Better support for comparison to truth. Now emits FP rates for each covariate if a truth file is provided. Also now writes out a detailed recal.log file that can be parsed directly into R
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@2179 348d0f76-0448-11de-a6fe-93d51630548a
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depristo
parent
662bbbd53b
commit
2ea93385be
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@ -231,12 +231,27 @@ def validateBins(bins):
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elif contains2(left1) or contains2(right2):
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raise Exception("Bad bins", left1, right1, left2, right2)
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def printFieldQualHeader(more = ""):
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print ' field left right nVariants nNovels titv titvNovels dbSNP fprate q', more
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def printFieldQualHeader():
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more = ""
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if TRUTH_CALLS <> None:
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more = "TP FP FPRate FN FNRate"
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def p(stream):
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if stream <> None:
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print >> stream, ' field left right nVariants nNovels titv titvNovels dbSNP fprate q', more
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p(sys.stdout)
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p(RECAL_LOG)
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def printFieldQual( field, left, right, variants, FPRate, more = ""):
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def printFieldQual( field, left, right, variants, FPRate ):
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more = ""
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if TRUTH_CALLS <> None:
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callComparison, theseFPs = sensitivitySpecificity(variants, TRUTH_CALLS)
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more = str(callComparison)
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novels = selectVariants(variants, VCFRecord.isNovel)
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print ' %s %s %8d %8d %.2f %.2f %.2f %.2e %d' % (field, binString(left, right), len(variants), len(novels), titv(variants), titv(novels), dbSNPRate(variants), FPRate, phredScale(FPRate)), more
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def p(stream):
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if stream <> None:
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print >> stream, ' %s %s %8d %8d %.2f %.2f %.2f %.2e %d' % (field, binString(left, right), len(variants), len(novels), titv(variants), titv(novels), dbSNPRate(variants), FPRate, phredScale(FPRate)), more
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p(sys.stdout)
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p(RECAL_LOG)
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def binString(left, right):
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leftStr = str(left)
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@ -280,11 +295,9 @@ def optimizeCalls(variants, fields, titvTarget):
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def printCallQuals(recalCalls, titvTarget, info = ""):
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print '--------------------------------------------------------------------------------'
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print info
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calibrateFeatures(recalCalls, ['QUAL'], titvTarget, printCall = True, cumulative = False, forcePrint = True )
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calibrateFeatures(recalCalls, ['QUAL'], titvTarget, printCall = True, cumulative = False, forcePrint = True, prefix = "OPT-", printHeader = False )
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print 'Cumulative'
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calibrateFeatures(recalCalls, ['QUAL'], titvTarget, printCall = True, cumulative = True, forcePrint = True )
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calibrateFeatures(recalCalls, ['QUAL'], titvTarget, printCall = True, cumulative = True, forcePrint = True, prefix = "OPTCUM-", printHeader = False )
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def all( p, l ):
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for elt in l:
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@ -310,10 +323,10 @@ def mapVariantBins(variants, field, cumulative = False):
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return imap( variantsInBin, bins )
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def calibrateFeatures(variants, fields, titvTarget, printCall = False, cumulative = False, forcePrint = False ):
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def calibrateFeatures(variants, fields, titvTarget, printCall = False, cumulative = False, forcePrint = False, prefix = '', printHeader = True ):
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covariates = []
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printFieldQualHeader()
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if printHeader: printFieldQualHeader()
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for field in fields:
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if DEBUG: print 'Optimizing field', field
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@ -325,7 +338,7 @@ def calibrateFeatures(variants, fields, titvTarget, printCall = False, cumulativ
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titv, FPRate = titvFPRateEstimate(selectedVariants, titvTarget)
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dbsnp = dbSNPRate(selectedVariants)
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covariates.append(CallCovariate(field, left, right, FPRate))
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printFieldQual(field, left, right, selectedVariants, FPRate )
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printFieldQual( prefix + field, left, right, selectedVariants, FPRate )
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else:
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print 'Not calibrating bin', left, right, 'because it contains too few variants:', len(selectedVariants)
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@ -369,22 +382,20 @@ def sensitivitySpecificity(variants, truth):
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nFN = len(truth) - nTP
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return CallCmp(nTP, nFP, nFN), FPs
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def compareCalls(calls, truthCalls):
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for variant in calls: variant.setField("TP", 0) # set the TP field to 0
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def compare1(name, cumulative):
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for left, right, selectedVariants in mapVariantBins(calls, 'QUAL', cumulative = cumulative):
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callComparison, theseFPs = sensitivitySpecificity(selectedVariants, truthCalls)
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#print selectedVariants[0]
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printFieldQual(name, left, right, selectedVariants, dephredScale(left), str(callComparison))
