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authorAlexander Kabui2021-03-24 12:59:49 +0300
committerAlexander Kabui2021-03-24 12:59:49 +0300
commitd913848572dd284ae7656e72dad199e99907871a (patch)
tree7a1757eb1ca6289c44a549f72ba1f431debe7f79 /wqflask
parentbe9c4a39500d7978b4cae7536a5f96c3818d211e (diff)
downloadgenenetwork2-d913848572dd284ae7656e72dad199e99907871a.tar.gz
initial commit for integrating to gn3 api
Diffstat (limited to 'wqflask')
-rw-r--r--wqflask/wqflask/correlation/show_corr_results.py428
1 files changed, 242 insertions, 186 deletions
diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index fb4dc4f4..a817a4a4 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -1,4 +1,4 @@
-## Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
+# 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
@@ -58,6 +58,31 @@ TISSUE_METHODS = [METHOD_TISSUE_PEARSON, METHOD_TISSUE_RANK]
TISSUE_MOUSE_DB = 1
+def compute_sample_r(start_vars,target_dataset, trait_data, target_samplelist, method="pearson"):
+ import requests
+ from wqflask.correlation.correlation_gn3_api import compute_correlation
+
+ # cor_results = compute_correlation(start_vars)
+
+ data = {
+ "target_dataset": target_dataset,
+ "target_samplelist": target_samplelist,
+ "trait_data": {
+ "trait_sample_data": trait_data,
+ "trait_id": "HC_Q"
+ }
+ }
+ requests_url = f"http://127.0.0.1:8080/api/correlation/sample_x/{method}"
+
+ results = requests.post(requests_url, json=data)
+
+ data = results.json()
+
+ print(data)
+
+ return data
+
+
class CorrelationResults(object):
def __init__(self, start_vars):
# get trait list from db (database name)
@@ -78,11 +103,12 @@ class CorrelationResults(object):
with Bench("Doing correlations"):
if start_vars['dataset'] == "Temp":
- self.dataset = data_set.create_dataset(dataset_name = "Temp", dataset_type = "Temp", group_name = start_vars['group'])
+ self.dataset = data_set.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)
+ name=self.trait_id,
+ cellid=None)
else:
helper_functions.get_species_dataset_trait(self, start_vars)
@@ -97,7 +123,7 @@ class CorrelationResults(object):
if ('loc_chr' in start_vars and
'min_loc_mb' in start_vars and
- 'max_loc_mb' in start_vars):
+ 'max_loc_mb' in start_vars):
self.location_type = get_string(start_vars, 'location_type')
self.location_chr = get_string(start_vars, 'loc_chr')
@@ -109,8 +135,8 @@ class CorrelationResults(object):
self.get_formatted_corr_type()
self.return_number = int(start_vars['corr_return_results'])
- #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
+ # 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
primary_samples = self.dataset.group.samplelist
if self.dataset.group.parlist != None:
@@ -118,23 +144,26 @@ class CorrelationResults(object):
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 either BXD/whatever Only or All Samples, append all of that group's samplelist
if corr_samples_group != 'samples_other':
self.process_samples(start_vars, primary_samples)
- #If either Non-BXD/whatever or All Samples, get all samples from this_trait.data and
- #exclude the primary samples (because they would have been added in the previous
- #if statement if the user selected All Samples)
+ # If either Non-BXD/whatever or All Samples, get all samples from this_trait.data and
+ # exclude the primary samples (because they would have been added in the previous
+ # if statement if the user selected All 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.dataset.group.parlist + self.dataset.group.f1list)]
+ self.process_samples(start_vars, list(
+ self.this_trait.data.keys()), primary_samples)
- self.target_dataset = data_set.create_dataset(start_vars['corr_dataset'])
+ self.target_dataset = data_set.create_dataset(
+ start_vars['corr_dataset'])
self.target_dataset.get_trait_data(list(self.sample_data.keys()))
- self.header_fields = get_header_fields(self.target_dataset.type, self.corr_method)
+ self.header_fields = get_header_fields(
+ self.target_dataset.type, self.corr_method)
if self.target_dataset.type == "ProbeSet":
self.filter_cols = [7, 6]
@@ -153,7 +182,8 @@ class CorrelationResults(object):
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])
+ 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)
@@ -163,80 +193,85 @@ class CorrelationResults(object):
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])
+ 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
- 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)
-
- if self.corr_type != "lit" and self.dataset.type == "ProbeSet" and self.target_dataset.type == "ProbeSet":
- 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.json_results = generate_corr_json(self.correlation_results, self.this_trait, self.dataset, self.target_dataset)
+
+ compute_sample_r(start_vars,
+ self.target_dataset.trait_data, self.sample_data, self.target_dataset.samplelist)
+ # 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
+ # 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)
+
+ # if self.corr_type != "lit" and self.dataset.type == "ProbeSet" and self.target_dataset.type == "ProbeSet":
+ # 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.json_results = generate_corr_json(
+ # self.correlation_results, self.this_trait, self.dataset, self.target_dataset)
############################################################################################################################################
@@ -259,39 +294,43 @@ class CorrelationResults(object):
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"""
