Most code in webqtlUtil.py has been removed. There is so little left that the remaining could should be migrated to the files that use them at some point.

Some of this code was in the correlation code but not being used, so it wss removed from there as well.

2 files changed, 0 insertions, 86 deletions

diff --git a/wqflask/wqflask/correlation/correlation_functions.py b/wqflask/wqflask/correlation/correlation_functions.py
index 80a0818c..1ee9b558 100644
--- a/wqflask/wqflask/correlation/correlation_functions.py
+++ b/wqflask/wqflask/correlation/correlation_functions.py
@@ -491,62 +491,6 @@ pcor.rec <- function(x,y,z,method="p",na.rm=T){
     return allcorrelations
 
 
-
-#XZ, April 30, 2010: The input primaryTrait and targetTrait are instance of webqtlTrait
-#XZ: The primaryTrait and targetTrait should have executed retrieveData function
-def calZeroOrderCorr(primaryTrait, targetTrait, method='pearson'):
-
-    #primaryTrait.retrieveData()
-
-    #there is no None value in primary_val
-    primary_strain, primary_val, primary_var = primaryTrait.exportInformative()
-
-    #targetTrait.retrieveData()
-
-    #there might be None value in target_val
-    target_val = targetTrait.exportData(primary_strain, type="val")
-
-    R_primary = rpy2.robjects.FloatVector(range(len(primary_val)))
-    for i in range(len(primary_val)):
-        R_primary[i] = primary_val[i]
-
-    N = len(target_val)
-
-    if None in target_val:
-        goodIndex = []
-        for i in range(len(target_val)):
-            if target_val[i] != None:
-                goodIndex.append(i)
-
-        N = len(goodIndex)
-
-        R_primary = rpy2.robjects.FloatVector(range(len(goodIndex)))
-        for i in range(len(goodIndex)):
-            R_primary[i] = primary_val[goodIndex[i]]
-
-        R_target = rpy2.robjects.FloatVector(range(len(goodIndex)))
-        for i in range(len(goodIndex)):
-            R_target[i] = target_val[goodIndex[i]]
-
-    else:
-        R_target = rpy2.robjects.FloatVector(range(len(target_val)))
-        for i in range(len(target_val)):
-            R_target[i] = target_val[i]
-
-    R_corr_test = rpy2.robjects.r['cor.test']
-
-    if method == 'spearman':
-        R_result = R_corr_test(R_primary, R_target, method='spearman')
-    else:
-        R_result = R_corr_test(R_primary, R_target)
-
-    corr_result = []
-    corr_result.append( R_result[3][0] )
-    corr_result.append( N )
-    corr_result.append( R_result[2][0] )
-
-    return corr_result
-
 #####################################################################################
 #Input: primaryValue(list): one list of expression values of one probeSet,
 #       targetValue(list): one list of expression values of one probeSet,
diff --git a/wqflask/wqflask/correlation/show_corr_results.py b/wqflask/wqflask/correlation/show_corr_results.py
index abf9fc89..85a8c0ef 100644
--- a/wqflask/wqflask/correlation/show_corr_results.py
+++ b/wqflask/wqflask/correlation/show_corr_results.py
@@ -555,21 +555,6 @@ class CorrelationResults(object):
 
         self.record_count = len(traits) #ZS: This isn't a good way to get this value, so I need to change it later
 
-        #XZ, 3/31/2010: Theoretically, we should create one function 'comTissueCorr'
-        #to compare each trait by their tissue corr p values.
-        #But because the tissue corr p values are generated by permutation test,
-        #the top ones always have p value 0. So comparing p values actually does nothing.
-        #In addition, for the tissue data in our database, the N is always the same.
-        #So it's safe to compare with tissue corr statistic value.
-        #That's the same as literature corr.
-        #if self.method in [METHOD_LIT, METHOD_TISSUE_PEARSON, METHOD_TISSUE_RANK] and self.gene_id:
-        #    traits.sort(webqtlUtil.cmpLitCorr)
-        #else:
-        #if self.method in TISSUE_METHODS:
-        #    sort(traits, key=lambda A: math.fabs(A.tissue_corr))
-        #elif self.method == METHOD_LIT:
-        #    traits.sort(traits, key=lambda A: math.fabs(A.lit_corr))
-        #else:
         traits = sortTraitCorrelations(traits, self.method)
 
         # Strip to the top N correlations
@@ -1069,21 +1054,6 @@ class CorrelationResults(object):
 
         return (symbolCorrDict, symbolPvalueDict)
 
-
-    def correlate(self):
-        self.correlation_data = collections.defaultdict(list)
-        for trait, values in self.target_dataset.trait_data.iteritems():
-            values_1 = []
-            values_2 = []
-            for index,sample in enumerate(self.target_dataset.samplelist):
-                target_value = values[index]
-                if sample in self.sample_data.keys():
-                    this_value = self.sample_data[sample]
-                    values_1.append(this_value)
-                    values_2.append(target_value)
-            correlation = calCorrelation(values_1, values_2)
-            self.correlation_data[trait] = correlation
-
     def getFileName(self, target_db_name):  ### dcrowell  August 2008
         """Returns the name of the reference database file with which correlations are calculated.
         Takes argument cursor which is a cursor object of any instance of a subclass of templatePage