def correlationMatrix(outf, inf, method = pcorrelation):
"""Takes a tab file and cross-correlates the columns"""
outData = dict()
inData = mData.rCRSData(inf)
inCols = inData.keys()
inRows = inData[inCols[0]].keys()
for i in inCols:
outData[i] = dict()
outData[i][i] = 1.0
for i in range(len(inCols)-1):
list1 = []
for k in inRows:
list1.append(inData[inCols[i]][k])
for j in range(i+1, len(inCols)):
list2 = []
for k in inRows:
list2.append(inData[inCols[j]][k])
value = method(list1, list2)
outData[inCols[i]][inCols[j]] = value
outData[inCols[j]][inCols[i]] = value
mData.wCRSData(outf, outData)
def correlationMatrix(outf, inf, method=pcorrelation):
"""Takes a tab file and cross-correlates the columns"""
outData = dict()
inData = mData.rCRSData(inf)
inCols = inData.keys()
inRows = inData[inCols[0]].keys()
for i in inCols:
outData[i] = dict()
outData[i][i] = 1.0
for i in range(len(inCols) - 1):
list1 = []
for k in inRows:
list1.append(inData[inCols[i]][k])
for j in range(i + 1, len(inCols)):
list2 = []
for k in inRows:
list2.append(inData[inCols[j]][k])
value = method(list1, list2)
outData[inCols[i]][inCols[j]] = value
outData[inCols[j]][inCols[i]] = value
mData.wCRSData(outf, outData)
)
f.close()
## summarize pathways
if iplMatrix is not None:
pathwayVals = {}
pathwayScores = {}
(iplData, iplSamples, iplFeatures) = mData.rCRSData(iplMatrix, retFeatures=True)
f = open(re.split("/", sifFile)[-2] + ".members.output", "r")
for line in f:
line = line.rstrip("\n\r")
pline = re.split("\t", line)
if pline[1] in statsMap:
if pline[1] not in pathwayVals:
pathwayVals[pline[1]] = {}
for sample in iplSamples:
pathwayVals[pline[1]][sample] = []
if pline[0] in iplFeatures:
for sample in iplSamples:
pathwayVals[pline[1]][sample].append(abs(float(iplData[sample][pline[0]])))
f.close()
for pathwayName in pathwayVals.keys():
pathwayScores[pathwayName] = {}
for sample in iplSamples:
pathwayScores[pathwayName][sample] = mData.mean(pathwayVals[pathwayName][sample])
mData.wCRSData(re.split("/", sifFile)[-2] + ".score", pathwayScores)
if __name__ == "__main__":
main(sys.argv[1:])
## summarize pathways
if iplMatrix is not None:
pathwayVals = {}
pathwayScores = {}
(iplData, iplSamples, iplFeatures) = mData.rCRSData(iplMatrix,
retFeatures=True)
f = open(re.split("/", sifFile)[-2] + ".members.output", "r")
for line in f:
line = line.rstrip("\n\r")
pline = re.split("\t", line)
if pline[1] in statsMap:
if pline[1] not in pathwayVals:
pathwayVals[pline[1]] = {}
for sample in iplSamples:
pathwayVals[pline[1]][sample] = []
if pline[0] in iplFeatures:
for sample in iplSamples:
pathwayVals[pline[1]][sample].append(
abs(float(iplData[sample][pline[0]])))
f.close()
for pathwayName in pathwayVals.keys():
pathwayScores[pathwayName] = {}
for sample in iplSamples:
pathwayScores[pathwayName][sample] = mData.mean(
pathwayVals[pathwayName][sample])
mData.wCRSData(re.split("/", sifFile)[-2] + ".score", pathwayScores)
if __name__ == "__main__":
main(sys.argv[1:])
