def testOverlaps(dataFN, oFF):
dataNX = Nexus(dataFN, oFF)
dataNX.load(['tcc'])
#check for overlaps
overlappingIDs = set()
chrom_strand_range = {}
while dataNX.nextID():
chrom, strand, start, end = bioLibCG.tccSplit(dataNX.tcc)
#check if overlap
chrom_strand_range.setdefault(chrom, {}).setdefault(strand, set())
overlap = False
for i in range(start, end + 1):
if i in chrom_strand_range[chrom][strand]:
overlap = True
break
#tag or add these coordinates
if overlap:
overlappingIDs.add(dataNX.id)
else:
for i in range(start, end + 1):
chrom_strand_range[chrom][strand].add(i)
print "THESE OVERLAP", overlappingIDs
def testOverlaps(dataFN, oFF):
dataNX = Nexus(dataFN, oFF)
dataNX.load(['tcc'])
#check for overlaps
overlappingIDs = set()
chrom_strand_range = {}
while dataNX.nextID():
chrom, strand, start, end = bioLibCG.tccSplit(dataNX.tcc)
#check if overlap
chrom_strand_range.setdefault(chrom, {}).setdefault(strand, set())
overlap = False
for i in range(start, end + 1):
if i in chrom_strand_range[chrom][strand]:
overlap = True
break
#tag or add these coordinates
if overlap:
overlappingIDs.add(dataNX.id)
else:
for i in range(start, end + 1):
chrom_strand_range[chrom][strand].add(i)
print "THESE OVERLAP", overlappingIDs
def check_ORNA_in_ago(oFN, oFF, agoFN, clippingAmount = 1):
NX = Nexus(oFN, oFF)
NX.load(['sequence', 'geneNames'])
#make truncated sequences
id_sequence = NX.createMap('id', 'sequence')
if clippingAmount > 0:
id_sequence = dict( (i, j[clippingAmount:-clippingAmount]) for i,j in id_sequence.items())
#get fastq sequences
agoF = open(agoFN, 'r')
agoSeqs = []
while True:
fPacket = nextFilePacket(agoF, 4)
if not fPacket: break
agoSeqs.append(fPacket[1])
agoF.close()
#count for each oRNA
id_count = {}
for id, seq in id_sequence.items():
for agoSeq in agoSeqs:
if seq in agoSeq:
id_count[id] = id_count.get(id, 0) + 1
#out
totalCount = 0
for id, count in id_count.items():
NX.id = id
print '%s\t%s\t%s' % (id, count, NX.geneNames)
totalCount += count
print totalCount
def testMap(fN, fF):
NX = Nexus(fN, fF)
NX.load(['geneName', 'numReads', 'otherIDs'])
geneName_numReads = NX.createMap('otherIDs', 'geneName', False) #not 1to1
for k,v in geneName_numReads.iteritems():
print k, v[:5]
return
def updateGeneName(dFN,
fFN,
wigDir,
chrom,
strand,
prefix,
switchStrand=False):
NX = Nexus(dFN, fFN)
NX.load(['geneNames', 'tcc'])
if switchStrand:
strand = -strand
strand = str(strand)
coord_gName = cgWig.loadSingleWigTranscript(wigDir, chrom, strand, prefix)
while NX.nextID():
chrom, strand, start, end = bioLibCG.tccSplit(NX.tcc)
overlappingGenes = coord_gName.get(start, ".")
