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snpSelector now supports min and max q scores. 

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@2751 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
depristo 2010-01-31 19:38:34 +00:00
parent 7b3c34d210
commit e964660df3
1 changed files with 14 additions and 10 deletions
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24
python/snpSelector.py
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@ -91,7 +91,7 @@ class RecalibratedCall:
def __str__(self):
return '[%s: %s => Q%d]' % (str(self.call), self.featureStringList(), phredScale(self.jointFPErrorRate()))
def readVariants( file, maxRecords = None, decodeAll = True, downsampleFraction = 1, filter = None, minQScore = -1, mustBeVariant = False, skip = None ):
def readVariants( file, maxRecords = None, decodeAll = True, downsampleFraction = 1, filter = None, minQScore = -1, maxQScore = 10000000, mustBeVariant = False, skip = None ):
if filter == None:
filter = not OPTIONS.unfiltered
@ -108,14 +108,15 @@ def readVariants( file, maxRecords = None, decodeAll = True, downsampleFraction
if filter and not VCF.passesFilters() or ( False and mustBeVariant == True and not VCF.isVariant() ): # currently ignore mustBeVariant
#print 'filtering', VCF
return None
elif VCF.getQual() <= minQScore:
#print 'filtering', VCF
elif VCF.getQual() <= minQScore or VCF.getQual() > maxQScore:
#print 'filtering', VCF.getQual()
#nLowQual += 1
return None
elif skip <> None and counter % skip <> 0:
#print 'skipping'
return None
elif random.random() <= downsampleFraction:
#print 'keeping', VCF
return VCF
else:
return None
@ -160,7 +161,7 @@ def titvFPRateEstimate(variants, target):
else:
FPRate = (titvRatio - target) / (0.5 - target)
FPRate = min(max(FPRate, 0), 1)
TPRate = max(min(1 - FPRate, 1 - dephredScale(OPTIONS.maxQScore)), dephredScale(OPTIONS.maxQScore))
TPRate = max(min(1 - FPRate, 1 - dephredScale(OPTIONS.maxQScoreForCovariate)), dephredScale(OPTIONS.maxQScoreForCovariate))
if DEBUG: print 'FPRate', FPRate, titvRatio, target
assert FPRate >= 0 and FPRate <= 1
return TPRate
@ -174,8 +175,8 @@ def titvFPRateEstimate(variants, target):
TPRate = gaussian(titvRatio, target, sigma) / gaussian(target, target, sigma)
if LEFT_HANDED and titvRatio >= target:
TPRate = 1
TPRate -= dephredScale(OPTIONS.maxQScore)
if DEBUG: print 'TPRate', TPRate, constant, exponent, dephredScale(OPTIONS.maxQScore)
TPRate -= dephredScale(OPTIONS.maxQScoreForCovariate)
if DEBUG: print 'TPRate', TPRate, constant, exponent, dephredScale(OPTIONS.maxQScoreForCovariate)
return TPRate
FPRate = 1 - theoreticalCalc()
@ -556,13 +557,16 @@ def setup():
parser.add_option("-M", "--maxRecords", dest="maxRecords",
type='int', default=None,
help="Maximum number of input VCF records to process, if provided. Default is all records")
parser.add_option("-Q", "--qMin", dest="minQScore",
parser.add_option("-q", "--qMin", dest="minQScore",
type='int', default=-1,
help="The minimum Q score of the initial SNP list to consider for selection [default: %default]")
parser.add_option("-Q", "--qMax", dest="maxQScore",
type='int', default=1000000,
help="The maximum Q score allowed for both a single covariate and the overall QUAL score [default: %default]")
parser.add_option("", "--QBreaks", dest="QBreaks",
type='string', default=None,
help="Breaks in QUAL for generating covarites [default: %default]")
parser.add_option("-q", "--qMax", dest="maxQScore",
parser.add_option("", "--maxQScoreForCovariate", dest="maxQScoreForCovariate",
type='int', default=60,
help="The maximum Q score allowed for both a single covariate and the overall QUAL score [default: %default]")
parser.add_option("-o", "--outputVCF", dest="outputVCF",
@ -602,7 +606,7 @@ def assessCalls(file):
numberOfRecords = OPTIONS.maxRecords
downsampleFraction = min(float(OPTIONS.maxRecordsForCovariates) / numberOfRecords, 1)
#print 'Reading variants', OPTIONS.skip, downsampleFraction
header, allCalls = readVariants(file, OPTIONS.maxRecords, downsampleFraction=downsampleFraction, minQScore = OPTIONS.minQScore, skip = OPTIONS.skip)
header, allCalls = readVariants(file, OPTIONS.maxRecords, downsampleFraction=downsampleFraction, minQScore = OPTIONS.minQScore, maxQScore = OPTIONS.maxQScore, skip = OPTIONS.skip)
allCalls = list(allCalls)
print 'Number of VCF records', numberOfRecords, ', max number of records for covariates is', OPTIONS.maxRecordsForCovariates, 'so keeping', downsampleFraction * 100, '% of records'
print 'Number of selected VCF records', len(allCalls)
@ -698,7 +702,7 @@ def main():
if not OPTIONS.dontRecalibrate:
covariates = determineCovariates(allCalls, titvTarget, fields)
print OPTIONS
header, callsToRecalibate = readVariants(args[0], OPTIONS.maxRecords, minQScore = OPTIONS.minQScore, skip = OPTIONS.skip)
header, callsToRecalibate = readVariants(args[0], OPTIONS.maxRecords, minQScore = OPTIONS.minQScore, maxQScore = OPTIONS.maxQScore, skip = OPTIONS.skip)
RecalibratedCalls = recalibrateCalls(callsToRecalibate, fields, covariates)
writeRecalibratedCalls(OPTIONS.outputVCF, header, RecalibratedCalls)
else: