
+ """
+ >>> 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)
+ >>> sorted(common_keys(a, b))
+ ['BXD1', 'BXD14']
+ """
+ return set(a_samples.keys()).intersection(set(b_samples.keys()))
+
+
+def normalize_values_with_samples(a_samples, b_samples):
+ """function to normalize values with samples"""
+ common_samples = common_keys(a_samples, b_samples)
+ a_new = {}
+ b_new = {}
+ for sample in common_samples:
+ a_new[sample] = a_samples[sample]
+ b_new[sample] = b_samples[sample]
+
+ return a_new, b_new, len(a_new)
diff --git a/gn3/utility/db_tools.py b/gn3/utility/db_tools.py
new file mode 100644
index 0000000..446acda
--- /dev/null
+++ b/gn3/utility/db_tools.py
@@ -0,0 +1,19 @@
+"""module for db_tools"""
+from MySQLdb import escape_string as escape_
+
+
+def create_in_clause(items):
+ """Create an in clause for mysql"""
+ in_clause = ', '.join("'{}'".format(x) for x in mescape(*items))
+ in_clause = '( {} )'.format(in_clause)
+ return in_clause
+
+
+def mescape(*items):
+ """Multiple escape"""
+ return [escape_(str(item)).decode('utf8') for item in items]
+
+
+def escape(string_):
+ """escape function"""
+ return escape_(string_).decode('utf8')
diff --git a/gn3/utility/get_group_samplelists.py b/gn3/utility/get_group_samplelists.py
new file mode 100644
index 0000000..8fb322a
--- /dev/null
+++ b/gn3/utility/get_group_samplelists.py
@@ -0,0 +1,47 @@
+
+"""module for group samplelist"""
+import os
+
+#todo close the files after opening
+def get_samplelist(file_type, geno_file):
+ """get samplelist function"""
+ if file_type == "geno":
+ return get_samplelist_from_geno(geno_file)
+ elif file_type == "plink":
+ return get_samplelist_from_plink(geno_file)
+
+def get_samplelist_from_geno(genofilename):
+ if os.path.isfile(genofilename + '.gz'):
+ genofilename += '.gz'
+ genofile = gzip.open(genofilename)
+ else:
+ genofile = open(genofilename)
+
+ for line in genofile:
+ line = line.strip()
+ if not line:
+ continue
+ if line.startswith(("#", "@")):
+ continue
+ break
+
+ headers = line.split("\t")
+
+ if headers[3] == "Mb":
+ samplelist = headers[4:]
+ else:
+ samplelist = headers[3:]
+ return samplelist
+
+
+
+def get_samplelist_from_plink(genofilename):
+ """get samplelist from plink"""
+ genofile = open(genofilename)
+
+ samplelist = []
+ for line in genofile:
+ line = line.split(" ")
+ samplelist.append(line[1])
+
+ return samplelist
diff --git a/gn3/utility/helper_functions.py b/gn3/utility/helper_functions.py
new file mode 100644
index 0000000..f5a8b80
--- /dev/null
+++ b/gn3/utility/helper_functions.py
@@ -0,0 +1,24 @@
+"""module contains general helper functions """
+from gn3.base.data_set import create_dataset
+from gn3.base.trait import create_trait
+from gn3.base.species import TheSpecies
+
+
+def get_species_dataset_trait(self, start_vars):
+ """function to get species dataset and trait"""
+ if "temp_trait" in list(start_vars.keys()):
+ if start_vars['temp_trait'] == "True":
+ self.dataset = create_dataset(
+ dataset_name="Temp", dataset_type="Temp", group_name=start_vars['group'])
+
+ else:
+ self.dataset = create_dataset(start_vars['dataset'])
+
+ else:
+ self.dataset = create_dataset(start_vars['dataset'])
+ self.species = TheSpecies(dataset=self.dataset)
+
+ self.this_trait = create_trait(dataset=self.dataset,
+ name=start_vars['trait_id'],
+ cellid=None,
+ get_qtl_info=True)
diff --git a/gn3/utility/hmac.py b/gn3/utility/hmac.py
new file mode 100644
index 0000000..eb39e59
--- /dev/null
+++ b/gn3/utility/hmac.py
@@ -0,0 +1,50 @@
+"""module for hmac """
+
+# pylint: disable-all
+import hmac
+import hashlib
+
+# xtodo work on this file
+
+# from main import app
+
+
+def hmac_creation(stringy):
+ """Helper function to create the actual hmac"""
+
+ # secret = app.config['SECRET_HMAC_CODE']
+ # put in config
+ secret = "my secret"
+ hmaced = hmac.new(bytearray(secret, "latin-1"),
+ bytearray(stringy, "utf-8"),
+ hashlib.sha1)
+ hm = hmaced.hexdigest()
+ # ZS: Leaving the below comment here to ask Pjotr about
+ # "Conventional wisdom is that you don't lose much in terms of security if you throw away up to half of the output."
+ # http://www.w3.org/QA/2009/07/hmac_truncation_in_xml_signatu.html
+ hm = hm[:20]
+ return hm
+
+
+def data_hmac(stringy):
+ """Takes arbitrary data string and appends :hmac so we know data hasn't been tampered with"""
+ return stringy + ":" + hmac_creation(stringy)
+
+
+def url_for_hmac(endpoint, **values):
+ """Like url_for but adds an hmac at the end to insure the url hasn't been tampered with"""
+
+ url = url_for(endpoint, **values)
+
+ hm = hmac_creation(url)
+ if '?' in url:
+ combiner = "&"
+ else:
+ combiner = "?"
+ return url + combiner + "hm=" + hm
+
+
+
+# todo
+# app.jinja_env.globals.update(url_for_hmac=url_for_hmac,
+# data_hmac=data_hmac)
diff --git a/gn3/utility/logger.py b/gn3/utility/logger.py
new file mode 100644
index 0000000..4245a02
--- /dev/null
+++ b/gn3/utility/logger.py
@@ -0,0 +1,163 @@
+"""
+# GeneNetwork logger
+#
+# The standard python logging module is very good. This logger adds a
+# few facilities on top of that. Main one being that it picks up
+# settings for log levels (global and by module) and (potentially)
+# offers some fine grained log levels for the standard levels.
+#
+# All behaviour is defined here. Global settings (defined in
+# default_settings.py).
+#
+# To use logging and settings put this at the top of a module:
+#
+# import utility.logger
+# logger = utility.logger.getLogger(__name__ )
+#
+# To override global behaviour set the LOG_LEVEL in default_settings.py
+# or use an environment variable, e.g.
+#
+# env LOG_LEVEL=INFO ./bin/genenetwork2
+#
+# To override log level for a module replace that with, for example,
+#
+# import logging
+# import utility.logger
+# logger = utility.logger.getLogger(__name__,level=logging.DEBUG)
+#
+# We'll add more overrides soon.
+"""
+# todo incomplete file
+
+# pylint: disable-all
+import logging
+import datetime
+from inspect import isfunction
+from inspect import stack
+
+from pprint import pformat as pf
+
+
+# from utility.tools import LOG_LEVEL, LOG_LEVEL_DEBUG, LOG_SQL
+
+LOG_SQL = True
+
+
+class GNLogger:
+ """A logger class with some additional functionality, such as
+ multiple parameter logging, SQL logging, timing, colors, and lazy
+ functions.
+
+ """
+
+ def __init__(self, name):
+ self.logger = logging.getLogger(name)
+
+ def setLevel(self, value):
+ """Set the undelying log level"""
+ self.logger.setLevel(value)
+
+ def debug(self, *args):
+ """Call logging.debug for multiple args. Use (lazy) debugf and
+level=num to filter on LOG_LEVEL_DEBUG.
+
+ """
+ self.collect(self.logger.debug, *args)
+
+ def debug20(self, *args):
+ """Call logging.debug for multiple args. Use level=num to filter on
+LOG_LEVEL_DEBUG (NYI).
+
+ """
+ if level <= LOG_LEVEL_DEBUG:
+ if self.logger.getEffectiveLevel() < 20:
+ self.collect(self.logger.debug, *args)
+
+ def info(self, *args):
+ """Call logging.info for multiple args"""
+ self.collect(self.logger.info, *args)
+
+ def warning(self, *args):
+ """Call logging.warning for multiple args"""
+ self.collect(self.logger.warning, *args)
+ # self.logger.warning(self.collect(*args))
+
+ def error(self, *args):
+ """Call logging.error for multiple args"""
+ now = datetime.datetime.utcnow()
+ time_str = now.strftime('%H:%M:%S UTC %Y%m%d')
+ l = [time_str]+list(args)
+ self.collect(self.logger.error, *l)
+
+ def infof(self, *args):
+ """Call logging.info for multiple args lazily"""
+ # only evaluate function when logging
+ if self.logger.getEffectiveLevel() < 30:
+ self.collectf(self.logger.debug, *args)
+
+ def debugf(self, level=0, *args):
+ """Call logging.debug for multiple args lazily and handle
+ LOG_LEVEL_DEBUG correctly
+
+ """
+ # only evaluate function when logging
+ if level <= LOG_LEVEL_DEBUG:
+ if self.logger.getEffectiveLevel() < 20:
+ self.collectf(self.logger.debug, *args)
+
+ def sql(self, sqlcommand, fun=None):
+ """Log SQL command, optionally invoking a timed fun"""
+ if LOG_SQL:
+ caller = stack()[1][3]
+ if caller in ['fetchone', 'fetch1', 'fetchall']:
+ caller = stack()[2][3]
+ self.info(caller, sqlcommand)
+ if fun:
+ result = fun(sqlcommand)
+ if LOG_SQL:
+ self.info(result)
+ return result
+
+ def collect(self, fun, *args):
+ """Collect arguments and use fun to output"""
+ out = "."+stack()[2][3]
+ for a in args:
+ if len(out) > 1:
+ out += ": "
+ if isinstance(a, str):
+ out = out + a
+ else:
+ out = out + pf(a, width=160)
+ fun(out)
+
+ def collectf(self, fun, *args):
+ """Collect arguments and use fun to output one by one"""
+ out = "."+stack()[2][3]
+ for a in args:
+ if len(out) > 1:
+ out += ": "
+ if isfunction(a):
+ out += a()
+ else:
+ if isinstance(a, str):
+ out = out + a
+ else:
+ out = out + pf(a, width=160)
+ fun(out)
+
+# Get the module logger. You can override log levels at the
+# module level
+
+
+def getLogger(name, level=None):
+ """method to get logger"""
+ gnlogger = GNLogger(name)
+ _logger = gnlogger.logger
+
+ # if level:
+ # logger.setLevel(level)
+ # else:
+ # logger.setLevel(LOG_LEVEL)
+
+ # logger.info("Log level of "+name+" set to "+logging.getLevelName(logger.getEffectiveLevel()))
+ return gnlogger
diff --git a/gn3/utility/species.py b/gn3/utility/species.py
new file mode 100644
index 0000000..0140d41
--- /dev/null
+++ b/gn3/utility/species.py
@@ -0,0 +1,71 @@
+"""module contains species and chromosomes classes"""
+import collections
+
+from flask import g
+
+
+from gn3.utility.logger import getLogger
+logger = getLogger(__name__)
+
+ # pylint: disable=too-few-public-methods
+ # intentionally disabled check for few public methods
+
+class TheSpecies:
+ """class for Species"""
+
+ def __init__(self, dataset=None, species_name=None):
+ if species_name is not None:
+ self.name = species_name
+ self.chromosomes = Chromosomes(species=self.name)
+ else:
+ self.dataset = dataset
+ self.chromosomes = Chromosomes(dataset=self.dataset)
+
+
+
+class IndChromosome:
+ """class for IndChromosome"""
+
+ def __init__(self, name, length):
+ self.name = name
+ self.length = length
+
+ @property
+ def mb_length(self):
+ """Chromosome length in megabases"""
+ return self.length / 1000000
+
+
+
+
+class Chromosomes:
+ """class for Chromosomes"""
+
+ def __init__(self, dataset=None, species=None):
+ self.chromosomes = collections.OrderedDict()
+ if species is not None:
+ query = """
+ Select
+ Chr_Length.Name, Chr_Length.OrderId, Length from Chr_Length, Species
+ where
+ Chr_Length.SpeciesId = Species.SpeciesId AND
+ Species.Name = '%s'
+ Order by OrderId
+ """ % species.capitalize()
+ else:
+ self.dataset = dataset
+
+ query = """
+ Select
+ Chr_Length.Name, Chr_Length.OrderId, Length from Chr_Length, InbredSet
+ where
+ Chr_Length.SpeciesId = InbredSet.SpeciesId AND
+ InbredSet.Name = '%s'
+ Order by OrderId
+ """ % self.dataset.group.name
+ logger.sql(query)
+ results = g.db.execute(query).fetchall()
+
+ for item in results:
+ self.chromosomes[item.OrderId] = IndChromosome(
+ item.Name, item.Length)
diff --git a/gn3/utility/tools.py b/gn3/utility/tools.py
new file mode 100644
index 0000000..85df9f6
--- /dev/null
+++ b/gn3/utility/tools.py
@@ -0,0 +1,37 @@
+"""module contains general tools forgenenetwork"""
+
+import os
+
+from default_settings import GENENETWORK_FILES
+
+
+def valid_file(file_name):
+ """check if file is valid"""
+ if os.path.isfile(file_name):
+ return file_name
+ return None
+
+
+def valid_path(dir_name):
+ """check if path is valid"""
+ if os.path.isdir(dir_name):
+ return dir_name
+ return None
+
+
+def locate_ignore_error(name, subdir=None):
+ """
+ Locate a static flat file in the GENENETWORK_FILES environment.
+
+ This function does not throw an error when the file is not found
+ but returns None.
+ """
+ base = GENENETWORK_FILES
+ if subdir:
+ base = base+"/"+subdir
+ if valid_path(base):
+ lookfor = base + "/" + name
+ if valid_file(lookfor):
+ return lookfor
+
+ return None
diff --git a/gn3/utility/webqtlUtil.py b/gn3/utility/webqtlUtil.py
new file mode 100644
index 0000000..1c76410
--- /dev/null
+++ b/gn3/utility/webqtlUtil.py
@@ -0,0 +1,66 @@
+"""
+# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
+#
+# This program is free software: you can redistribute it and/or modify it
+# under the terms of the GNU Affero General Public License
+# as published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero General Public License for more details.
+#
+# This program is available from Source Forge: at GeneNetwork Project
+# (sourceforge.net/projects/genenetwork/).
+#
+# Contact Drs. Robert W. Williams and Xiaodong Zhou (2010)
+# at rwilliams@uthsc.edu and xzhou15@uthsc.edu
+#
+#
+#
+# This module is used by GeneNetwork project (www.genenetwork.org)
+#
+# Created by GeneNetwork Core Team 2010/08/10
+#
+# Last updated by GeneNetwork Core Team 2010/10/20
+
+# from base import webqtlConfig
+
+# NL, 07/27/2010. moved from webqtlForm.py
+# Dict of Parents and F1 information, In the order of [F1, Mat, Pat]
+
+"""
+ParInfo = {
+ 'BXH': ['BHF1', 'HBF1', 'C57BL/6J', 'C3H/HeJ'],
+ 'AKXD': ['AKF1', 'KAF1', 'AKR/J', 'DBA/2J'],
+ 'BXD': ['B6D2F1', 'D2B6F1', 'C57BL/6J', 'DBA/2J'],
+ 'C57BL-6JxC57BL-6NJF2': ['', '', 'C57BL/6J', 'C57BL/6NJ'],
+ 'BXD300': ['B6D2F1', 'D2B6F1', 'C57BL/6J', 'DBA/2J'],
+ 'B6BTBRF2': ['B6BTBRF1', 'BTBRB6F1', 'C57BL/6J', 'BTBRT<+>tf/J'],
+ 'BHHBF2': ['B6HF2', 'HB6F2', 'C57BL/6J', 'C3H/HeJ'],
+ 'BHF2': ['B6HF2', 'HB6F2', 'C57BL/6J', 'C3H/HeJ'],
+ 'B6D2F2': ['B6D2F1', 'D2B6F1', 'C57BL/6J', 'DBA/2J'],
+ 'BDF2-1999': ['B6D2F2', 'D2B6F2', 'C57BL/6J', 'DBA/2J'],
+ 'BDF2-2005': ['B6D2F1', 'D2B6F1', 'C57BL/6J', 'DBA/2J'],
+ 'CTB6F2': ['CTB6F2', 'B6CTF2', 'C57BL/6J', 'Castaneous'],
+ 'CXB': ['CBF1', 'BCF1', 'C57BL/6ByJ', 'BALB/cByJ'],
+ 'AXBXA': ['ABF1', 'BAF1', 'C57BL/6J', 'A/J'],
+ 'AXB': ['ABF1', 'BAF1', 'C57BL/6J', 'A/J'],
+ 'BXA': ['BAF1', 'ABF1', 'C57BL/6J', 'A/J'],
+ 'LXS': ['LSF1', 'SLF1', 'ISS', 'ILS'],
+ 'HXBBXH': ['SHR_BNF1', 'BN_SHRF1', 'BN-Lx/Cub', 'SHR/OlaIpcv'],
+ 'BayXSha': ['BayXShaF1', 'ShaXBayF1', 'Bay-0', 'Shahdara'],
+ 'ColXBur': ['ColXBurF1', 'BurXColF1', 'Col-0', 'Bur-0'],
+ 'ColXCvi': ['ColXCviF1', 'CviXColF1', 'Col-0', 'Cvi'],
+ 'SXM': ['SMF1', 'MSF1', 'Steptoe', 'Morex'],
+ 'HRDP': ['SHR_BNF1', 'BN_SHRF1', 'BN-Lx/Cub', 'SHR/OlaIpcv']
+}
+
+
+def has_access_to_confidentail_phenotype_trait(privilege, username, authorized_users):
+ """function to access to confidential phenotype Traits further implementation needed"""
+ access_to_confidential_phenotype_trait = 0
+
+ results = (privilege, username, authorized_users)
+ return access_to_confidential_phenotype_trait
diff --git a/guix.scm b/guix.scm
index f84b819..5f779be 100644
--- a/guix.scm
+++ b/guix.scm
@@ -27,11 +27,13 @@
(gnu packages base)
(gnu packages check)
(gnu packages databases)
+ (gnu packages statistics)
(gnu packages python)
(gnu packages python-check)
(gnu packages python-crypto)
(gnu packages python-web)
(gnu packages python-xyz)
+ (gnu packages python-science)
((guix build utils) #:select (with-directory-excursion))
(guix build-system python)
(guix gexp)
@@ -66,7 +68,6 @@
#:select? git-file?))
(propagated-inputs `(("coreutils" ,coreutils)
("gemma-wrapper" ,gemma-wrapper)
- ("jupyter" ,jupyter)
("python-bcrypt" ,python-bcrypt)
("python" ,python-wrapper)
("python-flask" ,python-flask)
@@ -75,7 +76,13 @@
("python-mypy" ,python-mypy)
("python-mypy-extensions" ,python-mypy-extensions)
("python-redis" ,python-redis)
- ("python-pylint" ,python-pylint)))
+ ("python-scipy" ,python-scipy)
+ ;; Remove one of these!
+ ("python-sqlalchemy" ,python-sqlalchemy)
+ ("python-mysqlclient" ,python-mysqlclient)
+ ;; This requires R in it's path
+ ;; TODO: Remove!
+ ("python-rpy2" ,python-rpy2)))
(build-system python-build-system)
(home-page "https://github.com/genenetwork/genenetwork3")
(synopsis "GeneNetwork3 API for data science and machine learning.")
diff --git a/requirements.txt b/requirements.txt
index 7dc7a01..c76e429 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,23 +1,11 @@
-astroid==2.4.2
bcrypt==3.1.7
-cffi==1.14.5
click==7.1.2
Flask==1.1.2
-isort==4.3.21
itsdangerous==1.1.0
Jinja2==2.11.3
-lazy-object-proxy==1.4.3
MarkupSafe==1.1.1
-mccabe==0.6.1
-mypy==0.790
-mypy-extensions==0.4.3
+mysqlclient==2.0.1
numpy==1.17.3
-pycparser==2.20
-pylint==2.5.3
-redis==3.5.3
-six==1.15.0
-toml==0.10.2
-typed-ast==1.4.2
-typing-extensions==3.7.4.3
+scipy==1.6.0
+SQLAlchemy==1.3.20
Werkzeug==1.0.1
-wrapt==1.12.1
diff --git a/tests/integration/correlation_data.json b/tests/integration/correlation_data.json
new file mode 100644
index 0000000..87d24e3
--- /dev/null
+++ b/tests/integration/correlation_data.json
@@ -0,0 +1,18 @@
+{
+ "primary_samples": "C57BL/6J,DBA/2J,B6D2F1,D2B6F1,BXD1,BXD2,BXD5,BXD6,BXD8,BXD9,BXD11,BXD12,BXD13,BXD14,BXD15,BXD16,BXD18,BXD19,BXD20,BXD21,BXD22,BXD23,BXD24,BXD24a,BXD25,BXD27,BXD28,BXD29,BXD30,BXD31,BXD32,BXD33,BXD34,BXD35,BXD36,BXD37,BXD38,BXD39,BXD40,BXD41,BXD42,BXD43,BXD44,BXD45,BXD48,BXD48a,BXD49,BXD50,BXD51,BXD52,BXD53,BXD54,BXD55,BXD56,BXD59,BXD60,BXD61,BXD62,BXD63,BXD64,BXD65,BXD65a,BXD65b,BXD66,BXD67,BXD68,BXD69,BXD70,BXD71,BXD72,BXD73,BXD73a,BXD73b,BXD74,BXD75,BXD76,BXD77,BXD78,BXD79,BXD81,BXD83,BXD84,BXD85,BXD86,BXD87,BXD88,BXD89,BXD90,BXD91,BXD93,BXD94,BXD95,BXD98,BXD99,BXD100,BXD101,BXD102,BXD104,BXD105,BXD106,BXD107,BXD108,BXD109,BXD110,BXD111,BXD112,BXD113,BXD114,BXD115,BXD116,BXD117,BXD119,BXD120,BXD121,BXD122,BXD123,BXD124,BXD125,BXD126,BXD127,BXD128,BXD128a,BXD130,BXD131,BXD132,BXD133,BXD134,BXD135,BXD136,BXD137,BXD138,BXD139,BXD141,BXD142,BXD144,BXD145,BXD146,BXD147,BXD148,BXD149,BXD150,BXD151,BXD152,BXD153,BXD154,BXD155,BXD156,BXD157,BXD160,BXD161,BXD162,BXD165,BXD168,BXD169,BXD170,BXD171,BXD172,BXD173,BXD174,BXD175,BXD176,BXD177,BXD178,BXD180,BXD181,BXD183,BXD184,BXD186,BXD187,BXD188,BXD189,BXD190,BXD191,BXD192,BXD193,BXD194,BXD195,BXD196,BXD197,BXD198,BXD199,BXD200,BXD201,BXD202,BXD203,BXD204,BXD205,BXD206,BXD207,BXD208,BXD209,BXD210,BXD211,BXD212,BXD213,BXD214,BXD215,BXD216,BXD217,BXD218,BXD219,BXD220",
+ "trait_id": "1444666_at",
+ "dataset": "HC_M2_0606_P",
+ "sample_vals": "{\"C57BL/6J\":\"6.638\",\"DBA/2J\":\"6.266\",\"B6D2F1\":\"6.494\",\"D2B6F1\":\"6.565\",\"BXD1\":\"6.357\",\"BXD2\":\"6.456\",\"BXD5\":\"6.590\",\"BXD6\":\"6.568\",\"BXD8\":\"6.581\",\"BXD9\":\"6.322\",\"BXD11\":\"6.519\",\"BXD12\":\"6.543\",\"BXD13\":\"6.636\",\"BXD14\":\"x\",\"BXD15\":\"6.578\",\"BXD16\":\"6.636\",\"BXD18\":\"x\",\"BXD19\":\"6.562\",\"BXD20\":\"6.610\",\"BXD21\":\"6.668\",\"BXD22\":\"6.607\",\"BXD23\":\"6.513\",\"BXD24\":\"6.601\",\"BXD24a\":\"x\",\"BXD25\":\"x\",\"BXD27\":\"6.573\",\"BXD28\":\"6.639\",\"BXD29\":\"6.656\",\"BXD30\":\"x\",\"BXD31\":\"6.549\",\"BXD32\":\"6.502\",\"BXD33\":\"6.584\",\"BXD34\":\"6.261\",\"BXD35\":\"x\",\"BXD36\":\"x\",\"BXD37\":\"x\",\"BXD38\":\"6.646\",\"BXD39\":\"6.584\",\"BXD40\":\"6.790\",\"BXD41\":\"x\",\"BXD42\":\"6.536\",\"BXD43\":\"6.476\",\"BXD44\":\"6.545\",\"BXD45\":\"6.742\",\"BXD48\":\"6.393\",\"BXD48a\":\"6.618\",\"BXD49\":\"x\",\"BXD50\":\"6.496\",\"BXD51\":\"6.494\",\"BXD52\":\"x\",\"BXD53\":\"x\",\"BXD54\":\"x\",\"BXD55\":\"6.263\",\"BXD56\":\"x\",\"BXD59\":\"x\",\"BXD60\":\"6.541\",\"BXD61\":\"6.662\",\"BXD62\":\"6.628\",\"BXD63\":\"6.556\",\"BXD64\":\"6.572\",\"BXD65\":\"6.530\",\"BXD65a\":\"6.280\",\"BXD65b\":\"6.490\",\"BXD66\":\"6.608\",\"BXD67\":\"6.534\",\"BXD68\":\"6.352\",\"BXD69\":\"6.548\",\"BXD70\":\"6.520\",\"BXD71\":\"x\",\"BXD72\":\"x\",\"BXD73\":\"6.484\",\"BXD73a\":\"6.486\",\"BXD73b\":\"x\",\"BXD74\":\"6.639\",\"BXD75\":\"6.401\",\"BXD76\":\"6.452\",\"BXD77\":\"6.568\",\"BXD78\":\"x\",\"BXD79\":\"6.642\",\"BXD81\":\"x\",\"BXD83\":\"6.446\",\"BXD84\":\"6.582\",\"BXD85\":\"6.484\",\"BXD86\":\"6.877\",\"BXD87\":\"6.474\",\"BXD88\":\"x\",\"BXD89\":\"6.676\",\"BXD90\":\"6.644\",\"BXD91\":\"x\",\"BXD93\":\"6.620\",\"BXD94\":\"6.528\",\"BXD95\":\"x\",\"BXD98\":\"6.486\",\"BXD99\":\"6.530\",\"BXD100\":\"x\",\"BXD101\":\"x\",\"BXD102\":\"x\",\"BXD104\":\"x\",\"BXD105\":\"x\",\"BXD106\":\"x\",\"BXD107\":\"x\",\"BXD108\":\"x\",\"BXD109\":\"x\",\"BXD110\":\"x\",\"BXD111\":\"x\",\"BXD112\":\"x\",\"BXD113\":\"x\",\"BXD114\":\"x\",\"BXD115\":\"x\",\"BXD116\":\"x\",\"BXD117\":\"x\",\"BXD119\":\"x\",\"BXD120\":\"x\",\"BXD121\":\"x\",\"BXD122\":\"x\",\"BXD123\":\"x\",\"BXD124\":\"x\",\"BXD125\":\"x\",\"BXD126\":\"x\",\"BXD127\":\"x\",\"BXD128\":\"x\",\"BXD128a\":\"x\",\"BXD130\":\"x\",\"BXD131\":\"x\",\"BXD132\":\"x\",\"BXD133\":\"x\",\"BXD134\":\"x\",\"BXD135\":\"x\",\"BXD136\":\"x\",\"BXD137\":\"x\",\"BXD138\":\"x\",\"BXD139\":\"x\",\"BXD141\":\"x\",\"BXD142\":\"x\",\"BXD144\":\"x\",\"BXD145\":\"x\",\"BXD146\":\"x\",\"BXD147\":\"x\",\"BXD148\":\"x\",\"BXD149\":\"x\",\"BXD150\":\"x\",\"BXD151\":\"x\",\"BXD152\":\"x\",\"BXD153\":\"x\",\"BXD154\":\"x\",\"BXD155\":\"x\",\"BXD156\":\"x\",\"BXD157\":\"x\",\"BXD160\":\"x\",\"BXD161\":\"x\",\"BXD162\":\"x\",\"BXD165\":\"x\",\"BXD168\":\"x\",\"BXD169\":\"x\",\"BXD170\":\"x\",\"BXD171\":\"x\",\"BXD172\":\"x\",\"BXD173\":\"x\",\"BXD174\":\"x\",\"BXD175\":\"x\",\"BXD176\":\"x\",\"BXD177\":\"x\",\"BXD178\":\"x\",\"BXD180\":\"x\",\"BXD181\":\"x\",\"BXD183\":\"x\",\"BXD184\":\"x\",\"BXD186\":\"x\",\"BXD187\":\"x\",\"BXD188\":\"x\",\"BXD189\":\"x\",\"BXD190\":\"x\",\"BXD191\":\"x\",\"BXD192\":\"x\",\"BXD193\":\"x\",\"BXD194\":\"x\",\"BXD195\":\"x\",\"BXD196\":\"x\",\"BXD197\":\"x\",\"BXD198\":\"x\",\"BXD199\":\"x\",\"BXD200\":\"x\",\"BXD201\":\"x\",\"BXD202\":\"x\",\"BXD203\":\"x\",\"BXD204\":\"x\",\"BXD205\":\"x\",\"BXD206\":\"x\",\"BXD207\":\"x\",\"BXD208\":\"x\",\"BXD209\":\"x\",\"BXD210\":\"x\",\"BXD211\":\"x\",\"BXD212\":\"x\",\"BXD213\":\"x\",\"BXD214\":\"x\",\"BXD215\":\"x\",\"BXD216\":\"x\",\"BXD217\":\"x\",\"BXD218\":\"x\",\"BXD219\":\"x\",\"BXD220\":\"x\"}",
+ "corr_type": "lit",
+ "corr_dataset": "HC_M2_0606_P",
+ "corr_return_results": "100",
+ "corr_samples_group": "samples_primary",
+ "corr_sample_method": "pearson",
+ "min_expr": "",
+ "location_type": "gene",
+ "loc_chr": "",
+ "min_loc_mb": "",
+ "max_loc_mb": "",
+ "p_range_lower": "-0.60",
+ "p_range_upper": "0.74"
+} \ No newline at end of file
diff --git a/tests/integration/expected_corr_results.json b/tests/integration/expected_corr_results.json
new file mode 100644
index 0000000..b5bbc2d
--- /dev/null
+++ b/tests/integration/expected_corr_results.json
@@ -0,0 +1,1902 @@
+[
+ {
+ "index": 1,
+ "trait_id": "1415758_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415758_at:HC_M2_0606_P:da50fa1141a7d608ab20",
+ "symbol": "Fryl",
+ "description": "furry homolog-like; far 3' UTR",
+ "location": "Chr5: 72.964984",
+ "mean": "9.193",
+ "additive": "-0.081",
+ "lod_score": "4.4",
+ "lrs_location": "Chr1: 196.404284",
+ "sample_r": "-0.407",
+ "num_overlap": 67,
+ "sample_p": "6.234e-04",
+ "lit_corr": "--",
+ "tissue_corr": "-0.221",
+ "tissue_pvalue": "2.780e-01"
+ },
+ {
+ "index": 2,
+ "trait_id": "1415693_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415693_at:HC_M2_0606_P:0959e913366f559ea22b",
+ "symbol": "Derl1",
+ "description": "derlin 1; proximal to mid 3' UTR",
+ "location": "Chr15: 57.702171",
+ "mean": "9.445",
+ "additive": "0.056",
+ "lod_score": "2.1",
+ "lrs_location": "Chr1: 193.731996",
+ "sample_r": "0.398",
+ "num_overlap": 67,
+ "sample_p": "8.614e-04",
+ "lit_corr": "--",
+ "tissue_corr": "0.114",
+ "tissue_pvalue": "5.800e-01"
+ },
+ {
+ "index": 3,
+ "trait_id": "1415753_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415753_at:HC_M2_0606_P:d75ca42e7fa1613364bb",
+ "symbol": "Fam108a",
+ "description": "abhydrolase domain-containing protein FAM108A; last two exons and proximal 3' UTR",
+ "location": "Chr10: 80.046470",
+ "mean": "12.731",
+ "additive": "0.050",
+ "lod_score": "1.5",
+ "lrs_location": "ChrX: 103.404884",
+ "sample_r": "0.384",
+ "num_overlap": 67,
+ "sample_p": "1.344e-03",
+ "lit_corr": "--",
+ "tissue_corr": "0.108",
+ "tissue_pvalue": "5.990e-01"
+ },
+ {
+ "index": 4,
+ "trait_id": "1415740_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415740_at:HC_M2_0606_P:755cdc41d0d50a03b647",
+ "symbol": "Psmc5",
+ "description": "protease (prosome, macropain) 26S subunit, ATPase 5; exons 7, 8, 9",
+ "location": "Chr11: 106.123450",
+ "mean": "12.424",
+ "additive": "0.059",
+ "lod_score": "2.6",
+ "lrs_location": "Chr9: 34.013550",
+ "sample_r": "0.364",
+ "num_overlap": 67,
+ "sample_p": "2.476e-03",
+ "lit_corr": "--",
+ "tissue_corr": "0.333",
+ "tissue_pvalue": "9.696e-02"
+ },
+ {
+ "index": 5,
+ "trait_id": "1415757_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415757_at:HC_M2_0606_P:8bbf06aa2e3aa5530934",
+ "symbol": "Gbf1",
+ "description": "Golgi-specific brefeldin A-resistance factor 1; last exon and proximal 3' UTR",
+ "location": "Chr19: 46.360410",
+ "mean": "9.800",
+ "additive": "-0.062",
+ "lod_score": "2.0",
+ "lrs_location": "Chr17: 52.750885",
+ "sample_r": "0.363",
+ "num_overlap": 67,
+ "sample_p": "2.539e-03",
+ "lit_corr": "--",
+ "tissue_corr": "-0.059",
+ "tissue_pvalue": "7.741e-01"
+ },
+ {
+ "index": 6,