if overlappingGenes == "NONE":
NX.geneNames = []
else:
NX.geneNames = overlappingGenes.split(',')
NX.save()
def testNX(fN, fF):
NX = Nexus(fN, fF, 'geneName numReads isCoding otherIDs')
print 'START LOOPING'
for gene in NX:
gene.isCoding = True
gene.otherIDs = range(10)
gene.geneName = "testAuto"
gene.numReads = 300
NX.save()
def getTotalSpots(allFN, formatFN):
NX = Nexus(allFN, formatFN)
NX.load(['numNSpots', 'numReads'])
totalSpots = 0
totalReads = 0
while NX.nextID():
totalSpots += NX.numNSpots
totalReads += NX.numReads
print 'spotsNSpots', totalSpots
print 'numReads', totalReads
def updateContext(fN, fF, wigDir, chrom, strand, switchStrand = False):
NX = Nexus(fN, fF)
NX.load(['tcc', 'context'])
if switchStrand:
strand = str(-int(strand))
else:
strand = str(strand)
print 'loading wig'
coord_contexts = cgWig.loadSingleWigContext(wigDir, chrom, strand, 'context')
print 'done loading'
ds = bioLibCG.dominantSpotter(['C_EXON', 'C_3UTR', 'C_5UTR', 'NC_EXON', 'NC_3UTR', 'NC_5UTR', 'C_INTRON', 'NC_INTRON', 'INTER'])
while NX.nextID():
oChrom, oStrand, start, end = bioLibCG.tccSplit(NX.tcc)
#deg wigs is AS to actual clipping site
if switchStrand:
oStrand = str(-int(strand))
else:
oStrand = str(oStrand)
if oChrom == chrom and oStrand == strand:
contexts = coord_contexts.get(start, 'INTER').split(',')
NX.context = ds.spotItem(contexts)
NX.save()
def updateSNR(oFN, oFF):
NX = Nexus(oFN, oFF)
NX.load(['avgNumSimSS', 'numUFBS', 'snr'])
while NX.nextID():
try:
NX.snr = NX.numUFBS / NX.avgNumSimSS
except ZeroDivisionError:
NX.snr = NX.numUFBS / 0.01
NX.save()
def updateSimilarSiblings(oFN, oFF, frameLength):
dataNX = Nexus(oFN, oFF)
dataNX.load(['sequence', 'siblingSet'])
oID_sequence = dataNX.createMap('id', 'sequence')
consolidatedSets = getSimilarORNASets(oID_sequence, frameLength)
for cSet in consolidatedSets:
for oID in cSet:
dataNX.id = oID
dataNX.siblingSet = list(cSet)
dataNX.save()
def updateSNR(oFN, oFF):
NX = Nexus(oFN, oFF)
NX.load(['avgNumSimSS', 'numUFBS', 'snr'])
while NX.nextID():
try:
NX.snr = NX.numUFBS/NX.avgNumSimSS
except ZeroDivisionError:
NX.snr = NX.numUFBS/0.01
NX.save()
def updateScores(fN, fFN):
NX = Nexus(fN, fFN)
NX.load(['numNormMatches', 'numGUs', 'numMismatches', 'numQGaps', 'numRGaps', 'numExtensionsQ','numExtensionsR', 'score'])
while NX.nextID():
NX.score = calculateAlignmentScore(NX.numNormMatches, NX.numGUs, NX.numMismatches, NX.numQGaps, NX.numRGaps, NX.numExtensionsQ, NX.numExtensionsR)
NX.save()
def filterCenterProperties(fN, fFN):
NX = Nexus(fN, fFN)
NX.load(['query', 'reference', 'qStart', 'qEnd', 'rStart', 'rEnd', 'qLen', 'rLen', 'sigMask', 'centerPass', 'mismatchPass'])
while NX.nextID():
qRange = [NX.qStart, NX.qEnd]
rRange = [NX.rStart, NX.rEnd]
NX.mismatchPass = checkMismatchCenter(NX.query, NX.reference, NX.qLen, NX.rLen, qRange, rRange, NX.sigMask)
