Rewrote show_corr_results.py to remove all code calculating correlations (that was moved to correlation_gn3_api.py, which will probably be renamed at some point) and only include the code generating the table JSON and some template variables

1 files changed, 139 insertions, 449 deletions

diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index 2f3df67a..6c6d8f4e 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -18,474 +18,145 @@
 #
 # This module is used by GeneNetwork project (www.genenetwork.org)
 
-import collections
 import json
-import scipy
-import numpy
-# import rpy2.robjects as ro                    # R Objects
-import utility.logger
-import utility.webqtlUtil
 
-from base.trait import create_trait
+from base.trait import create_trait, jsonable
+from base.data_set import create_dataset
 
-from base import data_set
-from utility import helper_functions
-from utility import corr_result_helpers
 from utility import hmac
 
-from wqflask.correlation import correlation_functions
-from utility.benchmark import Bench
-
-from utility.type_checking import is_str
-from utility.type_checking import get_float
-from utility.type_checking import get_int
-from utility.type_checking import get_string
-from utility.db_tools import escape
-
-from flask import g
-
-logger = utility.logger.getLogger(__name__)
-
-METHOD_LIT = "3"
-METHOD_TISSUE_PEARSON = "4"
-METHOD_TISSUE_RANK = "5"
-
-TISSUE_METHODS = [METHOD_TISSUE_PEARSON, METHOD_TISSUE_RANK]
-
-TISSUE_MOUSE_DB = 1
-
-
-class CorrelationResults:
-    def __init__(self, start_vars):
-        # get trait list from db (database name)
-        # calculate correlation with Base vector and targets
-
-        # Check parameters
-        assert('corr_type' in start_vars)
-        assert(is_str(start_vars['corr_type']))
-        assert('dataset' in start_vars)
-        # assert('group' in start_vars) permitted to be empty?
-        assert('corr_sample_method' in start_vars)
-        assert('corr_samples_group' in start_vars)
-        assert('corr_dataset' in start_vars)
-        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)
-
-        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.trait_id = start_vars['trait_id']
-                self.this_trait = create_trait(dataset=self.dataset,
-                                               name=self.trait_id,
-                                               cellid=None)
-            else:
-                helper_functions.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 = get_float(start_vars, 'min_expr')
-            self.p_range_lower = get_float(start_vars, 'p_range_lower', -1.0)
-            self.p_range_upper = get_float(start_vars, 'p_range_upper', 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 = get_string(start_vars, 'location_type')
-                self.location_chr = get_string(start_vars, 'loc_chr')
-                self.min_location_mb = get_int(start_vars, 'min_loc_mb')
-                self.max_location_mb = get_int(start_vars, 'max_loc_mb')
-            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'])
-
-            # 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:
-                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':
-                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 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 = 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)
-
-            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 = []
+def set_template_vars(start_vars, correlation_data):
+    corr_type = start_vars['corr_type']
+    corr_method = start_vars['corr_sample_method']
 
-            self.correlation_data = {}
+    this_dataset_ob = create_dataset(dataset_name=start_vars['dataset'])
+    this_trait = create_trait(dataset=this_dataset_ob,
+                              name=start_vars['trait_id'])
 
-            if self.corr_type == "tissue":
-                self.trait_symbol_dict = self.dataset.retrieve_genes("Symbol")
+    correlation_data['this_trait'] = jsonable(this_trait, this_dataset_ob)
+    correlation_data['this_dataset'] = this_dataset_ob.as_dict()
 
-                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
-            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)
-
-############################################################################################################################################
-
-    def get_formatted_corr_type(self):
-        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 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
-        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_tissue_correlation_for_all_traits(self, tissue_dataset_id=1):
-        # 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(
-            )]
-
-            #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(
-                    )]
+    target_dataset_ob = create_dataset(correlation_data['target_dataset'])
+    correlation_data['target_dataset'] = target_dataset_ob.as_dict()
 
-                    result = correlation_functions.cal_zero_order_corr_for_tiss(primary_trait_tissue_values,
-                                                                                this_trait_tissue_values,
-                                                                                self.corr_method)
+    table_json = correlation_json_for_table(correlation_data,
+                                            correlation_data['this_trait'],
+                                            correlation_data['this_dataset'],
+                                            target_dataset_ob)
 
-                    tissue_corr_data[trait] = [symbol, result[0], result[2]]
+    correlation_data['table_json'] = table_json
 
-            tissue_corr_data = collections.OrderedDict(sorted(list(tissue_corr_data.items()),
-                                                              key=lambda t: -abs(t[1][1])))
+    if target_dataset_ob.type == "ProbeSet":
+        filter_cols = [7, 6]
+    elif target_dataset_ob.type == "Publish":
+        filter_cols = [6, 0]
+    else:
+        filter_cols = [4, 0]
 
-            return tissue_corr_data
+    correlation_data['corr_method'] = corr_method
+    correlation_data['filter_cols'] = filter_cols
+    correlation_data['header_fields'] = get_header_fields(target_dataset_ob.type, correlation_data['corr_method'])
+    correlation_data['formatted_corr_type'] = get_formatted_corr_type(corr_type, corr_method)
 
-    def do_lit_correlation_for_trait_list(self):
+    return correlation_data
 
-        input_trait_mouse_gene_id = self.convert_to_mouse_gene_id(
-            self.dataset.group.species.lower(), self.this_trait.geneid)
+def correlation_json_for_table(correlation_data, this_trait, this_dataset, target_dataset_ob):
+    """Return JSON data for use with the DataTable in the correlation result page
 
