From fafce2f44087edf51756f0118054d1e3aa654273 Mon Sep 17 00:00:00 2001
From: zsloan
Date: Fri, 18 Jun 2021 19:21:11 +0000
Subject: Re-enable bicor for correlations and fix issue where ro.Vector needed
 to be changed to ro.FloatVector

---
 wqflask/wqflask/correlation/show_corr_results.py | 30 ++++++++++++------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index 2f3df67a..f1cf3733 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -22,7 +22,7 @@ import collections
 import json
 import scipy
 import numpy
-# import rpy2.robjects as ro                    # R Objects
+import rpy2.robjects as ro                    # R Objects
 import utility.logger
 import utility.webqtlUtil
 
@@ -459,9 +459,9 @@ class CorrelationResults:
 
         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 == '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)
@@ -487,22 +487,22 @@ class CorrelationResults:
                     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
+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_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)
+    this_vals = ro.FloatVector(this_trait_vals)
+    target_vals = ro.FloatVector(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
+    return the_r, the_p
 
 
 def generate_corr_json(corr_results, this_trait, dataset, target_dataset, for_api=False):
-- 
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