NX.centerPass = checkPeakCenter(NX.query, NX.reference, NX.qLen, NX.rLen, qRange, rRange)
NX.save()
def updateSimilarSiblings(oFN, oFF, frameLength):
dataNX = Nexus(oFN, oFF)
dataNX.load(['sequence', 'siblingSet'])
oID_sequence = dataNX.createMap('id', 'sequence')
consolidatedSets = getSimilarORNASets(oID_sequence, frameLength)
for cSet in consolidatedSets:
for oID in cSet:
dataNX.id = oID
dataNX.siblingSet = list(cSet)
dataNX.save()
def testAutoLoad(fN, ff):
NX = Nexus(fN, ff)
print 'START LOOPING'
while NX.nextID():
NX.isCoding = True
NX.otherIDs = range(10)
NX.geneName = "testAuto"
NX.numReads = 300
NX.save()
def check_ORNA_in_ago(oFN, oFF, agoFN, clippingAmount=1):
NX = Nexus(oFN, oFF)
NX.load(['sequence', 'geneNames'])
#make truncated sequences
id_sequence = NX.createMap('id', 'sequence')
if clippingAmount > 0:
id_sequence = dict((i, j[clippingAmount:-clippingAmount])
for i, j in id_sequence.items())
#get fastq sequences
agoF = open(agoFN, 'r')
agoSeqs = []
while True:
fPacket = nextFilePacket(agoF, 4)
if not fPacket: break
agoSeqs.append(fPacket[1])
agoF.close()
#count for each oRNA
id_count = {}
for id, seq in id_sequence.items():
for agoSeq in agoSeqs:
if seq in agoSeq:
id_count[id] = id_count.get(id, 0) + 1
#out
totalCount = 0
for id, count in id_count.items():
NX.id = id
print '%s\t%s\t%s' % (id, count, NX.geneNames)
totalCount += count
print totalCount
def collectData(fN, fFN, outFN, logHeight = False):
'''x(pHeight) v. y(-log10(pval)). 0.0 is 1.0e-100'''
NX = Nexus(fN, fFN)
NX.load(['eLevel', 'pValBin'])
f = open(outFN, 'w')
while NX.nextID():
x = math.log(NX.eLevel, 10) if logHeight else NX.eLevel
pVal = NX.pValBin
if pVal < 0: continue
pVal = pVal if (pVal != 0.0) else float("1.0e-100")
try:
y = -math.log(pVal, 10)
except ValueError:
print x, pVal
return
f.write('%s\t%s\n' % (x,y))
f.close()
def testConsolidation(oFN, oFF, frameLength):
dataNX = Nexus(oFN, oFF)
dataNX.load(['sequence'])
oID_sequence = dataNX.createMap('id', 'sequence')
consolidatedSets = getSimilarORNASets(oID_sequence, frameLength)
#check if all oIDs are in set
allConsolidatedIDs = set()
[allConsolidatedIDs.add(x) for theSet in consolidatedSets for x in theSet]
oIDsSet = set(oID_sequence.keys())
print "DIFFERENCE"
print oIDsSet.symmetric_difference(allConsolidatedIDs)
#check Duplicates
#print out sets to verify that they work
for oIDSet in consolidatedSets:
print
print oIDSet
for oID in oIDSet:
print oID, oID_sequence[oID]
def cleanForSNR(dataFN, oFF):
dataNX = Nexus(dataFN, oFF)
dataNX.load(['numUniqueSims', 'numUFBS', 'snrClean', 'siblingSet'])
id_numUFBS = dataNX.createMap('id', 'numUFBS')
id_siblingSet = dataNX.createMap('id', 'siblingSet')
unusedSiblings = []
for id, siblingSet in id_siblingSet.iteritems():
if len(siblingSet) == 1: continue #NOTE: oRNA IDs are in their own sibling set