-        for trait in self.correlation_results:
+    Keyword arguments:
+    correlation_data -- Correlation results
+    this_trait -- Trait being correlated against a dataset, as a dict
+    this_dataset -- Dataset of this_trait, as a dict
+    target_dataset_ob - Target dataset, as a Dataset ob
+    """
+    this_trait = correlation_data['this_trait']
+    this_dataset = correlation_data['this_dataset']
+    target_dataset = target_dataset_ob.as_dict()
 
-            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):
-        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]
+    corr_results = correlation_data['correlation_results']
+    results_list = []
+    for i, trait_dict in enumerate(corr_results):
+        trait_name = list(trait_dict.keys())[0]
+        trait = trait_dict[trait_name]
+        target_trait_ob = create_trait(dataset=target_dataset_ob,
+                                       name=trait_name,
+                                       get_qtl_info=True)
+        target_trait = jsonable(target_trait_ob, target_dataset_ob)
+        if target_trait['view'] == False:
+            continue
+        results_dict = {}
+        results_dict['index'] = i + 1
+        results_dict['trait_id'] = target_trait['name']
+        results_dict['dataset'] = target_dataset['name']
+        results_dict['hmac'] = hmac.data_hmac(
+            '{}:{}'.format(target_trait['name'], target_dataset['name']))
+        results_dict['sample_r'] = f"{float(trait['corr_coeffient']):.3f}"
+        results_dict['num_overlap'] = trait['num_overlap']
+        results_dict['sample_p'] = f"{float(trait['p_value']):.3e}"
+        if target_dataset['type'] == "ProbeSet":
+            results_dict['symbol'] = target_trait['symbol']
+            results_dict['description'] = "N/A"
+            results_dict['location'] = target_trait['location']
+            results_dict['mean'] = "N/A"
+            results_dict['additive'] = "N/A"
+            if bool(target_trait['description']):
+                results_dict['description'] = target_trait['description']
+            if bool(target_trait['mean']):
+                results_dict['mean'] = f"{float(target_trait['mean']):.3f}"
+            try:
+                results_dict['lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}"
+            except:
+                results_dict['lod_score'] = "N/A"
+            results_dict['lrs_location'] = target_trait['lrs_location']
+            if bool(target_trait['additive']):
+                results_dict['additive'] = f"{float(target_trait['additive']):.3f}"
+            results_dict['lit_corr'] = "--"
+            results_dict['tissue_corr'] = "--"
+            results_dict['tissue_pvalue'] = "--"
+            if this_dataset['type'] == "ProbeSet":
+                if 'lit_corr' in trait:
+                    results_dict['lit_corr'] = f"{float(trait['lit_corr']):.3f}"
+                if 'tissue_corr' in trait:
+                    results_dict['tissue_corr'] = f"{float(trait['tissue_corr']):.3f}"
+                    results_dict['tissue_pvalue'] = f"{float(trait['tissue_p_val']):.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"
+            results_dict['pubmed_link'] = "N/A"
+            results_dict['pubmed_text'] = "N/A"
+
+            if bool(target_trait['abbreviation']):
+                results_dict['abbreviation_display'] = target_trait['abbreviation']
+            if bool(target_trait['description']):
+                results_dict['description'] = target_trait['description']
+            if bool(target_trait['mean']):
+                results_dict['mean'] = f"{float(target_trait['mean']):.3f}"
+            if bool(target_trait['authors']):
+                authors_list = target_trait['authors'].split(',')
+                if len(authors_list) > 6:
+                    results_dict['authors_display'] = ", ".join(
+                        authors_list[:6]) + ", et al."
                 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 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 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.
-
-        """
-
-        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 = 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/
-            # if self.corr_method == 'bicor':
-            #     sample_r, sample_p = do_bicor(
-            #         self.this_trait_vals, target_vals)
-            if 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 process_samples(self, start_vars, sample_names, excluded_samples=None):
-        if not excluded_samples:
-            excluded_samples = ()
+                    results_dict['authors_display'] = target_trait['authors']
+            if 'pubmed_id' in target_trait:
+                results_dict['pubmed_link'] = target_trait['pubmed_link']
+                results_dict['pubmed_text'] = target_trait['pubmed_text']
+            try:
+                results_dict['lod_score'] = f"{float(target_trait['lrs_score']) / 4.61:.1f}"
+            except:
+                results_dict['lod_score'] = "N/A"
+            results_dict['lrs_location'] = target_trait['lrs_location']
+            if bool(target_trait['additive']):
+                results_dict['additive'] = f"{float(target_trait['additive']):.3f}"
+        else:
+            results_dict['location'] = target_trait['lrs_location']
 
-        sample_val_dict = json.loads(start_vars['sample_vals'])
-        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)
+        results_list.append(results_dict)
 
+    return json.dumps(results_list)
 
 # def do_bicor(this_trait_vals, target_trait_vals):
 #     r_library = ro.r["library"]             # Map the library function
@@ -598,6 +269,25 @@ def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_ap
     return json.dumps(results_list)
 
 
+def get_formatted_corr_type(corr_type, corr_method):
+    formatted_corr_type = ""
+    if corr_type == "lit":
+        formatted_corr_type += "Literature Correlation "
+    elif corr_type == "tissue":
+        formatted_corr_type += "Tissue Correlation "
+    elif corr_type == "sample":
+        formatted_corr_type += "Genetic Correlation "
+
+    if corr_method == "pearson":
+        formatted_corr_type += "(Pearson's r)"
+    elif corr_method == "spearman":
+        formatted_corr_type += "(Spearman's rho)"
+    elif corr_method == "bicor":
+        formatted_corr_type += "(Biweight r)"
+
+    return formatted_corr_type
+
+
 def get_header_fields(data_type, corr_method):
     if data_type == "ProbeSet":
         if corr_method == "spearman":