+ "trait_id": "1415768_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415768_a_at:HC_M2_0606_P:5e67109eee04f5da3393",
+ "symbol": "Ube2r2",
+ "description": "ubiquitin-conjugating enzyme E2R 2",
+ "location": "Chr4: 41.137929",
+ "mean": "9.811",
+ "additive": "-0.087",
+ "lod_score": "3.3",
+ "lrs_location": "Chr12: 114.553844",
+ "sample_r": "-0.312",
+ "num_overlap": 67,
+ "sample_p": "1.019e-02",
+ "lit_corr": "--",
+ "tissue_corr": "-0.007",
+ "tissue_pvalue": "9.711e-01"
+ },
+ {
+ "index": 7,
+ "trait_id": "1415670_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415670_at:HC_M2_0606_P:4f82d7374f29ebfacaaf",
+ "symbol": "Copg",
+ "description": "coatomer protein complex, subunit gamma 1; two of the three last exons and proximal 3' UTR",
+ "location": "Chr6: 87.859681",
+ "mean": "11.199",
+ "additive": "-0.113",
+ "lod_score": "3.7",
+ "lrs_location": "Chr1: 157.588921",
+ "sample_r": "0.305",
+ "num_overlap": 67,
+ "sample_p": "1.200e-02",
+ "lit_corr": "--",
+ "tissue_corr": "-0.405",
+ "tissue_pvalue": "4.032e-02"
+ },
+ {
+ "index": 8,
+ "trait_id": "1415742_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415742_at:HC_M2_0606_P:b72a582a1f840a18c3e7",
+ "symbol": "Aup1",
+ "description": "ancient ubiquitous protein 1",
+ "location": "Chr6: 83.006784",
+ "mean": "9.529",
+ "additive": "-0.062",
+ "lod_score": "2.4",
+ "lrs_location": "Chr19: 16.955950",
+ "sample_r": "0.295",
+ "num_overlap": 67,
+ "sample_p": "1.523e-02",
+ "lit_corr": "--",
+ "tissue_corr": "-0.033",
+ "tissue_pvalue": "8.716e-01"
+ },
+ {
+ "index": 9,
+ "trait_id": "1415743_at",
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+ "tissue_pvalue": "9.823e-01"
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+ "trait_id": "1415690_at",
+ "dataset": "HC_M2_0606_P",
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+ "sample_p": "2.986e-02",
+ "lit_corr": "--",
+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
+ },
+ {
+ "index": 11,
+ "trait_id": "1415727_at",
+ "dataset": "HC_M2_0606_P",
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+ "location": "Chr3: 87.860534",
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+ "additive": "-0.076",
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+ "tissue_pvalue": "4.841e-03"
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+ "index": 12,
+ "trait_id": "1415730_at",
+ "dataset": "HC_M2_0606_P",
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+ "sample_p": "3.164e-02",
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+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
+ },
+ {
+ "index": 13,
+ "trait_id": "1415741_at",
+ "dataset": "HC_M2_0606_P",
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+ "tissue_pvalue": "1.812e-01"
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+ "dataset": "HC_M2_0606_P",
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+ "additive": "-0.085",
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+ "tissue_pvalue": "1.621e-03"
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+ {
+ "index": 15,
+ "trait_id": "1415717_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415717_at:HC_M2_0606_P:dd51438830e4033114f8",
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+ "description": "ring finger protein 220; mid 3' UTR",
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+ "mean": "10.778",
+ "additive": "-0.084",
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+ "lrs_location": "Chr4: 122.536808",
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+ "sample_p": "4.816e-02",
+ "lit_corr": "--",
+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
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+ {
+ "index": 16,
+ "trait_id": "1415703_at",
+ "dataset": "HC_M2_0606_P",
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+ "lrs_location": "Chr1: 135.891043",
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+ "tissue_corr": "0.528",
+ "tissue_pvalue": "5.576e-03"
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+ {
+ "index": 17,
+ "trait_id": "1415748_a_at",
+ "dataset": "HC_M2_0606_P",
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+ "description": "dynactin 5; last exon and proximal half of 3' UTR",
+ "location": "Chr7: 129.291923",
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+ "additive": "0.071",
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+ "sample_p": "6.133e-02",
+ "lit_corr": "--",
+ "tissue_corr": "0.064",
+ "tissue_pvalue": "7.557e-01"
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+ {
+ "index": 18,
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+ "dataset": "HC_M2_0606_P",
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+ "tissue_corr": "-0.147",
+ "tissue_pvalue": "4.739e-01"
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+ {
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+ "dataset": "HC_M2_0606_P",
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+ "mean": "11.447",
+ "additive": "-0.051",
+ "lod_score": "2.4",
+ "lrs_location": "Chr15: 87.788313",
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+ "sample_p": "7.356e-02",
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+ "tissue_corr": "-0.559",
+ "tissue_pvalue": "3.015e-03"
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+ {
+ "index": 20,
+ "trait_id": "1415731_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415731_at:HC_M2_0606_P:9e91e97ca1001091a5f3",
+ "symbol": "Angel2",
+ "description": "angel homolog 2; distal 3' UTR",
+ "location": "Chr1: 192.769800",
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+ "additive": "0.062",
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+ "lrs_location": "Chr14: 124.508018",
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+ "tissue_corr": "0.232",
+ "tissue_pvalue": "2.544e-01"
+ },
+ {
+ "index": 21,
+ "trait_id": "1415750_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415750_at:HC_M2_0606_P:c9f757736d57e5f23aa5",
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+ "description": "transducin (beta)-like 3",
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+ "mean": "8.703",
+ "additive": "-0.132",
+ "lod_score": "10.0",
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+ "sample_p": "8.332e-02",
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+ "tissue_corr": "0.312",
+ "tissue_pvalue": "1.211e-01"
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+ {
+ "index": 22,
+ "trait_id": "1415680_at",
+ "dataset": "HC_M2_0606_P",
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+ "description": "anaphase promoting complex subunit 1; last 3 exons and 3' UTR",
+ "location": "Chr2: 128.438499",
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+ "additive": "-0.102",
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+ "sample_p": "8.734e-02",
+ "lit_corr": "--",
+ "tissue_corr": "0.367",
+ "tissue_pvalue": "6.539e-02"
+ },
+ {
+ "index": 23,
+ "trait_id": "1415712_at",
+ "dataset": "HC_M2_0606_P",
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+ "description": "zinc finger, RAN-binding domain containing 1 (ubiquitin thioesterase, TRAF-binding protein); far 3' UTR (M430AB control duplicate)",
+ "location": "Chr7: 140.175988",
+ "mean": "9.923",
+ "additive": "-0.079",
+ "lod_score": "2.8",
+ "lrs_location": "Chr5: 143.642242",
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+ "sample_p": "9.125e-02",
+ "lit_corr": "--",
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+ "tissue_pvalue": "7.413e-01"
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+ "index": 24,
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+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415674_a_at:HC_M2_0606_P:c8e7fb1fcad21d73fcfd",
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+ "lrs_location": "Chr5: 69.527298",
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+ "lit_corr": "--",
+ "tissue_corr": "-0.334",
+ "tissue_pvalue": "9.587e-02"
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+ "index": 25,
+ "trait_id": "1415747_s_at",
+ "dataset": "HC_M2_0606_P",
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+ "description": "RIO kinase 3 (yeast); mid to distal 3' UTR",
+ "location": "Chr18: 12.314783",
+ "mean": "10.906",
+ "additive": "0.068",
+ "lod_score": "2.1",
+ "lrs_location": "Chr4: 13.764991",
+ "sample_r": "-0.198",
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+ "sample_p": "1.081e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.282",
+ "tissue_pvalue": "1.628e-01"
+ },
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+ "index": 26,
+ "trait_id": "1415682_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415682_at:HC_M2_0606_P:d02febdf17a279a71088",
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+ "description": "exportin 7",
+ "location": "Chr14: 71.064730",
+ "mean": "9.075",
+ "additive": "-0.073",
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+ "lrs_location": "Chr17: 68.421021",
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+ "lit_corr": "--",
+ "tissue_corr": "-0.322",
+ "tissue_pvalue": "1.084e-01"
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+ "index": 27,
+ "trait_id": "1415732_at",
+ "dataset": "HC_M2_0606_P",
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+ "description": "abhydrolase domain containing 16A; last five exons including proximal 3' UTR",
+ "location": "Chr17: 35.238940",
+ "mean": "10.798",
+ "additive": "-0.132",
+ "lod_score": "6.1",
+ "lrs_location": "Chr17: 37.015392",
+ "sample_r": "0.177",
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+ "sample_p": "1.527e-01",
+ "lit_corr": "--",
+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
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+ "index": 28,
+ "trait_id": "1415688_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415688_at:HC_M2_0606_P:4c3b6c7cd3d447f2346c",
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+ "description": "ubiquitin-conjugating enzyme E2 G1; mid to distal 3' UTR",
+ "location": "Chr11: 72.497627",
+ "mean": "11.494",
+ "additive": "-0.116",
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+ "lrs_location": "Chr11: 72.486317",
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+ "sample_p": "1.605e-01",
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+ "tissue_corr": "0.365",
+ "tissue_pvalue": "6.671e-02"
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+ "trait_id": "1415698_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415698_at:HC_M2_0606_P:4d8988a8fac8bdbce9c2",
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+ "description": "Golgi membrane protein 1; distal 3' UTR",
+ "location": "Chr13: 59.736417",
+ "mean": "11.367",
+ "additive": "0.113",
+ "lod_score": "2.9",
+ "lrs_location": "Chr7: 36.124856",
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+ "sample_p": "2.221e-01",
+ "lit_corr": "--",
+ "tissue_corr": "-0.053",
+ "tissue_pvalue": "7.958e-01"
+ },
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+ "index": 30,
+ "trait_id": "1415697_at",
+ "dataset": "HC_M2_0606_P",
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+ "symbol": "G3bp2",
+ "description": "GTPase activating protein (SH3 domain) binding protein 2; mid proximal 3' UTR",
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+ "mean": "10.768",
+ "additive": "0.137",
+ "lod_score": "3.6",
+ "lrs_location": "Chr5: 138.337847",
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+ "sample_p": "2.504e-01",
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+ "tissue_corr": "0.107",
+ "tissue_pvalue": "6.032e-01"
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+ "index": 31,
+ "trait_id": "1415676_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415676_a_at:HC_M2_0606_P:b236ce0b2af4408662b6",
+ "symbol": "Psmb5",
+ "description": "proteasome (prosome, macropain) subunit, beta type 5; coding exons 2 and 3",
+ "location": "Chr14: 55.233131",
+ "mean": "14.199",
+ "additive": "0.130",
+ "lod_score": "6.9",
+ "lrs_location": "Chr14: 54.987777",
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+ "sample_p": "2.725e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.152",
+ "tissue_pvalue": "4.580e-01"
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+ "index": 32,
+ "trait_id": "1415723_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415723_at:HC_M2_0606_P:8672294efc1c30e220c2",
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+ "description": "eukaryotic translation initiation factor 5; distal 3' UTR",