numUFBS__id = [(id_numUFBS[x], x) for x in siblingSet]
numUFBS__id.sort()
numUFBS__id.pop() #take last one (one we're keeping) out of list
unusedIDs = [x[1] for x in numUFBS__id]
unusedSiblings.extend(unusedIDs)
#tag unclean oRNA
while dataNX.nextID():
if (dataNX.id in unusedSiblings) or (dataNX.numUniqueSims < 10):
dataNX.snrClean = False
else:
dataNX.snrClean = True
dataNX.save()
def testConsolidation(oFN, oFF, frameLength):
dataNX = Nexus(oFN, oFF)
dataNX.load(['sequence'])
oID_sequence = dataNX.createMap('id', 'sequence')
consolidatedSets = getSimilarORNASets(oID_sequence, frameLength)
#check if all oIDs are in set
allConsolidatedIDs = set()
[allConsolidatedIDs.add(x) for theSet in consolidatedSets for x in theSet]
oIDsSet = set(oID_sequence.keys())
print "DIFFERENCE"
print oIDsSet.symmetric_difference(allConsolidatedIDs)
#check Duplicates
#print out sets to verify that they work
for oIDSet in consolidatedSets:
print
print oIDSet
for oID in oIDSet:
print oID, oID_sequence[oID]
def updateSequence(oFN, oFF, extend, assembly):
NX = Nexus(oFN, oFF)
NX.load(['sequence', 'tcc'])
gf = GenomeFetch.GenomeFetch(assembly)
while NX.nextID():
chrom, strand, start, end = bioLibCG.tccSplit(NX.tcc)
start, end = start - extend, end + extend
newTcc = bioLibCG.makeTcc(chrom, strand, start, end)
NX.sequence = gf.getSequence(newTcc)
NX.save()
def updateELevel2(dFN, dForm, wigDir):
'''Dont need to do it by chromosome because it is small enough'''
'''Also dont need to flip the strand because the wig is opposite as well'''
NX = Nexus(dFN, dForm)
NX.load(['tcc', 'eLevel'])
wigDict = cgWig.loadWigDictFloat(wigDir)
while NX.nextID():
coord_value = cgWig.getExpressionProfile(NX.tcc, wigDict)
NX.eLevel = max(coord_value.values())
NX.save()
def updateScores(fN, fFN):
NX = Nexus(fN, fFN)
NX.load([
'numNormMatches', 'numGUs', 'numMismatches', 'numQGaps', 'numRGaps',
'numExtensionsQ', 'numExtensionsR', 'score'
])
while NX.nextID():
NX.score = calculateAlignmentScore(NX.numNormMatches, NX.numGUs,
NX.numMismatches, NX.numQGaps,
NX.numRGaps, NX.numExtensionsQ,
NX.numExtensionsR)
NX.save()
def filterCenterProperties(fN, fFN):
NX = Nexus(fN, fFN)
NX.load([
'query', 'reference', 'qStart', 'qEnd', 'rStart', 'rEnd', 'qLen',
'rLen', 'sigMask', 'centerPass', 'mismatchPass'
])
while NX.nextID():
qRange = [NX.qStart, NX.qEnd]
rRange = [NX.rStart, NX.rEnd]
NX.mismatchPass = checkMismatchCenter(NX.query, NX.reference, NX.qLen,
NX.rLen, qRange, rRange,
NX.sigMask)
NX.centerPass = checkPeakCenter(NX.query, NX.reference, NX.qLen,
NX.rLen, qRange, rRange)
NX.save()
def updateSimSeqsForUnique(oFN, oFF, seqFN):
NX = Nexus(oFN, oFF)
NX.load(['sequence'])
id_seq = {}
f = open(seqFN, 'r')
for line in f:
ls = line.strip().split('\t')
id_seq[int(ls[0])] = ls[1]
f.close()
while NX.nextID():
NX.sequence = id_seq.get(NX.id, '.')
