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testing_convert_to_FVs.py
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testing_convert_to_FVs.py
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import time
startTime=time.clock()
import sys
from msTools import *
from fvTools import *
import random
import allel
import numpy as np
import math
'''usage eg:
pMisPol=`python2 stairwayPlotToPMisPol.py AOM.meru_mela.sfs.sp.summary`
python2 testing_convert_to_FVs.py spNeut.msOut.gz 2L,2R,3L,3R 5000 11 0.25 $pMisPol AOM_partial_stats.txt Anopheles-gambiae-PEST_CHROMOSOMES_AgamP3.accessible.fa anc.meru_mela.fa ./ spNeut.msOut.fvec
'''
if len(sys.argv)!=12:
sys.exit("usage:\npython2 testing_convert_to_FVs.py trainingDataFileName chrArmsForMasking subWinSize numSubWins unmaskedFracCutoff pMisPol partialStatAndDafFileName maskFileName ancestralArmFaFileName statDir fvecFileName\n")
else:
trainingDataFileName, chrArmsForMasking, subWinSize, numSubWins, unmaskedFracCutoff, pMisPol, partialStatAndDafFileName, maskFileName, ancestralArmFaFileName, statDir, fvecFileName = sys.argv[1:]
subWinSize,numSubWins = int(subWinSize),int(numSubWins)
assert subWinSize>0 and numSubWins>1
totalPhysLen=(subWinSize*numSubWins)
trainingDataFileObj,sampleSize,numInstances=openMsOutFileForSequentialReading(trainingDataFileName)
chrArmsForMasking=chrArmsForMasking.split(",")
unmaskedFracCutoff,pMisPol=float(unmaskedFracCutoff),float(pMisPol)
if unmaskedFracCutoff>1.0:
sys.exit("unmaskedFracCutoff must lie within [0, 1].\n")
if pMisPol>1.0:
sys.exit("pMisPol must lie within [0, 1].\n")
standardizationInfo=readStatsDafsComputeStandardizationBins(partialStatAndDafFileName,nBins=50,pMisPol=pMisPol)
if maskFileName.lower() in ["none", "false"]:
unmaskedFracCutoff=1.0
unmasked=[True]*totalPhysLen
sys.stderr.write("Warning: not doing any masking! (i.e. mask.fa file for the chr arm with all masked sites N'ed out, or at least the reference with Ns, is not provided)\n")
maskFileName=False
else:
if ancestralArmFaFileName.lower() in ["none", "false"]:
maskData=readMaskDataForTraining(maskFileName,totalPhysLen,subWinSize,chrArmsForMasking,shuffle=True,cutoff=unmaskedFracCutoff)
else:
maskData=readMaskAndAncDataForTraining(maskFileName,ancestralArmFaFileName,totalPhysLen,subWinSize,chrArmsForMasking,shuffle=True,cutoff=unmaskedFracCutoff)
if len(maskData)<numInstances:
sys.stderr.write("Warning: not enough windows from masked data (needed %d; got %d); will draw with replacement!!\n" %(numInstances,len(maskData)))
drawWithReplacement=True
else:
sys.stderr.write("enough windows from masked data (needed %d; got %d); will draw without replacement.\n" %(numInstances,len(maskData)))
drawWithReplacement=False
statNames=["pi", "thetaW", "tajD", "thetaH", "fayWuH", "HapCount", "H1", "H12", "H2/H1", "ZnS", "Omega", "iHSMean", "iHSMax", "iHSOutFrac", "nSLMean", "nSLMax", "nSLOutFrac", "distVar", "distSkew", "distKurt"]
for i in ["HAF", "HAFunique", "phi", "kappa", "SFS", "SAFE"]:
for j in ["Mean", "Median", "Mode", "Lower95%", "Lower50%", "Upper50%", "Upper95%", "Max", "Var", "SD", "Skew", "Kurt"]:
statNames.append("%s-%s" %(i, j))
header=[]
for statName in statNames:
for i in range(numSubWins):
header.append("%s_win%d" %(statName, i))
header="\t".join(header)
statVals={}
for statName in statNames:
statVals[statName]=[]
def getSubWinBounds(subWinSize,totalPhysLen):
subWinStart=1-subWinSize
subWinEnd=0
subWinBounds=[]
for i in range(0,(numSubWins-1)):
subWinStart+=subWinSize
subWinEnd+=subWinSize
subWinBounds.append((subWinStart,subWinEnd))
subWinStart+=subWinSize
subWinEnd=totalPhysLen
subWinBounds.append((subWinStart,subWinEnd))
return subWinBounds
subWinBounds=getSubWinBounds(subWinSize,totalPhysLen)