+ "location": "Chr12: 112.784258",
+ "mean": "12.507",
+ "additive": "-0.196",
+ "lod_score": "12.9",
+ "lrs_location": "Chr12: 112.426348",
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+ "sample_p": "2.795e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.105",
+ "tissue_pvalue": "6.104e-01"
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+ {
+ "index": 33,
+ "trait_id": "1415692_s_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415692_s_at:HC_M2_0606_P:a30c9243d16dd6d28826",
+ "symbol": "Canx",
+ "description": "calnexin; mid 3' UTR",
+ "location": "Chr11: 50.108505",
+ "mean": "13.862",
+ "additive": "0.090",
+ "lod_score": "3.3",
+ "lrs_location": "Chr9: 15.693672",
+ "sample_r": "0.133",
+ "num_overlap": 67,
+ "sample_p": "2.828e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.298",
+ "tissue_pvalue": "1.388e-01"
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+ {
+ "index": 34,
+ "trait_id": "1415728_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415728_at:HC_M2_0606_P:449a770634eff3bac9f5",
+ "symbol": "Pabpn1",
+ "description": "polyadenylate-binding protein 2; far 3' UTR",
+ "location": "Chr14: 55.517242",
+ "mean": "10.510",
+ "additive": "0.150",
+ "lod_score": "2.3",
+ "lrs_location": "Chr19: 53.933992",
+ "sample_r": "-0.130",
+ "num_overlap": 67,
+ "sample_p": "2.942e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.132",
+ "tissue_pvalue": "5.194e-01"
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+ {
+ "index": 35,
+ "trait_id": "1415675_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415675_at:HC_M2_0606_P:9712db695d534370b0d9",
+ "symbol": "Dpm2",
+ "description": "dolichol-phosphate (beta-D) mannosyltransferase 2; last exon and proximal to mid 3' UTR",
+ "location": "Chr2: 32.428524",
+ "mean": "10.207",
+ "additive": "-0.043",
+ "lod_score": "2.6",
+ "lrs_location": "Chr13: 30.769380",
+ "sample_r": "-0.129",
+ "num_overlap": 67,
+ "sample_p": "2.966e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.102",
+ "tissue_pvalue": "6.201e-01"
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+ {
+ "index": 36,
+ "trait_id": "1415721_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415721_a_at:HC_M2_0606_P:fd804230fcc3400d6b4b",
+ "symbol": "Naa60",
+ "description": "N(alpha)-acetyltransferase 60, NatF catalytic subunit; distal 3' UTR",
+ "location": "Chr16: 3.904169",
+ "mean": "10.153",
+ "additive": "-0.059",
+ "lod_score": "3.6",
+ "lrs_location": "Chr2: 159.368724",
+ "sample_r": "0.128",
+ "num_overlap": 67,
+ "sample_p": "3.004e-01",
+ "lit_corr": "--",
+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
+ },
+ {
+ "index": 37,
+ "trait_id": "1415733_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415733_a_at:HC_M2_0606_P:4eff33f3ecd4c0dd418e",
+ "symbol": "Shb",
+ "description": "Src homology 2 domain containing adaptor protein B; putative far 3' UTR (or intercalated neighbor)",
+ "location": "Chr4: 45.118127",
+ "mean": "10.756",
+ "additive": "-0.044",
+ "lod_score": "1.9",
+ "lrs_location": "Chr5: 69.527298",
+ "sample_r": "0.126",
+ "num_overlap": 67,
+ "sample_p": "3.103e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.149",
+ "tissue_pvalue": "4.678e-01"
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+ {
+ "index": 38,
+ "trait_id": "1415720_s_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415720_s_at:HC_M2_0606_P:4e7dab211ec586e8297a",
+ "symbol": "Mad2l1bp",
+ "description": "mitotic arrest deficient 2, homolog-like 1 (MAD2L1) binding protein; last exon and 3' UTR",
+ "location": "Chr17: 46.284624",
+ "mean": "7.057",
+ "additive": "0.048",
+ "lod_score": "2.5",
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+ "tissue_corr": "0.179",
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+ "additive": "0.402",
+ "lod_score": "33.4",
+ "lrs_location": "Chr5: 90.500265",
+ "sample_r": "0.010",
+ "num_overlap": 67,
+ "sample_p": "9.360e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.420",
+ "tissue_pvalue": "3.257e-02"
+ },
+ {
+ "index": 98,
+ "trait_id": "1415702_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415702_a_at:HC_M2_0606_P:7ef725f27498e294d14a",
+ "symbol": "Ctbp1",
+ "description": "C-terminal binding protein 1; 3' UTR",
+ "location": "Chr5: 33.590456",
+ "mean": "12.530",
+ "additive": "-0.056",
+ "lod_score": "2.3",
+ "lrs_location": "Chr12: 76.993653",
+ "sample_r": "-0.010",
+ "num_overlap": 67,
+ "sample_p": "9.372e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.514",
+ "tissue_pvalue": "7.288e-03"
+ },
+ {
+ "index": 99,
+ "trait_id": "1415711_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415711_at:HC_M2_0606_P:f71bb40cdefd07ae95d6",
+ "symbol": "Arfgef1",
+ "description": "ADP-ribosylation factor guanine nucleotide-exchange factor 1 (brefeldin A-inhibited); 3' UTR",
+ "location": "Chr18: 22.122655",
+ "mean": "11.617",
+ "additive": "-0.055",
+ "lod_score": "3.3",
+ "lrs_location": "Chr2: 50.500580",
+ "sample_r": "-0.003",
+ "num_overlap": 67,
+ "sample_p": "9.802e-01",
+ "lit_corr": "--",
+ "tissue_corr": "-0.020",
+ "tissue_pvalue": "9.216e-01"
+ },
+ {
+ "index": 100,
+ "trait_id": "1415726_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415726_at:HC_M2_0606_P:89e8ab5b988a202a2fb0",
+ "symbol": "Ankrd17",
+ "description": "ankyrin repeat domain protein 17; last exon and proximal 3' UTR",
+ "location": "Chr5: 90.657781",
+ "mean": "11.533",
+ "additive": "0.046",
+ "lod_score": "2.0",
+ "lrs_location": "Chr14: 42.819085",
+ "sample_r": "0.000",
+ "num_overlap": 67,
+ "sample_p": "9.991e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.530",
+ "tissue_pvalue": "5.382e-03"
+ }
+] \ No newline at end of file
diff --git a/tests/integration/test_correlation.py b/tests/integration/test_correlation.py
new file mode 100644
index 0000000..b94487a
--- /dev/null
+++ b/tests/integration/test_correlation.py
@@ -0,0 +1,57 @@
+"""Integration tests for correlation api"""
+
+import os
+import json
+import pickle
+import unittest
+from unittest import mock
+
+from gn3.app import create_app
+
+
+def file_path(relative_path):
+ """getting abs path for file """
+ dir_name = os.path.dirname(os.path.abspath(__file__))
+ split_path = relative_path.split("/")
+ new_path = os.path.join(dir_name, *split_path)
+ return new_path
+
+
+class CorrelationAPITest(unittest.TestCase):
+ # currently disable
+ """Test cases for the Correlation API"""
+
+ def setUp(self):
+ self.app = create_app().test_client()
+
+ with open(file_path("correlation_data.json")) as json_file:
+ self.correlation_data = json.load(json_file)
+
+ with open(file_path("expected_corr_results.json")) as results_file:
+ self.correlation_results = json.load(results_file)
+
+ def tearDown(self):
+ self.correlation_data = ""
+
+ self.correlation_results = ""
+
+ @mock.patch("gn3.api.correlation.compute_correlation")
+ def test_corr_compute(self, compute_corr):
+ """Test that the correct response in correlation"""
+
+ compute_corr.return_value = self.correlation_results
+ response = self.app.post(
+ "/api/correlation/corr_compute", json=self.correlation_data, follow_redirects=True)
+
+ self.assertEqual(response.status_code, 200)
+
+ @mock.patch("gn3.api.correlation.compute_correlation")
+ def test_corr_compute_failed_request(self,compute_corr):
+ """test taht cormpute requests fails """
+
+ compute_corr.return_value = self.correlation_results
+
+ response = self.app.post(
+ "/api/correlation/corr_compute", json=None, follow_redirects=True)
+
+ self.assertEqual(response.status_code,400)
diff --git a/tests/unit/correlation/__init__.py b/tests/unit/correlation/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/tests/unit/correlation/__init__.py
diff --git a/tests/unit/correlation/correlation_test_data.json b/tests/unit/correlation/correlation_test_data.json
new file mode 100644
index 0000000..87d24e3
--- /dev/null
+++ b/tests/unit/correlation/correlation_test_data.json
@@ -0,0 +1,18 @@
+{
+ "primary_samples": "C57BL/6J,DBA/2J,B6D2F1,D2B6F1,BXD1,BXD2,BXD5,BXD6,BXD8,BXD9,BXD11,BXD12,BXD13,BXD14,BXD15,BXD16,BXD18,BXD19,BXD20,BXD21,BXD22,BXD23,BXD24,BXD24a,BXD25,BXD27,BXD28,BXD29,BXD30,BXD31,BXD32,BXD33,BXD34,BXD35,BXD36,BXD37,BXD38,BXD39,BXD40,BXD41,BXD42,BXD43,BXD44,BXD45,BXD48,BXD48a,BXD49,BXD50,BXD51,BXD52,BXD53,BXD54,BXD55,BXD56,BXD59,BXD60,BXD61,BXD62,BXD63,BXD64,BXD65,BXD65a,BXD65b,BXD66,BXD67,BXD68,BXD69,BXD70,BXD71,BXD72,BXD73,BXD73a,BXD73b,BXD74,BXD75,BXD76,BXD77,BXD78,BXD79,BXD81,BXD83,BXD84,BXD85,BXD86,BXD87,BXD88,BXD89,BXD90,BXD91,BXD93,BXD94,BXD95,BXD98,BXD99,BXD100,BXD101,BXD102,BXD104,BXD105,BXD106,BXD107,BXD108,BXD109,BXD110,BXD111,BXD112,BXD113,BXD114,BXD115,BXD116,BXD117,BXD119,BXD120,BXD121,BXD122,BXD123,BXD124,BXD125,BXD126,BXD127,BXD128,BXD128a,BXD130,BXD131,BXD132,BXD133,BXD134,BXD135,BXD136,BXD137,BXD138,BXD139,BXD141,BXD142,BXD144,BXD145,BXD146,BXD147,BXD148,BXD149,BXD150,BXD151,BXD152,BXD153,BXD154,BXD155,BXD156,BXD157,BXD160,BXD161,BXD162,BXD165,BXD168,BXD169,BXD170,BXD171,BXD172,BXD173,BXD174,BXD175,BXD176,BXD177,BXD178,BXD180,BXD181,BXD183,BXD184,BXD186,BXD187,BXD188,BXD189,BXD190,BXD191,BXD192,BXD193,BXD194,BXD195,BXD196,BXD197,BXD198,BXD199,BXD200,BXD201,BXD202,BXD203,BXD204,BXD205,BXD206,BXD207,BXD208,BXD209,BXD210,BXD211,BXD212,BXD213,BXD214,BXD215,BXD216,BXD217,BXD218,BXD219,BXD220",
+ "trait_id": "1444666_at",
+ "dataset": "HC_M2_0606_P",
+ "sample_vals": "{\"C57BL/6J\":\"6.638\",\"DBA/2J\":\"6.266\",\"B6D2F1\":\"6.494\",\"D2B6F1\":\"6.565\",\"BXD1\":\"6.357\",\"BXD2\":\"6.456\",\"BXD5\":\"6.590\",\"BXD6\":\"6.568\",\"BXD8\":\"6.581\",\"BXD9\":\"6.322\",\"BXD11\":\"6.519\",\"BXD12\":\"6.543\",\"BXD13\":\"6.636\",\"BXD14\":\"x\",\"BXD15\":\"6.578\",\"BXD16\":\"6.636\",\"BXD18\":\"x\",\"BXD19\":\"6.562\",\"BXD20\":\"6.610\",\"BXD21\":\"6.668\",\"BXD22\":\"6.607\",\"BXD23\":\"6.513\",\"BXD24\":\"6.601\",\"BXD24a\":\"x\",\"BXD25\":\"x\",\"BXD27\":\"6.573\",\"BXD28\":\"6.639\",\"BXD29\":\"6.656\",\"BXD30\":\"x\",\"BXD31\":\"6.549\",\"BXD32\":\"6.502\",\"BXD33\":\"6.584\",\"BXD34\":\"6.261\",\"BXD35\":\"x\",\"BXD36\":\"x\",\"BXD37\":\"x\",\"BXD38\":\"6.646\",\"BXD39\":\"6.584\",\"BXD40\":\"6.790\",\"BXD41\":\"x\",\"BXD42\":\"6.536\",\"BXD43\":\"6.476\",\"BXD44\":\"6.545\",\"BXD45\":\"6.742\",\"BXD48\":\"6.393\",\"BXD48a\":\"6.618\",\"BXD49\":\"x\",\"BXD50\":\"6.496\",\"BXD51\":\"6.494\",\"BXD52\":\"x\",\"BXD53\":\"x\",\"BXD54\":\"x\",\"BXD55\":\"6.263\",\"BXD56\":\"x\",\"BXD59\":\"x\",\"BXD60\":\"6.541\",\"BXD61\":\"6.662\",\"BXD62\":\"6.628\",\"BXD63\":\"6.556\",\"BXD64\":\"6.572\",\"BXD65\":\"6.530\",\"BXD65a\":\"6.280\",\"BXD65b\":\"6.490\",\"BXD66\":\"6.608\",\"BXD67\":\"6.534\",\"BXD68\":\"6.352\",\"BXD69\":\"6.548\",\"BXD70\":\"6.520\",\"BXD71\":\"x\",\"BXD72\":\"x\",\"BXD73\":\"6.484\",\"BXD73a\":\"6.486\",\"BXD73b\":\"x\",\"BXD74\":\"6.639\",\"BXD75\":\"6.401\",\"BXD76\":\"6.452\",\"BXD77\":\"6.568\",\"BXD78\":\"x\",\"BXD79\":\"6.642\",\"BXD81\":\"x\",\"BXD83\":\"6.446\",\"BXD84\":\"6.582\",\"BXD85\":\"6.484\",\"BXD86\":\"6.877\",\"BXD87\":\"6.474\",\"BXD88\":\"x\",\"BXD89\":\"6.676\",\"BXD90\":\"6.644\",\"BXD91\":\"x\",\"BXD93\":\"6.620\",\"BXD94\":\"6.528\",\"BXD95\":\"x\",\"BXD98\":\"6.486\",\"BXD99\":\"6.530\",\"BXD100\":\"x\",\"BXD101\":\"x\",\"BXD102\":\"x\",\"BXD104\":\"x\",\"BXD105\":\"x\",\"BXD106\":\"x\",\"BXD107\":\"x\",\"BXD108\":\"x\",\"BXD109\":\"x\",\"BXD110\":\"x\",\"BXD111\":\"x\",\"BXD112\":\"x\",\"BXD113\":\"x\",\"BXD114\":\"x\",\"BXD115\":\"x\",\"BXD116\":\"x\",\"BXD117\":\"x\",\"BXD119\":\"x\",\"BXD120\":\"x\",\"BXD121\":\"x\",\"BXD122\":\"x\",\"BXD123\":\"x\",\"BXD124\":\"x\",\"BXD125\":\"x\",\"BXD126\":\"x\",\"BXD127\":\"x\",\"BXD128\":\"x\",\"BXD128a\":\"x\",\"BXD130\":\"x\",\"BXD131\":\"x\",\"BXD132\":\"x\",\"BXD133\":\"x\",\"BXD134\":\"x\",\"BXD135\":\"x\",\"BXD136\":\"x\",\"BXD137\":\"x\",\"BXD138\":\"x\",\"BXD139\":\"x\",\"BXD141\":\"x\",\"BXD142\":\"x\",\"BXD144\":\"x\",\"BXD145\":\"x\",\"BXD146\":\"x\",\"BXD147\":\"x\",\"BXD148\":\"x\",\"BXD149\":\"x\",\"BXD150\":\"x\",\"BXD151\":\"x\",\"BXD152\":\"x\",\"BXD153\":\"x\",\"BXD154\":\"x\",\"BXD155\":\"x\",\"BXD156\":\"x\",\"BXD157\":\"x\",\"BXD160\":\"x\",\"BXD161\":\"x\",\"BXD162\":\"x\",\"BXD165\":\"x\",\"BXD168\":\"x\",\"BXD169\":\"x\",\"BXD170\":\"x\",\"BXD171\":\"x\",\"BXD172\":\"x\",\"BXD173\":\"x\",\"BXD174\":\"x\",\"BXD175\":\"x\",\"BXD176\":\"x\",\"BXD177\":\"x\",\"BXD178\":\"x\",\"BXD180\":\"x\",\"BXD181\":\"x\",\"BXD183\":\"x\",\"BXD184\":\"x\",\"BXD186\":\"x\",\"BXD187\":\"x\",\"BXD188\":\"x\",\"BXD189\":\"x\",\"BXD190\":\"x\",\"BXD191\":\"x\",\"BXD192\":\"x\",\"BXD193\":\"x\",\"BXD194\":\"x\",\"BXD195\":\"x\",\"BXD196\":\"x\",\"BXD197\":\"x\",\"BXD198\":\"x\",\"BXD199\":\"x\",\"BXD200\":\"x\",\"BXD201\":\"x\",\"BXD202\":\"x\",\"BXD203\":\"x\",\"BXD204\":\"x\",\"BXD205\":\"x\",\"BXD206\":\"x\",\"BXD207\":\"x\",\"BXD208\":\"x\",\"BXD209\":\"x\",\"BXD210\":\"x\",\"BXD211\":\"x\",\"BXD212\":\"x\",\"BXD213\":\"x\",\"BXD214\":\"x\",\"BXD215\":\"x\",\"BXD216\":\"x\",\"BXD217\":\"x\",\"BXD218\":\"x\",\"BXD219\":\"x\",\"BXD220\":\"x\"}",
