def calcFDRStats(realCount,ctrlCounts):
FDRs = []
for i in range(len(ctrlCounts)):
ctrl = ctrlCounts[i]
real = realCount
if real == 0:
return (None,None)
else:
fdr = float(ctrl)/real
if fdr > 1:
FDRs.append(1.0)
else:
FDRs.append(fdr)
return mathDefs.stdDv(FDRs,'median')
def calcCtrlMedianStDv(self):
"""
Iterate through the self.ctrlCount data and calculate the MEDIAN and stdDev from the
MEDIAN for each seedType permutation set.
"""
# Make sure we have already run self.countHitsInOrthos()
assert self.matchCounts and self.ctrlCounts, \
'ERROR: It looks like we have not called countHitsInOrthos().'
# Initialize data structure
for seedType in _seedModels:
self.ctrlMedianStd[seedType] = [[None,None],[None,None],[None,None],[None,None]] # SEE self.ctrlStats def in __init__ for format definition
# Iterate through self.ctrlCounts
for seedType in self.ctrlCounts:
# Calc median and medianStDv for each countCategory in self.ctrlCounts[seedType]
for i in range(len(self.ctrlMedianStd[seedType])):
medStDv, median = mathDefs.stdDv([x[i] for x in self.ctrlCounts[seedType]],kind='median',df=1)
self.ctrlMedianStd[seedType][i][0] = median
self.ctrlMedianStd[seedType][i][1] = medStDv
def calcFDRStats(goDictEntry):
realCount = len(goDictEntry[0])
ctrlCounts = [len(x) for x in goDictEntry[1]]
FDRs = []
for i in range(len(ctrlCounts)):
ctrl = ctrlCounts[i]
real = realCount
if real == 0:
return (None)
else:
fdr = float(ctrl)/real
if fdr > 1:
FDRs.append(1.0)
else:
FDRs.append(fdr)
maths = mathDefs.stdDv(FDRs,'median')
maths = list(maths)
return [realCount]+maths
def writeTargetsFdrMedMeth(miRobj_Ca,miRobj_Cb,oFile):
#print 'Processing %s...' % (miRobj_Ca.name)
miRHits_Ca = miRobj_Ca.reportGeneTargetsFdrMedMeth(stdvLimit=stdvsAboveMed,
consFdrThresh=consFdrThreshold,
divide=0)
totReal = [None,set(),set(),set()]
totCtrl = initList(len(miRobj_Ca.ctrlEvents[miRobj_Ca.ctrlEvents.keys()[0]]),[None, set(), set(),set()])
# Calulate combined FDR for miRNA using Ctrl_b data from seedTypes that passed
# the reportGeneTargetsFdrMedMeth() Ctrl_a screen.
# >> Gather and combine data from passed seedTypes:
for oType in range(1,4):
for sType in miRT._seedModels:
if miRHits_Ca[sType][oType]:
trLen_0 = len(totReal[oType])
totReal[oType].update(miRHits_Ca[sType][oType][0])
rUpdtLen = len(miRHits_Ca[sType][oType][0])
trLen_1 = len(totReal[oType])
None
for i in range(len(totCtrl)):
tciLen_0 = len(totCtrl[i][oType])
totCtrl[i][oType].update(miRobj_Cb.ctrlEvents[sType][i][oType])
cUpdtLen = len(miRobj_Cb.ctrlEvents[sType][i][oType])
tciLen_1 = len(totCtrl[i][oType])
None
# >> Calculate separate FDRs for each Ctrl_b group:
totalsData = [None,None,None,None]
for oType in range(1,4):
if totReal[oType] == set():
continue
tempFDRs = []
for i in range(len(totCtrl)):
ctrlVal = len(totCtrl[i][oType])
realVal = len(totReal[oType])
if float(ctrlVal)/realVal >= 1:
tempFDRs.append(1.0)
else:
tempFDRs.append(float(ctrlVal)/realVal)
tLen = len(tempFDRs)
oFDRstdv,oFDRmed = mathDefs.stdDv(tempFDRs,'median')
cons_oFDR = oFDRmed + (stdvsAboveMed*oFDRstdv)
totalsData[oType] = [totReal[oType],oFDRmed,cons_oFDR]
# Write out Totals data:
print miRobj_Cb.name
outFile.write('-- %s --\n' % (miRobj_Cb.name))
for i in range(1,len(totalsData)):
if totalsData[i]:
outFile.write('%s : allPassedSeedsFor_%s : %s : %s : %s Seqs=%s\n' \
%(miRobj_Cb.name,
i,
len(totalsData[i][0]),
totalsData[i][2],
totalsData[i][1],
','.join(sorted([str(x) for x in totalsData[i][0]]))))
# write out passed Seed data
for seedType in sorted(miRHits_Ca):
for i in range(1,len(miRHits_Ca[seedType])):
if miRHits_Ca[seedType][i]:
outFile.write('%s : %s : orthoType_%s : %s : %.2f : %.4f Seqs=%s\n'\
%(miRobj_Ca.name,
seedType,
i,
len(miRHits_Ca[seedType][i][0]),
miRHits_Ca[seedType][i][2],
miRHits_Ca[seedType][i][1],
','.join(sorted([str(x) for x in miRHits_Ca[seedType][i][0]]))))
outFile.flush()