NX.save()
def calculateTotalSNR(dataFN, oFF, mm, iSNRCutoff):
mm = str(mm)
dataNX = Nexus(dataFN, oFF)
dataNX.load(['snr', 'numUFBS', 'numUniqueSims', 'totalNumUFBSSim'])
#Check SNR Cutoffs
dataIDs = set()
lowSNRORNA = set()
while dataNX.nextID():
dataIDs.add(dataNX.id)
if dataNX.snr < iSNRCutoff:
lowSNRORNA.add(dataNX.id)
#NOTE sum of avgs != total avg
#get total numUFBS for data and simulation total,num unique sims
totalUFBSData = 0.0
totalUFBSSim = 0.0
totalPassingORNA = 0
totalUniqueSims = 0
while dataNX.nextID():
if (dataNX.id in lowSNRORNA): continue
totalUFBSData += dataNX.numUFBS
totalPassingORNA += 1
totalUFBSSim += dataNX.totalNumUFBSSim
totalUniqueSims += dataNX.numUniqueSims
totalAvgSimUFBS, totalAvgDataUFBS = 0.0, 0.0
try:
totalAvgSimUFBS = totalUFBSSim / float(totalUniqueSims)
totalAvgDataUFBS = totalUFBSData / float(totalPassingORNA)
totalSNR = totalAvgDataUFBS/totalAvgSimUFBS
oS = [str(x) for x in [mm, iSNRCutoff, totalUFBSData, totalAvgSimUFBS, totalSNR, totalPassingORNA, float(totalUFBSData)/totalPassingORNA]]
except ZeroDivisionError:
oS = [str(x) for x in [mm, iSNRCutoff, totalUFBSData, totalAvgSimUFBS, "NA", totalPassingORNA, "NA"]]
print '\t'.join(oS)
def updateRepeatStatus(fN, fF, wigDir, chrom, strand):
#load oRNAs
NX = Nexus(fN, fF)
NX.load(['repeat', 'tcc'])
#load wig file for chrom, strand
coord_value = cgWig.loadSingleWig(wigDir, chrom, strand, 'REPEAT')
while NX.nextID():
oChrom, oStrand, start, end = bioLibCG.tccSplit(NX.tcc)
if oChrom != chrom or oStrand != strand: continue
NX.repeat = False
for i in range(start, end + 1):
if i in coord_value:
NX.repeat = True
break
NX.save()
def updateAdjustedMismatches(fN, fF, guValue = .5, otherValue = 1.0):
NX = Nexus(fN, fF)
NX.load(['sigMask', 'adjustedNumMismatches'])
while NX.nextID():
mask = NX.sigMask
numGU = mask.count('G')
numGapAndMM = mask.count('X')
NX.adjustedNumMismatches = (numGU * guValue) + (numGapAndMM * otherValue)
NX.save()
def updateSequence(oFN, oFF, extend, assembly):
NX = Nexus(oFN, oFF)
NX.load(['sequence', 'tcc'])
gf = GenomeFetch.GenomeFetch(assembly)
while NX.nextID():
chrom, strand, start, end = bioLibCG.tccSplit(NX.tcc)
start, end = start - extend, end + extend
newTcc = bioLibCG.makeTcc(chrom, strand, start, end)
NX.sequence = gf.getSequence(newTcc)
NX.save()
def testQuickLoad(fN):
ff = ['1 geneName string .',
'3 otherIDs intList .',
'4 isCoding bool F'
]
NX = Nexus(fN, ff)
while NX.nextID():
NX.isCoding = True
NX.otherIDs = range(18)
NX.geneName = "testAuto"
NX.save()
def updateELevel2(dFN, dForm, wigDir):
'''Dont need to do it by chromosome because it is small enough'''
'''Also dont need to flip the strand because the wig is opposite as well'''
NX = Nexus(dFN, dForm)
NX.load(['tcc', 'eLevel'])
wigDict = cgWig.loadWigDictFloat(wigDir)
while NX.nextID():
coord_value = cgWig.getExpressionProfile(NX.tcc, wigDict)
NX.eLevel = max(coord_value.values())
NX.save()
def pickBestAlignment(fN, fFN):
NX = Nexus(fN, fFN)
NX.load(['sID', 'dID', 'score', 'best'])
#find best id
pair_score = {} #pair : score
pair_highID = {}
while NX.nextID():
pair = '%s_%s' % (NX.sID, NX.dID)
score = NX.score
if score > pair_score.get(pair, 0.0):
pair_score[pair] = score
pair_highID[pair] = NX.id
# update best id
bestIDs = set(pair_highID.values())
while NX.nextID():
if NX.id in bestIDs:
NX.best = True
NX.save()
def updateSimSeqsForUnique(oFN, oFF, seqFN):
NX = Nexus(oFN, oFF)
NX.load(['sequence'])
id_seq = {}
f = open(seqFN, 'r')
for line in f:
ls = line.strip().split('\t')
id_seq[int(ls[0])] = ls[1]
f.close()
while NX.nextID():
NX.sequence = id_seq.get(NX.id, '.')