- #Gets tissue expression values for the primary trait
+ # 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])
+ 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]
+ 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)
+ 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()]
+ 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)
+ this_trait_tissue_values,
+ self.corr_method)
trait.tissue_corr = result[0]
trait.tissue_pvalue = result[2]
def do_tissue_correlation_for_all_traits(self, tissue_dataset_id=1):
- #Gets tissue expression values for the primary trait
+ # 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])
+ 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()]
+ 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()))
+ 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))
@@ -300,27 +339,30 @@ class CorrelationResults(object):
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()]
+ 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)
+ 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])))
+ key=lambda t: -abs(t[1][1])))
return tissue_corr_data
def do_lit_correlation_for_trait_list(self):
- input_trait_mouse_gene_id = self.convert_to_mouse_gene_id(self.dataset.group.species.lower(), self.this_trait.geneid)
+ 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)
+ trait.mouse_gene_id = self.convert_to_mouse_gene_id(
+ self.dataset.group.species.lower(), trait.geneid)
else:
trait.mouse_gene_id = None
@@ -348,13 +390,14 @@ class CorrelationResults(object):
else:
trait.lit_corr = 0
-
def do_lit_correlation_for_all_traits(self):
- input_trait_mouse_gene_id = self.convert_to_mouse_gene_id(self.dataset.group.species.lower(), self.this_trait.geneid)
+ 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)
+ 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)
@@ -382,7 +425,7 @@ class CorrelationResults(object):
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])))
+ key=lambda t: -abs(t[1][1])))
return lit_corr_data
@@ -422,6 +465,7 @@ class CorrelationResults(object):
return mouse_gene_id
+
def get_sample_r_and_p_values(self, trait, target_samples):
"""Calculates the sample r (or rho) and p-value
@@ -431,6 +475,9 @@ class CorrelationResults(object):
"""
+ print("below here>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
+ print(self.target_dataset.trait_data)
+
self.this_trait_vals = []
target_vals = []
for index, sample in enumerate(self.target_dataset.samplelist):
@@ -440,21 +487,26 @@ class CorrelationResults(object):
self.this_trait_vals.append(sample_value)
target_vals.append(target_sample_value)
- self.this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(self.this_trait_vals, target_vals)
+ self.this_trait_vals, target_vals, num_overlap = corr_result_helpers.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/
+ # 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)
+ 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)
+ 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)
+ 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]
+ self.correlation_data[trait] = [
+ sample_r, sample_p, num_overlap]
def process_samples(self, start_vars, sample_names, excluded_samples=None):
if not excluded_samples:
@@ -475,16 +527,18 @@ def do_bicor(this_trait_vals, target_trait_vals):
r_library("WGCNA")
r_bicor = ro.r["bicorAndPvalue"] # Map the bicorAndPvalue function
- r_options(stringsAsFactors = False)
+ 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)]
+ 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 generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_api = False):
+
+def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_api=False):
results_list = []
for i, trait in enumerate(corr_results):
if trait.view == False:
@@ -493,7 +547,8 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
results_dict['index'] = i + 1
results_dict['trait_id'] = trait.name
results_dict['dataset'] = trait.dataset.name
- results_dict['hmac'] = hmac.data_hmac('{}:{}'.format(trait.name, trait.dataset.name))
+ 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"
@@ -544,7 +599,8 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
if bool(trait.authors):
authors_list = trait.authors.split(',')
if len(authors_list) > 6:
- results_dict['authors_display'] = ", ".join(authors_list[:6]) + ", et al."
+ results_dict['authors_display'] = ", ".join(
+ authors_list[:6]) + ", et al."
else:
results_dict['authors_display'] = trait.authors
if bool(trait.pubmed_id):
@@ -574,85 +630,85 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
return json.dumps(results_list)
+
def get_header_fields(data_type, corr_method):
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']
+ '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',
- 'Symbol',
- 'Description',
- 'Location',
- 'Mean',
- 'Sample r',
- 'N',
- 'Sample p(r)',
- 'Lit r',
- 'Tissue r',
- 'Tissue p(r)',
- 'Max LRS',
- 'Max LRS Location',
- 'Additive Effect']
+ 'Record',
+ 'Symbol',
+ 'Description',
+ 'Location',
+ 'Mean',
+ 'Sample r',
+ 'N',
+ 'Sample p(r)',
+ 'Lit r',
+ 'Tissue r',
+ 'Tissue 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']
+ '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']
+ '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)']
+ 'ID',
+ 'Location',
+ 'Sample rho',
+ 'N',
+ 'Sample p(rho)']
else:
header_fields = ['Index',
- 'ID',
- 'Location',
- 'Sample r',
- 'N',
- 'Sample p(r)']
+ 'ID',
+ 'Location',
+ 'Sample r',
+ 'N',
+ 'Sample p(r)']
return header_fields
-