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printFieldQual(name, left, right, selectedVariants, dephredScale(left))
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print 'PER BIN nCalls=', len(calls)
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printFieldQualHeader("TP FP FPRate FN FNRate")
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compare1('TRUTH-PER-BIN', False)
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# printFieldQualHeader()
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compare1('RECAL-QUAL-TRUTH-PER-BIN', False)
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print 'CUMULATIVE nCalls=', len(calls)
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compare1('TRUTH-CUMULATIVE', True)
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compare1('RECAL-QUAL-TRUTH-CUMULATIVE', True)
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def randomSplit(l, pLeft):
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def keep(elt, p):
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@ -406,6 +417,9 @@ def setup():
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parser.add_option("-t", "--truth", dest="truth",
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type='string', default=None,
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help="VCF formated truth file. If provided, the script will compare the input calls with the truth calls. It also emits calls tagged as TP and a separate file of FP calls")
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parser.add_option("-l", "--recalLog", dest="recalLog",
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type='string', default="recal.log",
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help="VCF formated truth file. If provided, the script will compare the input calls with the truth calls. It also emits calls tagged as TP and a separate file of FP calls")
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parser.add_option("", "--unFilteredTruth", dest="unFilteredTruth",
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action='store_true', default=False,
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help="If provided, the unfiltered truth calls will be used in comparisons [default: %default]")
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@ -425,7 +439,7 @@ def setup():
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type='int', default=60,
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help="The maximum Q score allowed for both a single covariate and the overall QUAL score [default: %default]")
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parser.add_option("-o", "--outputVCF", dest="outputVCF",
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type='string', default=None,
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type='string', default="recal.vcf",
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help="If provided, a VCF file will be written out to this file [default: %default]")
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parser.add_option("", "--FNoutputVCF", dest="FNoutputVCF",
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type='string', default=None,
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@ -491,7 +505,7 @@ def writeRecalibratedCalls(file, header, calls):
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print >> f, line
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f.close()
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def evaluateTruth(header, callVCF, truthVCF):
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def readTruth(truthVCF):
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print 'Reading truth file', truthVCF
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rawTruth = list(readVariants(truthVCF, maxRecords = None, decodeAll = False)[1])
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def keepVariant(t):
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@ -499,7 +513,9 @@ def evaluateTruth(header, callVCF, truthVCF):
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return OPTIONS.unFilteredTruth or t.passesFilters()
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truth = dict( [[v.getLoc(), v] for v in filter(keepVariant, rawTruth)])
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print len(rawTruth), len(truth)
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return truth
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def evaluateTruth(header, callVCF, truth, truthVCF):
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print 'Reading variants back in from', callVCF
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header, calls = readVariants(callVCF)
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calls = list(calls)
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@ -519,12 +535,28 @@ def evaluateTruth(header, callVCF, truthVCF):
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print >> f, line
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f.close()
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TRUTH_CALLS = None
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RECAL_LOG = None
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def main():
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global TRUTH_CALLS, RECAL_LOG
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args = setup()
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fields = OPTIONS.fields.split(',')
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truthVCF = None
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if len(args) > 1:
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truthVCF = args[1]
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TRUTH_CALLS = readTruth(truthVCF)
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if OPTIONS.recalLog <> None:
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RECAL_LOG = open(OPTIONS.recalLog, "w")
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print >> RECAL_LOG, "# optimized vcf", args[0]
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print >> RECAL_LOG, "# truth vcf", truthVCF
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for key, value in OPTIONS.__dict__.iteritems():
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print >> RECAL_LOG, '#', key, value
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header, allCalls, titvTarget = assessCalls(args[0])
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if not OPTIONS.dontRecalibrate:
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fields = OPTIONS.fields.split(',')
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covariates = determineCovariates(allCalls, titvTarget, fields)
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header, callsToRecalibate = readVariants(args[0], OPTIONS.maxRecords)
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RecalibratedCalls = recalibrateCalls(callsToRecalibate, fields, covariates)
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@ -534,7 +566,7 @@ def main():
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OPTIONS.outputVCF = args[0]
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if len(args) > 1:
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evaluateTruth(header, OPTIONS.outputVCF, args[1])
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evaluateTruth(header, OPTIONS.outputVCF, TRUTH_CALLS, truthVCF)
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PROFILE = False
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