+ "corr_type": "lit",
+ "corr_dataset": "HC_M2_0606_P",
+ "corr_return_results": "100",
+ "corr_samples_group": "samples_primary",
+ "corr_sample_method": "pearson",
+ "min_expr": "",
+ "location_type": "gene",
+ "loc_chr": "",
+ "min_loc_mb": "",
+ "max_loc_mb": "",
+ "p_range_lower": "-0.60",
+ "p_range_upper": "0.74"
+} \ No newline at end of file
diff --git a/tests/unit/correlation/dataset.json b/tests/unit/correlation/dataset.json
new file mode 100644
index 0000000..8a53ed5
--- /dev/null
+++ b/tests/unit/correlation/dataset.json
@@ -0,0 +1,64 @@
+{
+ "name":"HC_M2_0606_P",
+ "id":112,
+ "shortname":"Hippocampus M430v2 BXD 06/06 PDNN",
+ "fullname":"Hippocampus Consortium M430v2 (Jun06) PDNN",
+ "type":"ProbeSet",
+ "data_scale":"log2",
+ "search_fields":[
+ "Name",
+ "Description",
+ "Probe_Target_Description",
+ "Symbol",
+ "Alias",
+ "GenbankId",
+ "UniGeneId",
+ "RefSeq_TranscriptId"
+ ],
+ "display_fields":[
+ "name",
+ "symbol",
+ "description",
+ "probe_target_description",
+ "chr",
+ "mb",
+ "alias",
+ "geneid",
+ "genbankid",
+ "unigeneid",
+ "omim",
+ "refseq_transcriptid",
+ "blatseq",
+ "targetseq",
+ "chipid",
+ "comments",
+ "strand_probe",
+ "strand_gene",
+ "proteinid",
+ "uniprotid",
+ "probe_set_target_region",
+ "probe_set_specificity",
+ "probe_set_blat_score",
+ "probe_set_blat_mb_start",
+ "probe_set_blat_mb_end",
+ "probe_set_strand",
+ "probe_set_note_by_rw",
+ "flag"
+ ],
+ "header_fields":[
+ "Index",
+ "Record",
+ "Symbol",
+ "Description",
+ "Location",
+ "Mean",
+ "Max LRS",
+ "Max LRS Location",
+ "Additive Effect"
+ ],
+ "query_for_group":"\n SELECT\n InbredSet.Name, InbredSet.Id, InbredSet.GeneticType\n FROM\n InbredSet, ProbeSetFreeze, ProbeFreeze\n WHERE\n ProbeFreeze.InbredSetId = InbredSet.Id AND\n ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId AND\n ProbeSetFreeze.Name = \"HC_M2_0606_P\"\n ",
+ "tissue":"Hippocampus mRNA",
+ "group":"None",
+ "accession_id":"None",
+ "species":"None"
+} \ No newline at end of file
diff --git a/tests/unit/correlation/expected_correlation_results.json b/tests/unit/correlation/expected_correlation_results.json
new file mode 100644
index 0000000..b5bbc2d
--- /dev/null
+++ b/tests/unit/correlation/expected_correlation_results.json
@@ -0,0 +1,1902 @@
+[
+ {
+ "index": 1,
+ "trait_id": "1415758_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415758_at:HC_M2_0606_P:da50fa1141a7d608ab20",
+ "symbol": "Fryl",
+ "description": "furry homolog-like; far 3' UTR",
+ "location": "Chr5: 72.964984",
+ "mean": "9.193",
+ "additive": "-0.081",
+ "lod_score": "4.4",
+ "lrs_location": "Chr1: 196.404284",
+ "sample_r": "-0.407",
+ "num_overlap": 67,
+ "sample_p": "6.234e-04",
+ "lit_corr": "--",
+ "tissue_corr": "-0.221",
+ "tissue_pvalue": "2.780e-01"
+ },
+ {
+ "index": 2,
+ "trait_id": "1415693_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415693_at:HC_M2_0606_P:0959e913366f559ea22b",
+ "symbol": "Derl1",
+ "description": "derlin 1; proximal to mid 3' UTR",
+ "location": "Chr15: 57.702171",
+ "mean": "9.445",
+ "additive": "0.056",
+ "lod_score": "2.1",
+ "lrs_location": "Chr1: 193.731996",
+ "sample_r": "0.398",
+ "num_overlap": 67,
+ "sample_p": "8.614e-04",
+ "lit_corr": "--",
+ "tissue_corr": "0.114",
+ "tissue_pvalue": "5.800e-01"
+ },
+ {
+ "index": 3,
+ "trait_id": "1415753_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415753_at:HC_M2_0606_P:d75ca42e7fa1613364bb",
+ "symbol": "Fam108a",
+ "description": "abhydrolase domain-containing protein FAM108A; last two exons and proximal 3' UTR",
+ "location": "Chr10: 80.046470",
+ "mean": "12.731",
+ "additive": "0.050",
+ "lod_score": "1.5",
+ "lrs_location": "ChrX: 103.404884",
+ "sample_r": "0.384",
+ "num_overlap": 67,
+ "sample_p": "1.344e-03",
+ "lit_corr": "--",
+ "tissue_corr": "0.108",
+ "tissue_pvalue": "5.990e-01"
+ },
+ {
+ "index": 4,
+ "trait_id": "1415740_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415740_at:HC_M2_0606_P:755cdc41d0d50a03b647",
+ "symbol": "Psmc5",
+ "description": "protease (prosome, macropain) 26S subunit, ATPase 5; exons 7, 8, 9",
+ "location": "Chr11: 106.123450",
+ "mean": "12.424",
+ "additive": "0.059",
+ "lod_score": "2.6",
+ "lrs_location": "Chr9: 34.013550",
+ "sample_r": "0.364",
+ "num_overlap": 67,
+ "sample_p": "2.476e-03",
+ "lit_corr": "--",
+ "tissue_corr": "0.333",
+ "tissue_pvalue": "9.696e-02"
+ },
+ {
+ "index": 5,
+ "trait_id": "1415757_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415757_at:HC_M2_0606_P:8bbf06aa2e3aa5530934",
+ "symbol": "Gbf1",
+ "description": "Golgi-specific brefeldin A-resistance factor 1; last exon and proximal 3' UTR",
+ "location": "Chr19: 46.360410",
+ "mean": "9.800",
+ "additive": "-0.062",
+ "lod_score": "2.0",
+ "lrs_location": "Chr17: 52.750885",
+ "sample_r": "0.363",
+ "num_overlap": 67,
+ "sample_p": "2.539e-03",
+ "lit_corr": "--",
+ "tissue_corr": "-0.059",
+ "tissue_pvalue": "7.741e-01"
+ },
+ {
+ "index": 6,
+ "trait_id": "1415768_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415768_a_at:HC_M2_0606_P:5e67109eee04f5da3393",
+ "symbol": "Ube2r2",
+ "description": "ubiquitin-conjugating enzyme E2R 2",
+ "location": "Chr4: 41.137929",
+ "mean": "9.811",
+ "additive": "-0.087",
+ "lod_score": "3.3",
+ "lrs_location": "Chr12: 114.553844",
+ "sample_r": "-0.312",
+ "num_overlap": 67,
+ "sample_p": "1.019e-02",
+ "lit_corr": "--",
+ "tissue_corr": "-0.007",
+ "tissue_pvalue": "9.711e-01"
+ },
+ {
+ "index": 7,
+ "trait_id": "1415670_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415670_at:HC_M2_0606_P:4f82d7374f29ebfacaaf",
+ "symbol": "Copg",
+ "description": "coatomer protein complex, subunit gamma 1; two of the three last exons and proximal 3' UTR",
+ "location": "Chr6: 87.859681",
+ "mean": "11.199",
+ "additive": "-0.113",
+ "lod_score": "3.7",
+ "lrs_location": "Chr1: 157.588921",
+ "sample_r": "0.305",
+ "num_overlap": 67,
+ "sample_p": "1.200e-02",
+ "lit_corr": "--",
+ "tissue_corr": "-0.405",
+ "tissue_pvalue": "4.032e-02"
+ },
+ {
+ "index": 8,
+ "trait_id": "1415742_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415742_at:HC_M2_0606_P:b72a582a1f840a18c3e7",
+ "symbol": "Aup1",
+ "description": "ancient ubiquitous protein 1",
+ "location": "Chr6: 83.006784",
+ "mean": "9.529",
+ "additive": "-0.062",
+ "lod_score": "2.4",
+ "lrs_location": "Chr19: 16.955950",
+ "sample_r": "0.295",
+ "num_overlap": 67,
+ "sample_p": "1.523e-02",
+ "lit_corr": "--",
+ "tissue_corr": "-0.033",
+ "tissue_pvalue": "8.716e-01"
+ },
+ {
+ "index": 9,
+ "trait_id": "1415743_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415743_at:HC_M2_0606_P:3187245a079e824b4236",
+ "symbol": "Hdac5",
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+ "tissue_corr": "0.005",
+ "tissue_pvalue": "9.823e-01"
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+ {
+ "index": 10,
+ "trait_id": "1415690_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415690_at:HC_M2_0606_P:603b215ede00b6fe1104",
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+ "description": "39S ribosomal protein L27, mitochondrial; last three exons",
+ "location": "Chr11: 94.517922",
+ "mean": "12.569",
+ "additive": "0.063",
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+ "sample_p": "2.986e-02",
+ "lit_corr": "--",
+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
+ },
+ {
+ "index": 11,
+ "trait_id": "1415727_at",
+ "dataset": "HC_M2_0606_P",
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+ "symbol": "Apoa1bp",
+ "description": "apolipoprotein A-I binding protein; exons 3 through 6",
+ "location": "Chr3: 87.860534",
+ "mean": "11.707",
+ "additive": "-0.076",
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+ "lrs_location": "Chr3: 56.295375",
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+ "lit_corr": "--",
+ "tissue_corr": "-0.535",
+ "tissue_pvalue": "4.841e-03"
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+ {
+ "index": 12,
+ "trait_id": "1415730_at",
+ "dataset": "HC_M2_0606_P",
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+ "description": "cleavage and polyadenylation specificity factor 7; distal 3' UTR (transQTL on Chr 4 in BXD eye data)",
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+ "additive": "-0.048",
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+ "lrs_location": "Chr1: 188.085707",
+ "sample_r": "-0.263",
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+ "sample_p": "3.164e-02",
+ "lit_corr": "--",
+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
+ },
+ {
+ "index": 13,
+ "trait_id": "1415741_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415741_at:HC_M2_0606_P:033752be361d32960c29",
+ "symbol": "Tmem165",
+ "description": "transmembrane protein 165; 3' UTR",
+ "location": "Chr5: 76.637708",
+ "mean": "10.974",
+ "additive": "0.048",
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+ "lrs_location": "Chr4: 5.606394",
+ "sample_r": "-0.258",
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+ "sample_p": "3.489e-02",
+ "lit_corr": "--",
+ "tissue_corr": "0.271",
+ "tissue_pvalue": "1.812e-01"
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+ {
+ "index": 14,
+ "trait_id": "1415725_at",
+ "dataset": "HC_M2_0606_P",
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+ "symbol": "Rrn3",
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+ "location": "Chr16: 13.814359",
+ "mean": "9.195",
+ "additive": "-0.085",
+ "lod_score": "2.8",
+ "lrs_location": "Chr1: 148.717644",
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+ "num_overlap": 67,
+ "sample_p": "3.636e-02",
+ "lit_corr": "--",
+ "tissue_corr": "0.587",
+ "tissue_pvalue": "1.621e-03"
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+ {
+ "index": 15,
+ "trait_id": "1415717_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415717_at:HC_M2_0606_P:dd51438830e4033114f8",
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+ "description": "ring finger protein 220; mid 3' UTR",
+ "location": "Chr4: 116.944155",
+ "mean": "10.778",
+ "additive": "-0.084",
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+ "lrs_location": "Chr4: 122.536808",
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+ "sample_p": "4.816e-02",
+ "lit_corr": "--",
+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
+ },
+ {
+ "index": 16,
+ "trait_id": "1415703_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415703_at:HC_M2_0606_P:51ee8e47654845a546f0",
+ "symbol": "Huwe1",
+ "description": "HECT, UBA and WWE domain containing 1; last 3 exons and proximal 3' UTR",
+ "location": "ChrX: 148.367136",
+ "mean": "11.335",
+ "additive": "-0.094",
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+ "lrs_location": "Chr1: 135.891043",
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+ "sample_p": "5.541e-02",
+ "lit_corr": "--",
+ "tissue_corr": "0.528",
+ "tissue_pvalue": "5.576e-03"
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+ {
+ "index": 17,
+ "trait_id": "1415748_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415748_a_at:HC_M2_0606_P:749a2279081b54e89885",
+ "symbol": "Dctn5",
+ "description": "dynactin 5; last exon and proximal half of 3' UTR",
+ "location": "Chr7: 129.291923",
+ "mean": "11.250",
+ "additive": "0.071",
+ "lod_score": "3.4",
+ "lrs_location": "Chr5: 138.337847",
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+ "sample_p": "6.133e-02",
+ "lit_corr": "--",
+ "tissue_corr": "0.064",
+ "tissue_pvalue": "7.557e-01"
+ },
+ {
+ "index": 18,
+ "trait_id": "1415706_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415706_at:HC_M2_0606_P:ddfffdb78d0ff84d6a1a",
+ "symbol": "Copa",
+ "description": "coatomer protein complex, subunit alpha; 3' UTR",
+ "location": "Chr1: 174.051912",
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+ "additive": "-0.143",
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+ "sample_p": "6.829e-02",
+ "lit_corr": "--",
+ "tissue_corr": "-0.147",
+ "tissue_pvalue": "4.739e-01"
+ },
+ {
+ "index": 19,
+ "trait_id": "1415696_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415696_at:HC_M2_0606_P:da00b2667d7c27dc76a2",
+ "symbol": "Sar1a",
+ "description": "SAR1 gene homolog A; distal 3' UTR",
+ "location": "Chr10: 61.155492",
+ "mean": "11.447",
+ "additive": "-0.051",
+ "lod_score": "2.4",
+ "lrs_location": "Chr15: 87.788313",
+ "sample_r": "0.220",
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+ "sample_p": "7.356e-02",
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+ "tissue_corr": "-0.559",
+ "tissue_pvalue": "3.015e-03"
+ },
+ {
+ "index": 20,
+ "trait_id": "1415731_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415731_at:HC_M2_0606_P:9e91e97ca1001091a5f3",
+ "symbol": "Angel2",
+ "description": "angel homolog 2; distal 3' UTR",
+ "location": "Chr1: 192.769800",
+ "mean": "9.490",
+ "additive": "0.062",
+ "lod_score": "2.6",
+ "lrs_location": "Chr14: 124.508018",
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+ "sample_p": "7.623e-02",
+ "lit_corr": "--",
+ "tissue_corr": "0.232",
+ "tissue_pvalue": "2.544e-01"
+ },
+ {
+ "index": 21,
+ "trait_id": "1415750_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415750_at:HC_M2_0606_P:c9f757736d57e5f23aa5",
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+ "description": "transducin (beta)-like 3",
+ "location": "Chr17: 24.838067",
+ "mean": "8.703",
+ "additive": "-0.132",
+ "lod_score": "10.0",
+ "lrs_location": "Chr17: 23.322636",
+ "sample_r": "0.213",
+ "num_overlap": 67,
+ "sample_p": "8.332e-02",
+ "lit_corr": "--",
+ "tissue_corr": "0.312",
+ "tissue_pvalue": "1.211e-01"
+ },
+ {
+ "index": 22,
+ "trait_id": "1415680_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415680_at:HC_M2_0606_P:22e90a54261cb373975e",
+ "symbol": "Anapc1",
+ "description": "anaphase promoting complex subunit 1; last 3 exons and 3' UTR",
+ "location": "Chr2: 128.438499",
+ "mean": "9.180",
+ "additive": "-0.102",
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+ "lrs_location": "Chr2: 125.304784",
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+ "sample_p": "8.734e-02",
+ "lit_corr": "--",
+ "tissue_corr": "0.367",
+ "tissue_pvalue": "6.539e-02"
+ },
+ {
+ "index": 23,
+ "trait_id": "1415712_at",
+ "dataset": "HC_M2_0606_P",
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+ "description": "zinc finger, RAN-binding domain containing 1 (ubiquitin thioesterase, TRAF-binding protein); far 3' UTR (M430AB control duplicate)",
+ "location": "Chr7: 140.175988",
+ "mean": "9.923",
+ "additive": "-0.079",
+ "lod_score": "2.8",
+ "lrs_location": "Chr5: 143.642242",
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+ "sample_p": "9.125e-02",
+ "lit_corr": "--",
+ "tissue_corr": "-0.068",
+ "tissue_pvalue": "7.413e-01"
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+ "index": 24,
+ "trait_id": "1415674_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415674_a_at:HC_M2_0606_P:c8e7fb1fcad21d73fcfd",
+ "symbol": "Trappc4",
+ "description": "trafficking protein particle complex 4; exons 3 and 4",
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+ "additive": "-0.065",
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+ "lrs_location": "Chr5: 69.527298",
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+ "sample_p": "1.028e-01",
+ "lit_corr": "--",
+ "tissue_corr": "-0.334",
+ "tissue_pvalue": "9.587e-02"
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+ "index": 25,
+ "trait_id": "1415747_s_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415747_s_at:HC_M2_0606_P:5d86584be55f6cec47ab",
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+ "description": "RIO kinase 3 (yeast); mid to distal 3' UTR",
+ "location": "Chr18: 12.314783",
+ "mean": "10.906",
+ "additive": "0.068",
+ "lod_score": "2.1",
+ "lrs_location": "Chr4: 13.764991",
+ "sample_r": "-0.198",
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+ "sample_p": "1.081e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.282",
+ "tissue_pvalue": "1.628e-01"
+ },
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+ "index": 26,
+ "trait_id": "1415682_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415682_at:HC_M2_0606_P:d02febdf17a279a71088",
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+ "description": "exportin 7",
+ "location": "Chr14: 71.064730",
+ "mean": "9.075",
+ "additive": "-0.073",
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+ "lrs_location": "Chr17: 68.421021",
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+ "sample_p": "1.092e-01",
+ "lit_corr": "--",
+ "tissue_corr": "-0.322",
+ "tissue_pvalue": "1.084e-01"
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+ "index": 27,
+ "trait_id": "1415732_at",
+ "dataset": "HC_M2_0606_P",
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+ "description": "abhydrolase domain containing 16A; last five exons including proximal 3' UTR",
+ "location": "Chr17: 35.238940",
+ "mean": "10.798",
+ "additive": "-0.132",
+ "lod_score": "6.1",
+ "lrs_location": "Chr17: 37.015392",
+ "sample_r": "0.177",
+ "num_overlap": 67,
+ "sample_p": "1.527e-01",
+ "lit_corr": "--",
+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
+ },
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+ "index": 28,
+ "trait_id": "1415688_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415688_at:HC_M2_0606_P:4c3b6c7cd3d447f2346c",
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+ "description": "ubiquitin-conjugating enzyme E2 G1; mid to distal 3' UTR",
+ "location": "Chr11: 72.497627",
+ "mean": "11.494",
+ "additive": "-0.116",
+ "lod_score": "7.1",
+ "lrs_location": "Chr11: 72.486317",
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+ "sample_p": "1.605e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.365",
+ "tissue_pvalue": "6.671e-02"
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+ "index": 29,
+ "trait_id": "1415698_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415698_at:HC_M2_0606_P:4d8988a8fac8bdbce9c2",
+ "symbol": "Golm1",
+ "description": "Golgi membrane protein 1; distal 3' UTR",
+ "location": "Chr13: 59.736417",
+ "mean": "11.367",
+ "additive": "0.113",
+ "lod_score": "2.9",
+ "lrs_location": "Chr7: 36.124856",
+ "sample_r": "0.151",
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+ "sample_p": "2.221e-01",
+ "lit_corr": "--",
+ "tissue_corr": "-0.053",
+ "tissue_pvalue": "7.958e-01"
+ },
+ {
+ "index": 30,
+ "trait_id": "1415697_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415697_at:HC_M2_0606_P:ab18358c61fbc03fdf13",
+ "symbol": "G3bp2",
+ "description": "GTPase activating protein (SH3 domain) binding protein 2; mid proximal 3' UTR",
+ "location": "Chr5: 92.482845",
+ "mean": "10.768",
+ "additive": "0.137",
+ "lod_score": "3.6",
+ "lrs_location": "Chr5: 138.337847",
+ "sample_r": "0.142",
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+ "sample_p": "2.504e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.107",
+ "tissue_pvalue": "6.032e-01"
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+ "index": 31,
+ "trait_id": "1415676_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415676_a_at:HC_M2_0606_P:b236ce0b2af4408662b6",
+ "symbol": "Psmb5",
+ "description": "proteasome (prosome, macropain) subunit, beta type 5; coding exons 2 and 3",
+ "location": "Chr14: 55.233131",
+ "mean": "14.199",
+ "additive": "0.130",
+ "lod_score": "6.9",
+ "lrs_location": "Chr14: 54.987777",
+ "sample_r": "-0.136",
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+ "sample_p": "2.725e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.152",
+ "tissue_pvalue": "4.580e-01"
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+ {
+ "index": 32,
+ "trait_id": "1415723_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415723_at:HC_M2_0606_P:8672294efc1c30e220c2",
+ "symbol": "Eif5",
+ "description": "eukaryotic translation initiation factor 5; distal 3' UTR",
+ "location": "Chr12: 112.784258",
+ "mean": "12.507",
+ "additive": "-0.196",
+ "lod_score": "12.9",
+ "lrs_location": "Chr12: 112.426348",
+ "sample_r": "-0.134",
+ "num_overlap": 67,
+ "sample_p": "2.795e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.105",
+ "tissue_pvalue": "6.104e-01"
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+ {
+ "index": 33,
+ "trait_id": "1415692_s_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415692_s_at:HC_M2_0606_P:a30c9243d16dd6d28826",
+ "symbol": "Canx",
+ "description": "calnexin; mid 3' UTR",
+ "location": "Chr11: 50.108505",
+ "mean": "13.862",
+ "additive": "0.090",
+ "lod_score": "3.3",
+ "lrs_location": "Chr9: 15.693672",
+ "sample_r": "0.133",
+ "num_overlap": 67,
+ "sample_p": "2.828e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.298",
+ "tissue_pvalue": "1.388e-01"
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+ {
+ "index": 34,
+ "trait_id": "1415728_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415728_at:HC_M2_0606_P:449a770634eff3bac9f5",
+ "symbol": "Pabpn1",
+ "description": "polyadenylate-binding protein 2; far 3' UTR",
+ "location": "Chr14: 55.517242",
+ "mean": "10.510",
+ "additive": "0.150",
+ "lod_score": "2.3",
+ "lrs_location": "Chr19: 53.933992",
+ "sample_r": "-0.130",
+ "num_overlap": 67,
+ "sample_p": "2.942e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.132",
+ "tissue_pvalue": "5.194e-01"
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+ {
+ "index": 35,
+ "trait_id": "1415675_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415675_at:HC_M2_0606_P:9712db695d534370b0d9",
+ "symbol": "Dpm2",
+ "description": "dolichol-phosphate (beta-D) mannosyltransferase 2; last exon and proximal to mid 3' UTR",
+ "location": "Chr2: 32.428524",
+ "mean": "10.207",
+ "additive": "-0.043",
+ "lod_score": "2.6",
+ "lrs_location": "Chr13: 30.769380",
+ "sample_r": "-0.129",
+ "num_overlap": 67,
+ "sample_p": "2.966e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.102",
+ "tissue_pvalue": "6.201e-01"
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+ {
+ "index": 36,
+ "trait_id": "1415721_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415721_a_at:HC_M2_0606_P:fd804230fcc3400d6b4b",
+ "symbol": "Naa60",
+ "description": "N(alpha)-acetyltransferase 60, NatF catalytic subunit; distal 3' UTR",
+ "location": "Chr16: 3.904169",
+ "mean": "10.153",
+ "additive": "-0.059",
+ "lod_score": "3.6",
+ "lrs_location": "Chr2: 159.368724",
+ "sample_r": "0.128",
+ "num_overlap": 67,
+ "sample_p": "3.004e-01",
+ "lit_corr": "--",
+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
+ },
+ {
+ "index": 37,
+ "trait_id": "1415733_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415733_a_at:HC_M2_0606_P:4eff33f3ecd4c0dd418e",
+ "symbol": "Shb",
+ "description": "Src homology 2 domain containing adaptor protein B; putative far 3' UTR (or intercalated neighbor)",
+ "location": "Chr4: 45.118127",
+ "mean": "10.756",
+ "additive": "-0.044",
+ "lod_score": "1.9",
+ "lrs_location": "Chr5: 69.527298",
+ "sample_r": "0.126",
+ "num_overlap": 67,
+ "sample_p": "3.103e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.149",
+ "tissue_pvalue": "4.678e-01"
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+ {
+ "index": 38,
+ "trait_id": "1415720_s_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415720_s_at:HC_M2_0606_P:4e7dab211ec586e8297a",
+ "symbol": "Mad2l1bp",
+ "description": "mitotic arrest deficient 2, homolog-like 1 (MAD2L1) binding protein; last exon and 3' UTR",
+ "location": "Chr17: 46.284624",
+ "mean": "7.057",
+ "additive": "0.048",
+ "lod_score": "2.5",
+ "lrs_location": "Chr8: 33.934048",
+ "sample_r": "0.122",
+ "num_overlap": 67,
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+ "tissue_corr": "0.179",
+ "tissue_pvalue": "3.809e-01"
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+ "additive": "-0.075",
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+ "sample_p": "6.841e-01",
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+ "tissue_corr": "-0.099",
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+ "sample_p": "9.360e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.420",
+ "tissue_pvalue": "3.257e-02"
+ },
+ {
+ "index": 98,
+ "trait_id": "1415702_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415702_a_at:HC_M2_0606_P:7ef725f27498e294d14a",
+ "symbol": "Ctbp1",
+ "description": "C-terminal binding protein 1; 3' UTR",
+ "location": "Chr5: 33.590456",
+ "mean": "12.530",
+ "additive": "-0.056",
