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-rw-r--r--wqflask/wqflask/correlation/show_corr_results.py30
1 files 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):