NX.save()
def updateGeneName(dFN, fFN, wigDir, chrom, strand, prefix, switchStrand = False):
NX = Nexus(dFN, fFN)
NX.load(['geneNames', 'tcc'])
if switchStrand:
strand = -strand
strand = str(strand)
coord_gName = cgWig.loadSingleWigTranscript(wigDir, chrom, strand, prefix)
while NX.nextID():
chrom, strand, start, end = bioLibCG.tccSplit(NX.tcc)
overlappingGenes = coord_gName.get(start, ".")
if overlappingGenes == "NONE":
NX.geneNames = []
else:
NX.geneNames = overlappingGenes.split(',')
NX.save()
def calculateTotalSNR(dataFN, oFF, mm, iSNRCutoff):
mm = str(mm)
dataNX = Nexus(dataFN, oFF)
dataNX.load(['snr', 'numUFBS', 'numUniqueSims', 'totalNumUFBSSim'])
#Check SNR Cutoffs
dataIDs = set()
lowSNRORNA = set()
while dataNX.nextID():
dataIDs.add(dataNX.id)
if dataNX.snr < iSNRCutoff:
lowSNRORNA.add(dataNX.id)
#NOTE sum of avgs != total avg
#get total numUFBS for data and simulation total,num unique sims
totalUFBSData = 0.0
totalUFBSSim = 0.0
totalPassingORNA = 0
totalUniqueSims = 0
while dataNX.nextID():
if (dataNX.id in lowSNRORNA): continue
totalUFBSData += dataNX.numUFBS
totalPassingORNA += 1
totalUFBSSim += dataNX.totalNumUFBSSim
totalUniqueSims += dataNX.numUniqueSims
totalAvgSimUFBS, totalAvgDataUFBS = 0.0, 0.0
try:
totalAvgSimUFBS = totalUFBSSim / float(totalUniqueSims)
totalAvgDataUFBS = totalUFBSData / float(totalPassingORNA)
totalSNR = totalAvgDataUFBS / totalAvgSimUFBS
oS = [
str(x) for x in [
mm, iSNRCutoff, totalUFBSData, totalAvgSimUFBS, totalSNR,
totalPassingORNA,
float(totalUFBSData) / totalPassingORNA
]
]
except ZeroDivisionError:
oS = [
str(x) for x in [
mm, iSNRCutoff, totalUFBSData, totalAvgSimUFBS, "NA",
totalPassingORNA, "NA"
]
]
print '\t'.join(oS)
def pickBestAlignment(fN, fFN):
NX = Nexus(fN, fFN)
NX.load(['sID', 'dID', 'score', 'best'])
#find best id
pair_score = {} #pair : score
pair_highID = {}
while NX.nextID():
pair = '%s_%s' % (NX.sID, NX.dID)
score = NX.score
if score > pair_score.get(pair, 0.0):
pair_score[pair] = score
pair_highID[pair] = NX.id
# update best id
bestIDs = set(pair_highID.values())
while NX.nextID():
if NX.id in bestIDs:
NX.best = True
NX.save()
def updateTargetIDs(oFN, oFF, aFN, aFF):
NX = Nexus(oFN, oFF)
NX.load(['filteredTargets'])
aNX = Nexus(aFN, aFF)
aNX.load(['sID'])
while aNX.nextID():
NX.id = aNX.sID
NX.filteredTargets.append(aNX.id)
NX.save()
def cleanForSNR(dataFN, oFF):
dataNX = Nexus(dataFN, oFF)
dataNX.load(['numUniqueSims', 'numUFBS', 'snrClean', 'siblingSet'])
id_numUFBS = dataNX.createMap('id', 'numUFBS')
id_siblingSet = dataNX.createMap('id', 'siblingSet')
unusedSiblings = []
for id, siblingSet in id_siblingSet.iteritems():
if len(siblingSet) == 1:
continue #NOTE: oRNA IDs are in their own sibling set
numUFBS__id = [(id_numUFBS[x], x) for x in siblingSet]
numUFBS__id.sort()
numUFBS__id.pop() #take last one (one we're keeping) out of list