+ "lod_score": "2.3",
+ "lrs_location": "Chr12: 76.993653",
+ "sample_r": "-0.010",
+ "num_overlap": 67,
+ "sample_p": "9.372e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.514",
+ "tissue_pvalue": "7.288e-03"
+ },
+ {
+ "index": 99,
+ "trait_id": "1415711_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415711_at:HC_M2_0606_P:f71bb40cdefd07ae95d6",
+ "symbol": "Arfgef1",
+ "description": "ADP-ribosylation factor guanine nucleotide-exchange factor 1 (brefeldin A-inhibited); 3' UTR",
+ "location": "Chr18: 22.122655",
+ "mean": "11.617",
+ "additive": "-0.055",
+ "lod_score": "3.3",
+ "lrs_location": "Chr2: 50.500580",
+ "sample_r": "-0.003",
+ "num_overlap": 67,
+ "sample_p": "9.802e-01",
+ "lit_corr": "--",
+ "tissue_corr": "-0.020",
+ "tissue_pvalue": "9.216e-01"
+ },
+ {
+ "index": 100,
+ "trait_id": "1415726_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1415726_at:HC_M2_0606_P:89e8ab5b988a202a2fb0",
+ "symbol": "Ankrd17",
+ "description": "ankyrin repeat domain protein 17; last exon and proximal 3' UTR",
+ "location": "Chr5: 90.657781",
+ "mean": "11.533",
+ "additive": "0.046",
+ "lod_score": "2.0",
+ "lrs_location": "Chr14: 42.819085",
+ "sample_r": "0.000",
+ "num_overlap": 67,
+ "sample_p": "9.991e-01",
+ "lit_corr": "--",
+ "tissue_corr": "0.530",
+ "tissue_pvalue": "5.382e-03"
+ }
+] \ No newline at end of file
diff --git a/tests/unit/correlation/group_data_test.json b/tests/unit/correlation/group_data_test.json
new file mode 100644
index 0000000..9a73a46
--- /dev/null
+++ b/tests/unit/correlation/group_data_test.json
@@ -0,0 +1,214 @@
+{
+ "name":"BXD",
+ "id":1,
+ "genetic_type":"riset",
+ "f1list":"None",
+ "parlist":"None",
+ "mapping_id":"1",
+ "mapping_names":[
+ "GEMMA",
+ "QTLReaper",
+ "R/qtl"
+ ],
+ "species":"mouse",
+ "samplelist":[
+ "BXD1",
+ "BXD2",
+ "BXD5",
+ "BXD6",
+ "BXD8",
+ "BXD9",
+ "BXD11",
+ "BXD12",
+ "BXD13",
+ "BXD14",
+ "BXD15",
+ "BXD16",
+ "BXD18",
+ "BXD19",
+ "BXD20",
+ "BXD21",
+ "BXD22",
+ "BXD23",
+ "BXD24",
+ "BXD24a",
+ "BXD25",
+ "BXD27",
+ "BXD28",
+ "BXD29",
+ "BXD30",
+ "BXD31",
+ "BXD32",
+ "BXD33",
+ "BXD34",
+ "BXD35",
+ "BXD36",
+ "BXD37",
+ "BXD38",
+ "BXD39",
+ "BXD40",
+ "BXD41",
+ "BXD42",
+ "BXD43",
+ "BXD44",
+ "BXD45",
+ "BXD48",
+ "BXD48a",
+ "BXD49",
+ "BXD50",
+ "BXD51",
+ "BXD52",
+ "BXD53",
+ "BXD54",
+ "BXD55",
+ "BXD56",
+ "BXD59",
+ "BXD60",
+ "BXD61",
+ "BXD62",
+ "BXD63",
+ "BXD64",
+ "BXD65",
+ "BXD65a",
+ "BXD65b",
+ "BXD66",
+ "BXD67",
+ "BXD68",
+ "BXD69",
+ "BXD70",
+ "BXD71",
+ "BXD72",
+ "BXD73",
+ "BXD73a",
+ "BXD73b",
+ "BXD74",
+ "BXD75",
+ "BXD76",
+ "BXD77",
+ "BXD78",
+ "BXD79",
+ "BXD81",
+ "BXD83",
+ "BXD84",
+ "BXD85",
+ "BXD86",
+ "BXD87",
+ "BXD88",
+ "BXD89",
+ "BXD90",
+ "BXD91",
+ "BXD93",
+ "BXD94",
+ "BXD95",
+ "BXD98",
+ "BXD99",
+ "BXD100",
+ "BXD101",
+ "BXD102",
+ "BXD104",
+ "BXD105",
+ "BXD106",
+ "BXD107",
+ "BXD108",
+ "BXD109",
+ "BXD110",
+ "BXD111",
+ "BXD112",
+ "BXD113",
+ "BXD114",
+ "BXD115",
+ "BXD116",
+ "BXD117",
+ "BXD119",
+ "BXD120",
+ "BXD121",
+ "BXD122",
+ "BXD123",
+ "BXD124",
+ "BXD125",
+ "BXD126",
+ "BXD127",
+ "BXD128",
+ "BXD128a",
+ "BXD130",
+ "BXD131",
+ "BXD132",
+ "BXD133",
+ "BXD134",
+ "BXD135",
+ "BXD136",
+ "BXD137",
+ "BXD138",
+ "BXD139",
+ "BXD141",
+ "BXD142",
+ "BXD144",
+ "BXD145",
+ "BXD146",
+ "BXD147",
+ "BXD148",
+ "BXD149",
+ "BXD150",
+ "BXD151",
+ "BXD152",
+ "BXD153",
+ "BXD154",
+ "BXD155",
+ "BXD156",
+ "BXD157",
+ "BXD160",
+ "BXD161",
+ "BXD162",
+ "BXD165",
+ "BXD168",
+ "BXD169",
+ "BXD170",
+ "BXD171",
+ "BXD172",
+ "BXD173",
+ "BXD174",
+ "BXD175",
+ "BXD176",
+ "BXD177",
+ "BXD178",
+ "BXD180",
+ "BXD181",
+ "BXD183",
+ "BXD184",
+ "BXD186",
+ "BXD187",
+ "BXD188",
+ "BXD189",
+ "BXD190",
+ "BXD191",
+ "BXD192",
+ "BXD193",
+ "BXD194",
+ "BXD195",
+ "BXD196",
+ "BXD197",
+ "BXD198",
+ "BXD199",
+ "BXD200",
+ "BXD201",
+ "BXD202",
+ "BXD203",
+ "BXD204",
+ "BXD205",
+ "BXD206",
+ "BXD207",
+ "BXD208",
+ "BXD209",
+ "BXD210",
+ "BXD211",
+ "BXD212",
+ "BXD213",
+ "BXD214",
+ "BXD215",
+ "BXD216",
+ "BXD217",
+ "BXD218",
+ "BXD219",
+ "BXD220"
+ ]
+} \ No newline at end of file
diff --git a/tests/unit/correlation/my_results.json b/tests/unit/correlation/my_results.json
new file mode 100644
index 0000000..2061c6e
--- /dev/null
+++ b/tests/unit/correlation/my_results.json
@@ -0,0 +1,388 @@
+[
+
+ {
+ "sample_r_correlation_using_genenetwork3":"Results",
+
+ },
+
+ {
+ "index": 1,
+ "trait_id": "1445813_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1445813_at:HC_M2_0606_P:ca1b85915ccba7198af3",
+ "symbol": "0610012K18Rik",
+ "description": "RIKEN cDNA 0610012H03 (no human homolog defined)",
+ "location": "Chr17: 14.966404",
+ "mean": "6.643",
+ "additive": "0.042",
+ "lod_score": "2.6",
+ "lrs_location": "Chr5: 133.538653",
+ "sample_r": "-0.694",
+ "num_overlap": 67,
+ "sample_p": "7.244e-11",
+ "lit_corr": "--",
+ "tissue_corr": "--",
+ "tissue_pvalue": "--"
+ },
+ {
+ "index": 2,
+ "trait_id": "1439910_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1439910_a_at:HC_M2_0606_P:0c4c7a3cb088699af36c",
+ "symbol": "Tradd",
+ "description": "TNFRSF1A-associated via death domain",
+ "location": "Chr8: 107.783836",
+ "mean": "7.449",
+ "additive": "0.039",
+ "lod_score": "1.7",
+ "lrs_location": "Chr1: 195.987783",
+ "sample_r": "-0.692",
+ "num_overlap": 67,
+ "sample_p": "9.012e-11",
+ "lit_corr": "--",
+ "tissue_corr": "-0.285",
+ "tissue_pvalue": "1.575e-01"
+ },
+ {
+ "index": 3,
+ "trait_id": "1421499_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1421499_a_at:HC_M2_0606_P:b807253e0829c2592ceb",
+ "symbol": "Ptpn14",
+ "description": "protein tyrosine phosphatase, non-receptor type 14",
+ "location": "Chr1: 191.689356",
+ "mean": "6.655",
+ "additive": "0.049",
+ "lod_score": "2.4",
+ "lrs_location": "Chr1: 197.014645",
+ "sample_r": "-0.691",
+ "num_overlap": 67,
+ "sample_p": "1.009e-10",
+ "lit_corr": "--",
+ "tissue_corr": "-0.242",
+ "tissue_pvalue": "2.337e-01"
+ },
+ {
+ "index": 4,
+ "trait_id": "1421167_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1421167_at:HC_M2_0606_P:d7b29d02c306e1ae105a",
+ "symbol": "Atp11a",
+ "description": "ATPase, class VI, type 11A; last four exons and 3' UTR",
+ "location": "Chr8: 12.856932",
+ "mean": "7.341",
+ "additive": "0.050",
+ "lod_score": "1.7",
+ "lrs_location": "Chr9: 62.226499",
+ "sample_r": "-0.690",
+ "num_overlap": 67,
+ "sample_p": "1.059e-10",
+ "lit_corr": "--",
+ "tissue_corr": "0.154",
+ "tissue_pvalue": "4.522e-01"
+ },
+ {
+ "index": 5,
+ "trait_id": "1436525_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1436525_at:HC_M2_0606_P:b36b6c4053c40cc5b25b",
+ "symbol": "Ap3s2",
+ "description": "adaptor-related protein complex 3, sigma 2 subunit",
+ "location": "Chr7: 87.022674",
+ "mean": "8.227",
+ "additive": "-0.070",
+ "lod_score": "3.0",
+ "lrs_location": "Chr4: 63.346622",
+ "sample_r": "-0.679",
+ "num_overlap": 67,
+ "sample_p": "2.627e-10",
+ "lit_corr": "--",
+ "tissue_corr": "-0.059",
+ "tissue_pvalue": "7.750e-01"
+ },
+ {
+ "index": 6,
+ "trait_id": "1450824_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1450824_at:HC_M2_0606_P:fa9b15d1d4a2d629decb",
+ "symbol": "Ptch1",
+ "description": "patched homolog 1",
+ "location": "Chr13: 63.612887",
+ "mean": "7.105",
+ "additive": "0.070",
+ "lod_score": "3.9",
+ "lrs_location": "Chr5: 133.538653",
+ "sample_r": "-0.679",
+ "num_overlap": 67,
+ "sample_p": "2.771e-10",
+ "lit_corr": "--",
+ "tissue_corr": "-0.360",
+ "tissue_pvalue": "7.075e-02"
+ },
+ {
+ "index": 7,
+ "trait_id": "1450540_x_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1450540_x_at:HC_M2_0606_P:0836630187705e0a98ed",
+ "symbol": "Krtap5-1",
+ "description": "keratin associated protein 5-1",
+ "location": "Chr7: 149.482282",
+ "mean": "7.584",
+ "additive": "0.059",
+ "lod_score": "2.2",
+ "lrs_location": "Chr5: 140.893042",
+ "sample_r": "-0.678",
+ "num_overlap": 67,
+ "sample_p": "2.828e-10",
+ "lit_corr": "--",
+ "tissue_corr": "-0.486",
+ "tissue_pvalue": "1.174e-02"
+ },
+ {
+ "index": 8,
+ "trait_id": "1454403_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1454403_at:HC_M2_0606_P:d6625a44bb78d0c9a7bc",
+ "symbol": "Fgd5",
+ "description": "FYVE, RhoGEF and PH domain containing 5",
+ "location": "Chr6: 91.964079",
+ "mean": "6.447",
+ "additive": "-0.036",
+ "lod_score": "1.8",
+ "lrs_location": "Chr8: 7.701081",
+ "sample_r": "-0.669",
+ "num_overlap": 67,
+ "sample_p": "5.895e-10",
+ "lit_corr": "--",
+ "tissue_corr": "-0.209",
+ "tissue_pvalue": "3.062e-01"
+ },
+ {
+ "index": 9,
+ "trait_id": "1444162_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1444162_at:HC_M2_0606_P:fea27946a4ed1ee2b47a",
+ "symbol": "Frs2",
+ "description": "fibroblast growth factor receptor substrate 2",
+ "location": "Chr10: 116.521472",
+ "mean": "5.677",
+ "additive": "-0.040",
+ "lod_score": "1.8",
+ "lrs_location": "Chr4: 66.843058",
+ "sample_r": "-0.666",
+ "num_overlap": 67,
+ "sample_p": "7.946e-10",
+ "lit_corr": "--",
+ "tissue_corr": "-0.241",
+ "tissue_pvalue": "2.352e-01"
+ },
+ {
+ "index": 10,
+ "trait_id": "1451876_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1451876_a_at:HC_M2_0606_P:eb879785591c3c2addeb",
+ "symbol": "Trp63",
+ "description": "transformation related protein 63",
+ "location": "Chr16: 25.884897",
+ "mean": "6.207",
+ "additive": "0.059",
+ "lod_score": "2.0",
+ "lrs_location": "Chr9: 74.382952",
+ "sample_r": "-0.664",
+ "num_overlap": 67,
+ "sample_p": "8.743e-10",
+ "lit_corr": "--",
+ "tissue_corr": "-0.187",
+ "tissue_pvalue": "3.601e-01"
+ },
+ {
+ "index": 11,
+ "trait_id": "1457795_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1457795_at:HC_M2_0606_P:617d62e702f1f04b065d",
+ "symbol": "Scamp4",
+ "description": "secretory carrier membrane protein 4",
+ "location": "Chr10: 80.076487",
+ "mean": "7.060",
+ "additive": "-0.042",
+ "lod_score": "2.4",
+ "lrs_location": "Chr11: 58.923978",
+ "sample_r": "-0.663",
+ "num_overlap": 67,
+ "sample_p": "9.806e-10",
+ "lit_corr": "--",
+ "tissue_corr": "-0.040",
+ "tissue_pvalue": "8.462e-01"
+ },
+ {
+ "index": 12,
+ "trait_id": "1439472_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1439472_at:HC_M2_0606_P:f52f356bf5d00add1ba9",
+ "symbol": "Gcn1l1",
+ "description": "general control of amino-acid synthesis 1-like 1",
+ "location": "Chr5: 116.033483",
+ "mean": "7.325",
+ "additive": "0.058",
+ "lod_score": "3.1",
+ "lrs_location": "Chr1: 196.404284",
+ "sample_r": "-0.662",
+ "num_overlap": 67,
+ "sample_p": "1.075e-09",
+ "lit_corr": "--",
+ "tissue_corr": "-0.205",
+ "tissue_pvalue": "3.157e-01"
+ },
+ {
+ "index": 13,
+ "trait_id": "1422074_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1422074_at:HC_M2_0606_P:4acd73cfd3d194327d79",
+ "symbol": "Cdx2",
+ "description": "caudal type homeo box 2",
+ "location": "Chr5: 148.113293",
+ "mean": "6.415",
+ "additive": "-0.037",
+ "lod_score": "1.8",
+ "lrs_location": "Chr2: 180.825581",
+ "sample_r": "-0.661",
+ "num_overlap": 67,
+ "sample_p": "1.140e-09",
+ "lit_corr": "--",
+ "tissue_corr": "0.002",
+ "tissue_pvalue": "9.926e-01"
+ },
+ {
+ "index": 14,
+ "trait_id": "1429140_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1429140_at:HC_M2_0606_P:16116d150fd7a8d09687",
+ "symbol": "Spns3",
+ "description": "spinster homolog 3; exons 10, 12, and proximal 3' UTR",
+ "location": "Chr11: 72.311676",
+ "mean": "7.194",
+ "additive": "0.050",
+ "lod_score": "2.1",
+ "lrs_location": "Chr9: 69.810185",
+ "sample_r": "-0.661",
+ "num_overlap": 67,
+ "sample_p": "1.175e-09",
+ "lit_corr": "--",
+ "tissue_corr": "0.557",
+ "tissue_pvalue": "3.116e-03"
+ },
+ {
+ "index": 15,
+ "trait_id": "1437477_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1437477_at:HC_M2_0606_P:990d16df933d7bb03428",
+ "symbol": "Lrrfip1",
+ "description": "leucine rich repeat (in FLII) interacting protein 1",
+ "location": "Chr1: 93.011523",
+ "mean": "7.597",
+ "additive": "0.068",
+ "lod_score": "2.3",
+ "lrs_location": "Chr5: 133.538653",
+ "sample_r": "-0.658",
+ "num_overlap": 67,
+ "sample_p": "1.393e-09",
+ "lit_corr": "--",
+ "tissue_corr": "0.132",
+ "tissue_pvalue": "5.204e-01"
+ },
+ {
+ "index": 16,
+ "trait_id": "1440212_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1440212_at:HC_M2_0606_P:01ae82e4856177dd9d89",
+ "symbol": "Slc12a1",
+ "description": "solute carrier family 12, member 1",
+ "location": "Chr2: 124.990152",
+ "mean": "7.061",
+ "additive": "0.038",
+ "lod_score": "2.2",
