diff options
| -rw-r--r-- | wqflask/wqflask/correlation/show_corr_results.py | 30 |
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): |