unusedIDs = [x[1] for x in numUFBS__id]
unusedSiblings.extend(unusedIDs)
#tag unclean oRNA
while dataNX.nextID():
if (dataNX.id in unusedSiblings) or (dataNX.numUniqueSims < 10):
dataNX.snrClean = False
else:
dataNX.snrClean = True
dataNX.save()
def updateNumUFBS(oFN, oFF, aFN, aFF):
oNX = Nexus(oFN, oFF)
oNX.load(['filteredTargets', 'numUFBS'])
#just give it some blanks
if os.path.getsize(aFN) == 0:
while oNX.nextID():
oNX.numUFBS = 0
oNX.save()
return
aNX = Nexus(aFN, aFF)
aNX.load(['sigMask'])
while oNX.nextID():
sigMaskSet = set()
for aID in oNX.filteredTargets:
aNX.id = aID
sigMaskSet.add(aNX.sigMask)
oNX.numUFBS = len(sigMaskSet)
oNX.save()
def linkTargetIDs(oFN, oFF, aFN, aFF):
oNX = Nexus(oFN, oFF)
oNX.load(['filteredTargets'])
#just give it some blanks
if os.path.getsize(aFN) == 0:
while oNX.nextID():
oNX.filteredTargets = []
oNX.save()
return
aNX = Nexus(aFN, aFF)
aNX.load(['sID'])
sID_aIDs = aNX.createMap('sID', 'id', False)
for sID, aIDs in sID_aIDs.iteritems():
oNX.id = sID
oNX.filteredTargets = aIDs
oNX.save()
def linkTargetIDs(oFN, oFF, aFN, aFF):
oNX = Nexus(oFN, oFF)
oNX.load(['filteredTargets'])
#just give it some blanks
if os.path.getsize(aFN) == 0:
while oNX.nextID():
oNX.filteredTargets = []
oNX.save()
return
aNX = Nexus(aFN, aFF)
aNX.load(['sID'])
sID_aIDs = aNX.createMap('sID', 'id', False)
for sID, aIDs in sID_aIDs.iteritems():
oNX.id = sID
oNX.filteredTargets = aIDs
oNX.save()
def updateNumUFBS(oFN, oFF, aFN, aFF):
oNX = Nexus(oFN, oFF)
oNX.load(['filteredTargets', 'numUFBS'])
#just give it some blanks
if os.path.getsize(aFN) == 0:
while oNX.nextID():
oNX.numUFBS = 0
oNX.save()
return
aNX = Nexus(aFN, aFF)
aNX.load(['sigMask'])
while oNX.nextID():
sigMaskSet = set()
for aID in oNX.filteredTargets:
aNX.id = aID
sigMaskSet.add(aNX.sigMask)
oNX.numUFBS = len(sigMaskSet)
oNX.save()
def testPeaks(degFN, dForm, allGeneInfo, gForm, switchStrand = False):
#load/configure gene Info
gNX = Nexus(allGeneInfo, gForm)
gNX.load(['geneName', 'numReads', 'numSpots'])
gName_numReads = {}
gName_numSpots = {}
while gNX.nextID():
gName_numReads[gNX.geneName] = gNX.numReads
gName_numSpots[gNX.geneName] = gNX.numSpots
#load degFN info
dNX = Nexus(degFN, dForm)
dNX.load(['tcc', 'eLevel', 'geneNames', 'pValBin'])
while dNX.nextID():
gNames, readsForPeak = dNX.geneNames, dNX.eLevel
chrom, strand, start, end = bioLibCG.tccSplit(dNX.tcc)
if switchStrand:
strand = -int(strand)
pVals = []
for gName in gNames:
#may have to change gene name cuz of multiple spans
try:
totGeneReads = gName_numReads[gName]
numSpotsForGene = gName_numSpots[gName]
except KeyError:
try:
gName = gName + '_RE_%s_%s' % (chrom, strand)
totGeneReads = gName_numReads[gName]
numSpotsForGene = gName_numSpots[gName]
except KeyError:
print "FIX THIS GENE NAME", gName
continue
#add psuedocount
totGeneReads += 1
numSpotsForGene += 1 # not sure whether to do this yet...
