Merge pull request #587 from zsloan/feature/use_gn3_for_correlations

Feature/use gn3 for correlations

7 files changed, 196 insertions, 497 deletions

diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index 6dc44829..4cb82665 100644
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -557,6 +557,7 @@ class DataSet:
         self.fullname = None
         self.type = None
         self.data_scale = None  # ZS: For example log2
+        self.accession_id = None
 
         self.setup()
 
@@ -573,6 +574,17 @@ class DataSet:
             self.group.get_samplelist()
         self.species = species.TheSpecies(self)
 
+    def as_dict(self):
+        return {
+            'name': self.name,
+            'shortname': self.shortname,
+            'fullname': self.fullname,
+            'type': self.type,
+            'data_scale': self.data_scale,
+            'group': self.group.name,
+            'accession_id': self.accession_id
+        }
+
     def get_accession_id(self):
         if self.type == "Publish":
             results = g.db.execute("""select InfoFiles.GN_AccesionId from InfoFiles, PublishFreeze, InbredSet where
diff --git a/wqflask/base/trait.py b/wqflask/base/trait.py
index d09cfd40..10851e00 100644
--- a/wqflask/base/trait.py
+++ b/wqflask/base/trait.py
@@ -284,17 +284,19 @@ def get_sample_data():
         return None
 
 
-def jsonable(trait):
+def jsonable(trait, dataset=None):
     """Return a dict suitable for using as json
 
     Actual turning into json doesn't happen here though"""
 
-    dataset = create_dataset(dataset_name=trait.dataset.name,
-                             dataset_type=trait.dataset.type,
-                             group_name=trait.dataset.group.name)
+    if not dataset:
+        dataset = create_dataset(dataset_name=trait.dataset.name,
+                                dataset_type=trait.dataset.type,
+                                group_name=trait.dataset.group.name)
 
     if dataset.type == "ProbeSet":
         return dict(name=trait.name,
+                    view=trait.view,
                     symbol=trait.symbol,
                     dataset=dataset.name,
                     dataset_name=dataset.shortname,
@@ -308,37 +310,44 @@ def jsonable(trait):
     elif dataset.type == "Publish":
         if trait.pubmed_id:
             return dict(name=trait.name,
+                        view=trait.view,
                         dataset=dataset.name,
                         dataset_name=dataset.shortname,
                         description=trait.description_display,
                         abbreviation=trait.abbreviation,
                         authors=trait.authors,
+                        pubmed_id=trait.pubmed_id,
                         pubmed_text=trait.pubmed_text,
                         pubmed_link=trait.pubmed_link,
+                        mean=trait.mean,
                         lrs_score=trait.LRS_score_repr,
                         lrs_location=trait.LRS_location_repr,
                         additive=trait.additive
                         )
         else:
             return dict(name=trait.name,
+                        view=trait.view,
                         dataset=dataset.name,
                         dataset_name=dataset.shortname,
                         description=trait.description_display,
                         abbreviation=trait.abbreviation,
                         authors=trait.authors,
                         pubmed_text=trait.pubmed_text,
+                        mean=trait.mean,
                         lrs_score=trait.LRS_score_repr,
                         lrs_location=trait.LRS_location_repr,
                         additive=trait.additive
                         )
     elif dataset.type == "Geno":
         return dict(name=trait.name,
+                    view=trait.view,
                     dataset=dataset.name,
                     dataset_name=dataset.shortname,
                     location=trait.location_repr
                     )
     elif dataset.name == "Temp":
         return dict(name=trait.name,
+                    view=trait.view,
                     dataset="Temp",
                     dataset_name="Temp")
     else:
diff --git a/wqflask/wqflask/correlation/correlation_gn3_api.py b/wqflask/wqflask/correlation/correlation_gn3_api.py
index 30c05f03..aea91220 100644
--- a/wqflask/wqflask/correlation/correlation_gn3_api.py
+++ b/wqflask/wqflask/correlation/correlation_gn3_api.py
@@ -165,8 +165,14 @@ def fetch_sample_data(start_vars, this_trait, this_dataset, target_dataset):
     return (this_trait_data, results)
 
 
-def compute_correlation(start_vars, method="pearson"):
-    """compute correlation for to call gn3  api"""
+def compute_correlation(start_vars, method="pearson", compute_all=False):
+    """Compute correlations using GN3 API
+
+    Keyword arguments:
+    start_vars -- All input from form; includes things like the trait/dataset names
+    method -- Correlation method to be used (pearson, spearman, or bicor)
+    compute_all -- Include sample, tissue, and literature correlations (when applicable)
+    """
     # pylint: disable-msg=too-many-locals
 
     corr_type = start_vars['corr_type']
@@ -220,11 +226,9 @@ def compute_correlation(start_vars, method="pearson"):
 
     correlation_results = correlation_results[0:corr_return_results]
 