def getSnpIndicesInSubWins(subWinBounds,positions):
snpIndicesInSubWins=[]
for subWinIndex in range(len(subWinBounds)):
snpIndicesInSubWins.append([])
subWinIndex=0
for i in range(len(positions)):
while not (positions[i]>=subWinBounds[subWinIndex][0] and positions[i]<=subWinBounds[subWinIndex][1]):
subWinIndex+=1
snpIndicesInSubWins[subWinIndex].append(i)
return snpIndicesInSubWins
quantiles={"Lower95%":2.5,"Lower50%":25,"Upper50%":75,"Upper95%":97.5}
if statDir.lower() in ["none","false","default"]:
statDir='./'
statFiles=[]
for subWinIndex in range(numSubWins):
statFileName='/'.join(("%s/%s.subWin%d.stats" %(statDir, trainingDataFileName.split("/")[-1].replace(".msOut.gz",""), subWinIndex)).split('//'))
statFiles.append(open(statFileName,"w"))
statFiles[-1].write("\t".join(statNames)+"\n")
if fvecFileName.lower() in ["none","false","default"]:
fvecFileName="%s.fvec" %trainingDataFileName.split("/")[-1].replace(".msOut.gz","")
fvecFile=open(fvecFileName,"w")
fvecFile.write(header+"\n")
for instanceIndex in range(numInstances):
hapArrayIn,positionArray=readNextMsRepToHaplotypeArrayIn(trainingDataFileObj,sampleSize,totalPhysLen)
for statName in statNames:
statVals[statName].append([])
if maskFileName:
if drawWithReplacement:
unmasked=random.choice(maskData)
else:
unmasked=maskData[instanceIndex]
assert len(unmasked)==totalPhysLen
snpIndicesToKeep=[x for x in range(len(positionArray)) if unmasked[positionArray[x]-1]]
if len(snpIndicesToKeep)==0:
for subWinIndex in range(numSubWins):
for statName in statNames:
appendStatValsForMonomorphic(statName,statVals,instanceIndex,subWinIndex)
statFiles[subWinIndex].write("\t".join([str(statVals[statName][instanceIndex][subWinIndex]) for statName in statNames])+"\n")
else:
haps=allel.HaplotypeArray(hapArrayIn,dtype='i1').subset(sel0=snpIndicesToKeep)
if pMisPol>0:
misPolarizeCorrectionIndex=np.random.binomial(1,pMisPol,len(haps))
for i in range(len(misPolarizeCorrectionIndex)):
if misPolarizeCorrectionIndex[i]==1:
for j in range(len(haps[i])):
if haps[i][j]==0:
haps[i][j]=1
else:
haps[i][j]=0
genos=haps.to_genotypes(ploidy=2)
alleleCounts=genos.count_alleles()
positions=[positionArray[x] for x in snpIndicesToKeep]
precomputedStats={}
dafs=alleleCounts[:,1]/float(sampleSize)
ihsVals=allel.stats.selection.ihs(haps,positions,use_threads=False,include_edges=False)
nonNanCount=[x for x in np.isnan(ihsVals)].count(False)
nonInfCount=[x for x in np.isinf(ihsVals)].count(False)
sys.stderr.write("number of iHS scores: %d (%d non-nan; %d non-inf)\n" %(len(ihsVals),nonNanCount,nonInfCount))
if nonNanCount==0:
precomputedStats["iHS"]=[]
for subWinIndex in range(numSubWins):
precomputedStats["iHS"].append([])
else:
ihsVals=standardize_by_allele_count_from_precomp_bins(ihsVals,dafs,standardizationInfo["iHS"])
precomputedStats["iHS"]=windowVals(ihsVals,subWinBounds,positions,keepNans=False,absVal=True)
nslVals=allel.stats.selection.nsl(haps,use_threads=False)
nonNanCount=[x for x in np.isnan(nslVals)].count(False)
sys.stderr.write("number of nSL scores: %d (%d non-nan)\n" %(len(nslVals),nonNanCount))
if nonNanCount==0:
precomputedStats["nSL"]=[]
for subWinIndex in range(numSubWins):
precomputedStats["nSL"].append([])
else:
nslVals=standardize_by_allele_count_from_precomp_bins(nslVals,dafs,standardizationInfo["nSL"])
precomputedStats["nSL"]=windowVals(nslVals,subWinBounds,positions,keepNans=False,absVal=True)
snpIndicesInSubWins=getSnpIndicesInSubWins(subWinBounds,positions)
for subWinIndex in range(numSubWins):
subWinStart,subWinEnd = subWinBounds[subWinIndex]
unmaskedFrac=unmasked[subWinStart-1:subWinEnd].count(True)/float(subWinSize)
assert unmaskedFrac>=unmaskedFracCutoff
if len(snpIndicesInSubWins[subWinIndex])==0:
for statName in statNames:
appendStatValsForMonomorphic(statName,statVals,instanceIndex,subWinIndex)