+ "lrs_location": "Chr1: 193.731996",
+ "sample_r": "-0.655",
+ "num_overlap": 67,
+ "sample_p": "1.769e-09",
+ "lit_corr": "--",
+ "tissue_corr": "0.028",
+ "tissue_pvalue": "8.923e-01"
+ },
+ {
+ "index": 17,
+ "trait_id": "1419755_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1419755_at:HC_M2_0606_P:15fea7c69b0d5faa1298",
+ "symbol": "Mfi2",
+ "description": "antigen p97 (melanoma associated) identified by monoclonal antibodies 133.2 and 96.5",
+ "location": "Chr16: 31.898518",
+ "mean": "6.697",
+ "additive": "-0.038",
+ "lod_score": "2.0",
+ "lrs_location": "Chr4: 50.881071",
+ "sample_r": "-0.654",
+ "num_overlap": 67,
+ "sample_p": "1.950e-09",
+ "lit_corr": "--",
+ "tissue_corr": "0.244",
+ "tissue_pvalue": "2.305e-01"
+ },
+ {
+ "index": 18,
+ "trait_id": "1425457_a_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1425457_a_at:HC_M2_0606_P:669c485b158c0207026c",
+ "symbol": "Grb10",
+ "description": "growth factor receptor bound protein 10",
+ "location": "Chr11: 11.833500",
+ "mean": "6.515",
+ "additive": "0.081",
+ "lod_score": "3.9",
+ "lrs_location": "Chr5: 133.538653",
+ "sample_r": "-0.652",
+ "num_overlap": 67,
+ "sample_p": "2.295e-09",
+ "lit_corr": "--",
+ "tissue_corr": "-0.090",
+ "tissue_pvalue": "6.617e-01"
+ },
+ {
+ "index": 19,
+ "trait_id": "1431329_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1431329_at:HC_M2_0606_P:a6df7ed818ea0042c550",
+ "symbol": "Nphp4",
+ "description": "nephronophthisis 4 (renal tubular development and function)",
+ "location": "Chr4: 151.863271",
+ "mean": "6.191",
+ "additive": "0.039",
+ "lod_score": "1.9",
+ "lrs_location": "ChrX: 112.637353",
+ "sample_r": "-0.652",
+ "num_overlap": 67,
+ "sample_p": "2.330e-09",
+ "lit_corr": "--",
+ "tissue_corr": "-0.104",
+ "tissue_pvalue": "6.144e-01"
+ },
+ {
+ "index": 20,
+ "trait_id": "1443987_at",
+ "dataset": "HC_M2_0606_P",
+ "hmac": "1443987_at:HC_M2_0606_P:681e6c787b4d652d0c07",
+ "symbol": "Klhl18",
+ "description": "kelch-like 18 (Drosophila)",
+ "location": "Chr9: 110.330597",
+ "mean": "7.244",
+ "additive": "-0.070",
+ "lod_score": "2.1",
+ "lrs_location": "Chr15: 13.149248",
+ "sample_r": "-0.650",
+ "num_overlap": 67,
+ "sample_p": "2.561e-09",
+ "lit_corr": "--",
+ "tissue_corr": "-0.200",
+ "tissue_pvalue": "3.270e-01"
+ }
+] \ No newline at end of file
diff --git a/tests/unit/correlation/test_correlation_computations.py b/tests/unit/correlation/test_correlation_computations.py
new file mode 100644
index 0000000..dbb2587
--- /dev/null
+++ b/tests/unit/correlation/test_correlation_computations.py
@@ -0,0 +1,65 @@
+"""module for testing correlation/correlation_computations"""
+
+import unittest
+from gn3.correlation.correlation_computations import compute_correlation
+
+
+# mock for calculating correlation function
+
+def mock_get_loading_page_data(initial_start_vars):
+ """function to mock filtering input"""
+ results = {'start_vars':
+ {'genofile': 'SAMPLE:X', 'dataset': 'HC_M2_0606_P',
+ 'sample_vals': '{"C57BL/6J":"7.197","DBA/2J":"7.148","B6D2F1":"6.999"}',
+ 'primary_samples': 'C57BL/6J,DBA/2J,B6D2F1',
+ 'n_samples': 3,
+ 'wanted_inputs': "sample_vals,dataset,genofile,primary_samples"}}
+
+ return results
+
+
+class MockCorrelationResults:
+ """mock class for CorrelationResults"""
+
+ def __init__(self, start_vars):
+ for _key, value in start_vars.items():
+ self.value = value
+
+ self.assert_start_vars(start_vars)
+
+ @staticmethod
+ def assert_start_vars(start_vars):
+ """assert data required is supplied"""
+ assert "wanted_inputs" in start_vars
+
+ def do_correlation(self, start_vars):
+ """mock method for doing correlation"""
+
+ return {
+ "results": "success"
+ }
+
+
+class TestCorrelationUtility(unittest.TestCase):
+ """tests for correlation computations"""
+
+ def test_compute_correlation(self):
+ """test function for doing correlation"""
+
+ sample_vals = """{"C57BL/6J":"7.197","DBA/2J":"7.148","B6D2F1":"6.999"}"""
+
+ correlation_input_data = {
+ "wanted_inputs": "sample_vals,dataset,genofile,primary_samples",
+ "genofile": "SAMPLE:X",
+ "dataset": "HC_M2_0606_P",
+
+ "sample_vals": sample_vals,
+ "primary_samples": "C57BL/6J,DBA/2J,B6D2F1"
+
+ }
+ correlation_results = compute_correlation(
+ correlation_input_data=correlation_input_data,
+ correlation_results=MockCorrelationResults)
+ results = {"results": "success"}
+
+ self.assertEqual(results,correlation_results)
diff --git a/tests/unit/correlation/test_show_corr_results.py b/tests/unit/correlation/test_show_corr_results.py
new file mode 100644
index 0000000..4846f5e
--- /dev/null
+++ b/tests/unit/correlation/test_show_corr_results.py
@@ -0,0 +1,226 @@
+"""module contains code for testing creating show correlation object"""
+
+import unittest
+import json
+import os
+from unittest import mock
+from types import SimpleNamespace
+from gn3.correlation.show_corr_results import CorrelationResults
+from gn3.correlation.show_corr_results import get_header_fields
+from gn3.correlation.show_corr_results import generate_corr_json
+# pylint: disable=unused-argument
+
+
+
+class ObjectMixin:
+ """object for adding other methods"""
+ def __str__(self):
+ raise NotImplementedError
+
+ def get_dict(self):
+ raise NotImplementedError
+
+class MockGroup(ObjectMixin):
+ """mock class for Group"""
+
+ def __init__(self):
+ self.samplelist = "add a mock for this"
+ self.parlist = None
+
+ self.filist = None
+
+class MockCreateTrait(ObjectMixin):
+ """mock class for create trait"""
+
+ def __init__(self):
+ pass
+
+ def get_dict(self):
+ """class for getting dict items"""
+ return self.__dict__
+
+ def __str__(self):
+ return self.__class__.__name__
+
+
+class MockCreateDataset:
+ """mock class for create dataset"""
+
+ def __init__(self):
+
+ self.group = MockGroup()
+
+ def get_trait_data(self, sample_keys):
+ """method for getting trait data"""
+ raise NotImplementedError()
+
+ def retrieve_genes(self, symbol):
+ """method for retrieving genes"""
+ raise NotImplementedError()
+
+
+def file_path(relative_path):
+ """getting abs path for file """
+ # adopted from github
+ dir_name = os.path.dirname(os.path.abspath(__file__))
+ split_path = relative_path.split("/")
+ new_path = os.path.join(dir_name, *split_path)
+ return new_path
+
+
+def create_trait(dataset="Temp", name=None, cellid=None):
+ """mock function for creating trait"""
+ return "trait results"
+
+
+def create_dataset(dataset_name="Temp", dataset_type="Temp", group_name=None):
+ """mock function to create dataset """
+ return "dataset results"
+
+
+def get_species(self, start_vars):
+ """
+ how this function works is that it sets the self.dataset and self.species and self.this_trait
+ """
+
+ with open(file_path("./dataset.json")) as dataset_file:
+ results = json.load(dataset_file)
+ self.dataset = SimpleNamespace(**results)
+
+ with open(file_path("./group_data_test.json")) as group_file:
+ results = json.load(group_file)
+ self.group = SimpleNamespace(**results)
+
+ self.dataset.group = self.group
+
+ trait_dict = {'name': '1434568_at', 'dataset': self.dataset, 'cellid': None,
+ 'identification': 'un-named trait', 'haveinfo': True, 'sequence': None}
+
+ trait_obj = SimpleNamespace(**trait_dict)
+
+ self.this_trait = trait_obj
+
+ self.species = "this species data"
+
+
+class TestCorrelationResults(unittest.TestCase):
+ """unittests for Correlation Results"""
+
+ def setUp(self):
+
+ with open(file_path("./correlation_test_data.json")) as json_file:
+ self.correlation_data = json.load(json_file)
+
+ def tearDown(self):
+
+ self.correlation_data = ""
+
+ def test_for_assertion(self):
+ """test for assertion failures"""
+ with self.assertRaises(AssertionError):
+ _corr_results_object = CorrelationResults(start_vars={})
+
+ @mock.patch("gn3.correlation.show_corr_results.CorrelationResults.process_samples")
+ def test_do_correlation(self, process_samples):
+ """test for doing correlation"""
+ process_samples.return_value = None
+ corr_object = CorrelationResults(start_vars=self.correlation_data)
+
+ with self.assertRaises(Exception) as _error:
+
+ # xtodo;to be completed
+
+ _corr_results = corr_object.do_correlation(start_vars=self.correlation_data,
+ create_dataset=create_dataset,
+ create_trait=None,
+ get_species_dataset_trait=get_species)
+
+
+
+ def test_get_header_fields(self):
+ expected = [
+ ['Index',
+ 'Record',
+ 'Symbol',
+ 'Description',
+ 'Location',
+ 'Mean',
+ 'Sample rho',
+ 'N',
+ 'Sample p(rho)',
+ 'Lit rho',
+ 'Tissue rho',
+ 'Tissue p(rho)',
+ 'Max LRS',
+ 'Max LRS Location',
+ 'Additive Effect'],
+
+ ['Index',
+ 'ID',
+ 'Location',
+ 'Sample r',
+ 'N',
+ 'Sample p(r)']
+
+ ]
+ result1 = get_header_fields("ProbeSet", "spearman")
+ result2 = get_header_fields("Other", "Other")
+ self.assertEqual(result1, expected[0])
+ self.assertEqual(result2, expected[1])
+
+
+
+ @mock.patch("gn3.utility.hmac.data_hmac")
+ def test_generate_corr_json(self, mock_data_hmac):
+ mock_data_hmac.return_value = "hajsdiau"
+
+ dataset = SimpleNamespace(**{"name": "the_name"})
+ this_trait = SimpleNamespace(**{"name": "trait_test", "dataset": dataset})
+ target_dataset = SimpleNamespace(**{"type": "Publish"})
+ corr_trait_1 = SimpleNamespace(**{
+ "name": "trait_1",
+ "dataset": SimpleNamespace(**{"name": "dataset_1"}),
+ "view": True,
+ "abbreviation": "T1",
+ "description_display": "Trait I description",
+ "authors": "JM J,JYEW",
+ "pubmed_id": "34n4nn31hn43",
+ "pubmed_text": "2016",
+ "pubmed_link": "https://www.load",
+ "lod_score": "",
+ "mean": "",
+ "LRS_location_repr": "BXBS",
+ "additive": "",
+ "sample_r": 10.5,
+ "num_overlap": 2,
+ "sample_p": 5
+
+
+
+
+ })
+ corr_results = [corr_trait_1]
+
+ dataset_type_other = {
+ "location": "cx-3-4",
+ "sample_4": 12.32,
+ "num_overlap": 3,
+ "sample_p": 10.34
+ }
+
+ expected_results = '[{"index": 1, "trait_id": "trait_1", "dataset": "dataset_1", "hmac": "hajsdiau", "abbreviation_display": "T1", "description": "Trait I description", "mean": "N/A", "authors_display": "JM J,JYEW", "additive": "N/A", "pubmed_id": "34n4nn31hn43", "year": "2016", "lod_score": "N/A", "lrs_location": "BXBS", "sample_r": "10.500", "num_overlap": 2, "sample_p": "5.000e+00"}]'
+
+ results1 = generate_corr_json(corr_results=corr_results, this_trait=this_trait,
+ dataset=dataset, target_dataset=target_dataset, for_api=True)
+ self.assertEqual(expected_results, results1)
+
+
+ def test_generate_corr_json_view_false(self):
+ trait = SimpleNamespace(**{"view": False})
+ corr_results = [trait]
+ this_trait = SimpleNamespace(**{"name": "trait_test"})
+ dataset = SimpleNamespace(**{"name": "the_name"})
+
+ results_where_view_is_false = generate_corr_json(
+ corr_results=corr_results, this_trait=this_trait, dataset={}, target_dataset={}, for_api=False)
+ self.assertEqual(results_where_view_is_false, "[]") \ No newline at end of file
diff --git a/tests/unit/utility/__init__.py b/tests/unit/utility/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/tests/unit/utility/__init__.py
diff --git a/tests/unit/utility/test_chunks.py b/tests/unit/utility/test_chunks.py
new file mode 100644
index 0000000..7c42b44
--- /dev/null
+++ b/tests/unit/utility/test_chunks.py
@@ -0,0 +1,19 @@
+"""Test chunking"""
+
+import unittest
+
+from gn3.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/tests/unit/utility/test_corr_result_helpers.py b/tests/unit/utility/test_corr_result_helpers.py
new file mode 100644
index 0000000..ce5891f
--- /dev/null
+++ b/tests/unit/utility/test_corr_result_helpers.py
@@ -0,0 +1,35 @@
+""" Test correlation helper methods """
+
+import unittest
+from gn3.utility.corr_result_helpers import normalize_values
+from gn3.utility.corr_result_helpers import common_keys
+from gn3.utility.corr_result_helpers import 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"""
+ test_a = dict(BXD1=9.113, BXD2=9.825, BXD14=8.985, BXD15=9.300)
+ test_b = dict(BXD1=9.723, BXD3=9.825, BXD14=9.124, BXD16=9.300)
+ self.assertEqual(sorted(common_keys(test_a, test_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/tests/unit/utility/test_hmac.py b/tests/unit/utility/test_hmac.py
new file mode 100644
index 0000000..eba25a3
--- /dev/null
+++ b/tests/unit/utility/test_hmac.py
@@ -0,0 +1,51 @@
+"""Test hmac utility functions"""
+# pylint: disable-all
+import unittest
+from unittest import mock
+
+from gn3.utility.hmac import data_hmac
+from gn3.utility.hmac import url_for_hmac
+from gn3.utility.hmac import hmac_creation
+
+
+class TestHmacUtil():
+ """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")