-    compute_all = True  # later to  be passed as argument
-
     if (compute_all):
-
-        correlation_results = compute_corr_for_top_results(correlation_results,
+        correlation_results = compute_corr_for_top_results(start_vars,
+                                                           correlation_results,
                                                            this_trait,
                                                            this_dataset,
                                                            target_dataset,
@@ -238,7 +242,8 @@ def compute_correlation(start_vars, method="pearson"):
     return correlation_data
 
 
-def compute_corr_for_top_results(correlation_results,
+def compute_corr_for_top_results(start_vars,
+                                 correlation_results,
                                  this_trait,
                                  this_dataset,
                                  target_dataset,
@@ -261,8 +266,12 @@ def compute_corr_for_top_results(correlation_results,
             correlation_results = merge_correlation_results(
                 correlation_results, lit_result)
 
-    if corr_type != "sample":
-        pass
+    if corr_type != "sample" and this_dataset.type == "ProbeSet" and target_dataset.type == "ProbeSet":
+        sample_result = sample_for_trait_lists(
+            correlation_results, target_dataset, this_trait, this_dataset, start_vars)
+        if sample_result:
+            correlation_results = merge_correlation_results(
+                correlation_results, sample_result)
 
     return correlation_results
 
diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index f1cf3733..bebef9e7 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -18,491 +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
+def set_template_vars(start_vars, correlation_data):
+    corr_type = start_vars['corr_type']
+    corr_method = start_vars['corr_sample_method']
 
-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
+    this_dataset_ob = create_dataset(dataset_name=start_vars['dataset'])
+    this_trait = create_trait(dataset=this_dataset_ob,
+                              name=start_vars['trait_id'])
 
-from flask import g
+    correlation_data['this_trait'] = jsonable(this_trait, this_dataset_ob)
+    correlation_data['this_dataset'] = this_dataset_ob.as_dict()
 
-logger = utility.logger.getLogger(__name__)
+    target_dataset_ob = create_dataset(correlation_data['target_dataset'])
+    correlation_data['target_dataset'] = target_dataset_ob.as_dict()
 
-METHOD_LIT = "3"
-METHOD_TISSUE_PEARSON = "4"
-METHOD_TISSUE_RANK = "5"
+    table_json = correlation_json_for_table(correlation_data,
+                                            correlation_data['this_trait'],
+                                            correlation_data['this_dataset'],
+                                            target_dataset_ob)
 
-TISSUE_METHODS = [METHOD_TISSUE_PEARSON, METHOD_TISSUE_RANK]
+    correlation_data['table_json'] = table_json
 
-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 = []
-
-            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
-            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(
-                    )]
-
-                    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])))
-
-            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)
-
-        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 target_dataset_ob.type == "ProbeSet":
+        filter_cols = [7, 6]
+    elif target_dataset_ob.type == "Publish":
+        filter_cols = [6, 0]
+    else:
+        filter_cols = [4, 0]
 
-            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()
+    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)
 
-                if result:
-                    lit_corr = result.value
-                    trait.lit_corr = lit_corr
-                else:
-                    trait.lit_corr = 0
-            else:
-                trait.lit_corr = 0
+    return correlation_data
 
-    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)
+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
 
-        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)
+    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 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 = ()
-
-        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)
-
-
-def do_bicor(this_trait_vals, target_trait_vals):
-    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.FloatVector(this_trait_vals)
-    target_vals = ro.FloatVector(target_trait_vals)
+                    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['location']
 
-    the_r, the_p, _fisher_transform, _the_t, _n_obs = [
-        numpy.asarray(x) for x in r_bicor(x=this_vals, y=target_vals)]
+        results_list.append(results_dict)
 