else:
hapsInSubWin=haps.subset(sel0=snpIndicesInSubWins[subWinIndex])
for statName in [x for x in statNames if x[0:3]!="HAF" and x[0:3]!="phi" and x[0:3]!="kap" and x[0:3]!="SFS" and x[0:3]!="SAF"]:
calcAndAppendStatVal(alleleCounts, positions, statName, subWinStart, subWinEnd, statVals, instanceIndex, subWinIndex, hapsInSubWin, unmasked, precomputedStats)
haplotypes={}
for i in range(len(hapsInSubWin[0])):
haplotype=[hapsInSubWin[x][i] for x in range(len(hapsInSubWin))]
haplotype="".join(str(x) for x in haplotype)
if haplotype in haplotypes:
haplotypes[haplotype].append(i)
else:
haplotypes[haplotype]=[i]
HAF=[]
HAFunique={}
for i in haplotypes:
HAFunique[i]=0
for j in range(len(hapsInSubWin)):
if hapsInSubWin[j][haplotypes[i][0]]==1:
HAFunique[i]+=sum([hapsInSubWin[j][x] for x in range(len(hapsInSubWin[j]))])
for j in range(len(haplotypes[i])):
HAF.append(HAFunique[i])
phi=[]
kappa=[]
SAFE=[]
for i in range(len(hapsInSubWin)):
phi.append(0)
kappa.append([])
phiDenom=0
for j in haplotypes:
phi[i]+=(int(list(j)[i])*HAFunique[j]*len(haplotypes[j]))
if int(list(j)[i])==1 and HAFunique[j] not in kappa[i] and HAFunique[j]!=0:
kappa[i].append(HAFunique[j])
phiDenom+=(HAFunique[j]*len(haplotypes[j]))
phi[i]/=float(phiDenom)
kappa[i]=len(kappa[i])/float(len(set([HAFunique[x] for x in HAFunique if HAFunique[x]!=0])))
if dafs[snpIndicesInSubWins[subWinIndex]][i]==0 or dafs[snpIndicesInSubWins[subWinIndex]][i]==1:
SAFE.append(0.0)
else:
SAFE.append((phi[i]-kappa[i])/float(math.sqrt(dafs[snpIndicesInSubWins[subWinIndex]][i]*(1-dafs[snpIndicesInSubWins[subWinIndex]][i]))))
for i in ["HAF", "HAFunique", "phi", "kappa", "SFS", "SAFE"]:
if i=="SFS":
windowStats=dafs[snpIndicesInSubWins[subWinIndex]]
elif i=="HAFunique":
windowStats=[eval(i)[x] for x in eval(i)]
else:
windowStats=eval(i)
statVals[i+"-Mean"][instanceIndex].append(np.mean(windowStats))
statVals[i+"-Median"][instanceIndex].append(np.median(windowStats))
if(len(np.unique(windowStats,return_counts=True)[1])==1):
statVals[i+"-Mode"][instanceIndex].append(windowStats[0])
else:
if(sorted(np.unique(windowStats,return_counts=True)[1])[-1]!=sorted(np.unique(windowStats,return_counts=True)[1])[-2]):
statVals[i+"-Mode"][instanceIndex].append(scipy.stats.mstats.mode(windowStats)[0][0])
else:
mode=min(windowStats)
for j in range(1,51):
if len([x for x in windowStats if x>=(min(windowStats)+(j*((max(windowStats)-min(windowStats))/50))) and x<(min(windowStats)+((j+1)*((max(windowStats)-min(windowStats))/50)))]) >= len([x for x in windowStats if x>=mode and x<(mode+((max(windowStats)-min(windowStats))/50))]):
mode=min(windowStats)+(j*((max(windowStats)-min(windowStats))/50))
statVals[i+"-Mode"][instanceIndex].append(mode+((max(windowStats)-min(windowStats))/100))
for j in quantiles:
statVals[i+"-"+j][instanceIndex].append(np.percentile(windowStats,quantiles[j]))
statVals[i+"-Max"][instanceIndex].append(max(windowStats))
statVals[i+"-Var"][instanceIndex].append(np.var(windowStats))
statVals[i+"-SD"][instanceIndex].append(np.std(windowStats))
statVals[i+"-Skew"][instanceIndex].append(scipy.stats.skew(windowStats))
statVals[i+"-Kurt"][instanceIndex].append(scipy.stats.kurtosis(windowStats))
statFiles[subWinIndex].write("\t".join([str(statVals[statName][instanceIndex][subWinIndex]) for statName in statNames])+"\n")
outVec=[]
for statName in statNames:
outVec+=normalizeFeatureVec(statVals[statName][instanceIndex])
fvecFile.write("\t".join([str(x) for x in outVec])+"\n")
closeMsOutFile(trainingDataFileObj)
for subWinIndex in range(numSubWins):
statFiles[subWinIndex].close()
fvecFile.close()
sys.stderr.write("total time spent calculating summary statistics and generating feature vectors: %g secs\n" %(time.clock()-startTime))