#check for hidden intron gene overlap
try:
q = 1.0/numSpotsForGene
except ZeroDivisionError:
continue #intron gene
#add p val
pVals.append(binom.sf(readsForPeak, totGeneReads, q))
dNX.pValBin = max(pVals) if pVals else -1.0
dNX.save()
def updateAvgSS(dataFN, oFF, simDir, simBase, mm, numSims=100):
#get simulation information (# unique sims, num UFBS)
fileNames = [
'%s/simulation.%s/%s.%s' % (simDir, i, simBase, mm)
for i in range(numSims)
]
sID_numSimUFBS = {}
sID_simSeqs = {}
for fN in fileNames:
oNX = Nexus(fN, oFF)
oNX.load(['numUFBS', 'sequence'])
while oNX.nextID():
if oNX.sequence in sID_simSeqs.get(oNX.id, set()):
pass #dont count again
else:
sID_numSimUFBS.setdefault(oNX.id, []).append(oNX.numUFBS)
sID_simSeqs.setdefault(oNX.id, set()).add(oNX.sequence)
#update data based on sim info
dataNX = Nexus(dataFN, oFF)
dataNX.load(['avgNumSimSS', 'numUniqueSims', 'totalNumUFBSSim'])
while dataNX.nextID():
numUniqueSims = len(sID_numSimUFBS.get(dataNX.id, []))
totalSimUFBS = sum(sID_numSimUFBS.get(dataNX.id, []))
avgSimUFBS = totalSimUFBS / float(
numUniqueSims) if numUniqueSims != 0 else -1
dataNX.avgNumSimSS = avgSimUFBS
dataNX.numUniqueSims = numUniqueSims
dataNX.totalNumUFBSSim = totalSimUFBS
dataNX.save()
def updateAvgSS(dataFN, oFF, simDir, simBase, mm, numSims = 100):
#get simulation information (# unique sims, num UFBS)
fileNames = ['%s/simulation.%s/%s.%s' % (simDir, i, simBase, mm) for i in range(numSims)]
sID_numSimUFBS = {}
sID_simSeqs = {}
for fN in fileNames:
oNX = Nexus(fN, oFF)
oNX.load(['numUFBS', 'sequence'])
while oNX.nextID():
if oNX.sequence in sID_simSeqs.get(oNX.id, set()):
pass #dont count again
else:
sID_numSimUFBS.setdefault(oNX.id, []).append(oNX.numUFBS)
sID_simSeqs.setdefault(oNX.id, set()).add(oNX.sequence)
#update data based on sim info
dataNX = Nexus(dataFN, oFF)
dataNX.load(['avgNumSimSS', 'numUniqueSims', 'totalNumUFBSSim'])
while dataNX.nextID():
numUniqueSims = len(sID_numSimUFBS.get(dataNX.id, []))
totalSimUFBS = sum(sID_numSimUFBS.get(dataNX.id, []))
avgSimUFBS = totalSimUFBS/float(numUniqueSims) if numUniqueSims != 0 else -1
dataNX.avgNumSimSS = avgSimUFBS
dataNX.numUniqueSims = numUniqueSims
dataNX.totalNumUFBSSim = totalSimUFBS
dataNX.save()