-    return the_r, the_p
+    return json.dumps(results_list)
 
 
 def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_api=False):
@@ -598,6 +252,25 @@ 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":
diff --git a/wqflask/wqflask/templates/correlation_page.html b/wqflask/wqflask/templates/correlation_page.html
index 4cad2749..f66eb4bd 100644
--- a/wqflask/wqflask/templates/correlation_page.html
+++ b/wqflask/wqflask/templates/correlation_page.html
@@ -17,9 +17,9 @@
             <hr style="height: 1px; background-color: #A9A9A9;">
         </div>
         <div style="max-width: 100%;">
-          <p>Values of record {{ this_trait.name }} in the <a href="http://genenetwork.org/webqtl/main.py?FormID=sharinginfo&{% if dataset.accession_id != 'None' %}GN_AccessionId={{ dataset.accession_id }}{% else %}InfoPageName={{ dataset.name }}{% endif %}">{{ dataset.fullname }}</a>
+          <p>Values of record {{ this_trait.name }} in the <a href="http://genenetwork.org/webqtl/main.py?FormID=sharinginfo&{% if this_dataset.accession_id != 'None' %}GN_AccessionId={{ this_dataset.accession_id }}{% else %}InfoPageName={{ this_dataset.name }}{% endif %}">{{ this_dataset.fullname }}</a>
           dataset were compared to all records in the <a href="http://genenetwork.org/webqtl/main.py?FormID=sharinginfo&{% if target_dataset.accession_id != 'None' %}GN_AccessionId={{ target_dataset.accession_id }}{% else %}InfoPageName={{ target_dataset.name }}{% endif %}">{{ target_dataset.fullname }}</a>
-          dataset. The top {{ return_number }} correlations ranked by the {{ formatted_corr_type }} are displayed.
+          dataset. The top {{ return_results }} correlations ranked by the {{ formatted_corr_type }} are displayed.
           You can resort this list by clicking the headers. Select the Record ID to open the trait data
           and analysis page.
           </p>
@@ -30,7 +30,7 @@
             <input type="hidden" name="form_url" value="" />
             <input type="hidden" name="trait_list" id="trait_list" value= "
             {% for this_trait in trait_list %}
-                {{ this_trait.name }}:{{ this_trait.dataset }},
+                {{ this_trait }}:{{ this_dataset.name }},
             {% endfor %}" >
             {% include 'tool_buttons.html' %}
           </form>
@@ -43,7 +43,7 @@
                 <button class="btn btn-success" id="add" type="button" disabled><span class="glyphicon glyphicon-plus-sign"></span> Add</button>
                 <input type="hidden" name="database_name" id="database_name" value="None">
                 <input type="hidden" name="export_data" id="export_data" value="">
-                <input type="hidden" name="file_name" id="file_name" value="{{ this_trait.name }}_{{ dataset.name }}_correlation">
+                <input type="hidden" name="file_name" id="file_name" value="{{ this_trait.name }}_{{ this_dataset.name }}_correlation">
                 <input type="text" id="searchbox" class="form-control" style="width: 200px; display: inline;" placeholder="Search Table For ...">
                 <input type="text" id="select_top" class="form-control" style="width: 200px; display: inline;" placeholder="Select Top ...">
                 <button class="btn btn-default" id="deselect_all" type="button"><span class="glyphicon glyphicon-remove"></span> Deselect</button>
@@ -146,7 +146,7 @@
     
 
     <script type="text/javascript" charset="utf-8">
-        var table_json = {{ json_results | safe }}
+        var table_json = {{ table_json | safe }}
     </script>
 
     <script type="text/javascript" charset="utf-8">
@@ -313,7 +313,7 @@
                       'orderSequence': [ "desc", "asc"],
                       'render': function(data, type, row, meta) {
                         if (data.sample_r != "N/A") {
-                          return "<a target\"_blank\" href=\"corr_scatter_plot?method={% if corr_method == 'spearman' %}spearman{% else %}pearson{% endif %}&dataset_1={% if dataset.name == 'Temp' %}Temp_{{ dataset.group.name }}{% else %}{{ dataset.name }}{% endif %}&dataset_2=" + data.dataset + "&trait_1={{ this_trait.name }}&trait_2=" + data.trait_id + "\">" + data.sample_r + "</a>"
+                          return "<a target\"_blank\" href=\"corr_scatter_plot?method={% if corr_method == 'spearman' %}spearman{% else %}pearson{% endif %}&dataset_1={% if this_dataset.name == 'Temp' %}Temp_{{ this_dataset.group }}{% else %}{{ this_dataset.name }}{% endif %}&dataset_2=" + data.dataset + "&trait_1={{ this_trait.name }}&trait_2=" + data.trait_id + "\">" + data.sample_r + "</a>"
                         } else {
                           return data.sample_r
                         }
@@ -441,7 +441,7 @@
                       'orderSequence': [ "desc", "asc"],
                       'render': function(data, type, row, meta) {
                         if (data.sample_r != "N/A") {
-                          return "<a target\"_blank\" href=\"corr_scatter_plot?method={% if corr_method == 'spearman' %}spearman{% else %}pearson{% endif %}&dataset_1={% if dataset.name == 'Temp' %}Temp_{{ dataset.group.name }}{% else %}{{ dataset.name }}{% endif %}&dataset_2=" + data.dataset + "&trait_1={{ this_trait.name }}&trait_2=" + data.trait_id + "\">" + data.sample_r + "</a>"
+                          return "<a target\"_blank\" href=\"corr_scatter_plot?method={% if corr_method == 'spearman' %}spearman{% else %}pearson{% endif %}&dataset_1={% if this_dataset.name== 'Temp' %}Temp_{{ this_dataset.group }}{% else %}{{ this_dataset.name }}{% endif %}&dataset_2=" + data.dataset + "&trait_1={{ this_trait.name }}&trait_2=" + data.trait_id + "\">" + data.sample_r + "</a>"
                         } else {
                           return data.sample_r
                         }
@@ -495,7 +495,7 @@
                       'orderSequence': [ "desc", "asc"],
                       'render': function(data, type, row, meta) {
                         if (data.sample_r != "N/A") {
-                          return "<a target\"_blank\" href=\"corr_scatter_plot?method={% if corr_method == 'spearman' %}spearman{% else %}pearson{% endif %}&dataset_1={% if dataset.name == 'Temp' %}Temp_{{ dataset.group.name }}{% else %}{{ dataset.name }}{% endif %}&dataset_2=" + data.dataset + "&trait_1={{ this_trait.name }}&trait_2=" + data.trait_id + "\">" + data.sample_r + "</a>"
+                          return "<a target\"_blank\" href=\"corr_scatter_plot?method={% if corr_method == 'spearman' %}spearman{% else %}pearson{% endif %}&dataset_1={% if this_dataset.name == 'Temp' %}Temp_{{ this_dataset.group }}{% else %}{{ this_dataset.name }}{% endif %}&dataset_2=" + data.dataset + "&trait_1={{ this_trait.name }}&trait_2=" + data.trait_id + "\">" + data.sample_r + "</a>"
                         } else {
                           return data.sample_r
                         }
diff --git a/wqflask/wqflask/templates/show_trait_calculate_correlations.html b/wqflask/wqflask/templates/show_trait_calculate_correlations.html
index 59f9b47c..16a819fa 100644
--- a/wqflask/wqflask/templates/show_trait_calculate_correlations.html
+++ b/wqflask/wqflask/templates/show_trait_calculate_correlations.html
@@ -7,8 +7,10 @@
             <div class="col-xs-3 controls">
                 <select name="corr_type" class="form-control">
                     <option value="sample">Sample r</option>
+                    {% if dataset.type == 'ProbeSet' %}
                     <option value="lit">Literature r</option>
                     <option value="tissue">Tissue r</option>
+                    {% endif %}
                 </select>
             </div>
         </div>
diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py
index b9181368..da427bfe 100644
--- a/wqflask/wqflask/views.py
+++ b/wqflask/wqflask/views.py
@@ -65,7 +65,7 @@ from wqflask.comparison_bar_chart import comparison_bar_chart
 from wqflask.marker_regression import run_mapping
 from wqflask.marker_regression import display_mapping_results
 from wqflask.network_graph import network_graph
-from wqflask.correlation import show_corr_results
+from wqflask.correlation.show_corr_results import set_template_vars
 from wqflask.correlation.correlation_gn3_api import compute_correlation
 from wqflask.correlation_matrix import show_corr_matrix
 from wqflask.correlation import corr_scatter_plot
@@ -1082,15 +1082,9 @@ def network_graph_page():
 
 @app.route("/corr_compute", methods=('POST',))
 def corr_compute_page():
-    logger.info("In corr_compute, request.form is:", pf(request.form))
-    logger.info(request.url)
-    template_vars = show_corr_results.CorrelationResults(request.form)
-    return render_template("correlation_page.html", **template_vars.__dict__)
-
-    # to test/disable the new  correlation api uncomment these lines
-
-    # correlation_results = compute_correlation(request.form)
-    # return render_template("test_correlation_page.html", correlation_results=correlation_results)
+    correlation_results = compute_correlation(request.form, compute_all=True)
+    correlation_results = set_template_vars(request.form, correlation_results)
+    return render_template("correlation_page.html", **correlation_results)
 
 
 @app.route("/test_corr_compute", methods=["POST"])