Automatic cutting of recalibrated variant calls using ApplyVariantCuts. VariantRecalibrator produces the tranches plot alongside the optimization curve. Specify the levels using -tranche 1.0 -tranche 5.0 etc
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@3472 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
rpoplin
parent
4a555827aa
commit
290771a8c2
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@ -32,4 +32,18 @@ points(data$pCut, data$numNovel,col="DarkGreen",pch=20)
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axis(side=4,col="DarkGreen")
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mtext("Number of Variants", side=4, line=2, col="DarkGreen",cex=1.4)
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legend("topright", c("Known","Novel"), col=c("Green","DarkGreen"), pch=c(20,20),cex=0.7)
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dev.off()
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dev.off()
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data2 = read.table(paste(input,".tranches",sep=""),sep=",",head=T)
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outfile = paste(input, ".FDRtranches.pdf", sep="")
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pdf(outfile, height=7, width=8)
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alpha = (data2$novelTITV - 0.5) / (targetTITV - 0.5);
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numGood = round(alpha * data2$numNovel);
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numBad = data2$numNovel - numGood;
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d=matrix(c(numGood,numBad),2,byrow=TRUE)
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barplot(d,horiz=TRUE,col=c(1,2),space=0.2,xlab="Number of Novel Variants",ylab="Novel Ti/Tv --> FDR (%)")
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legend('topright',c('implied TP','implied FP'),col=c(1,2),lty=1,lwd=16)
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axis(2,line=-1,at=0.7+(0:(length(data2$FDRtranche)-1))*1.2,tick=FALSE,labels=data2$FDRtranche)
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axis(2,line=0.4,at=0.7+(0:(length(data2$FDRtranche)-1))*1.2,tick=FALSE,labels=data2$novelTITV)
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dev.off()
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@ -0,0 +1,195 @@
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/*
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* Copyright (c) 2010 The Broad Institute
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*
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* Permission is hereby granted, free of charge, to any person
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* obtaining a copy of this software and associated documentation
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* files (the "Software"), to deal in the Software without
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* restriction, including without limitation the rights to use,
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* copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following
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* conditions:
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*
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* The above copyright notice and this permission notice shall be
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* included in all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
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* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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* NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
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* HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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* WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
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* THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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*/
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package org.broadinstitute.sting.gatk.walkers.variantrecalibration;
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import Jama.Matrix;
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import org.broad.tribble.dbsnp.DbSNPFeature;
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import org.broad.tribble.vcf.*;
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import org.broadinstitute.sting.gatk.contexts.AlignmentContext;
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import org.broadinstitute.sting.gatk.contexts.ReferenceContext;
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import org.broadinstitute.sting.gatk.contexts.variantcontext.VariantContext;
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import org.broadinstitute.sting.gatk.datasources.simpleDataSources.ReferenceOrderedDataSource;
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import org.broadinstitute.sting.gatk.refdata.RefMetaDataTracker;
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import org.broadinstitute.sting.gatk.refdata.VariantContextAdaptors;
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import org.broadinstitute.sting.gatk.refdata.tracks.RMDTrack;
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import org.broadinstitute.sting.gatk.refdata.utils.helpers.DbSNPHelper;
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import org.broadinstitute.sting.gatk.walkers.RodWalker;
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import org.broadinstitute.sting.utils.*;
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import org.broadinstitute.sting.utils.collections.ExpandingArrayList;
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import org.broadinstitute.sting.commandline.Argument;
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import org.broadinstitute.sting.utils.genotype.vcf.VCFReader;
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import org.broadinstitute.sting.utils.genotype.vcf.VCFUtils;
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import org.broadinstitute.sting.utils.genotype.vcf.VCFWriter;
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import org.broadinstitute.sting.utils.text.XReadLines;
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import java.io.File;
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import java.io.FileNotFoundException;
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import java.io.IOException;
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import java.util.*;
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/**
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* Applies cuts to the input vcf file (by adding filter lines) to achieve the desired novel FDR levels which were specified during VariantRecalibration
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*
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* @author rpoplin
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* @since Jun 2, 2010
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*
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* @help.summary Applies cuts to the input vcf file (by adding filter lines) to achieve the desired novel FDR levels which were specified during VariantRecalibration
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*/
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public class ApplyVariantCuts extends RodWalker<Integer, Integer> {
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/////////////////////////////
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// Command Line Arguments
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/////////////////////////////
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@Argument(fullName="tranchesFile", shortName="tf", doc="The input tranches file describing where to cut the data", required=true)
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private String TRANCHE_FILENAME = "optimizer.dat.tranches";
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@Argument(fullName="outputVCFFile", shortName="outputVCF", doc="The output filtered VCF file", required=true)
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private String OUTPUT_FILENAME = "optimizer.vcf";
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/////////////////////////////
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// Private Member Variables
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/////////////////////////////
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private VCFWriter vcfWriter;
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private final ArrayList<String> ALLOWED_FORMAT_FIELDS = new ArrayList<String>();
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final ExpandingArrayList<Double> qCuts = new ExpandingArrayList<Double>();
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final ExpandingArrayList<String> filterName = new ExpandingArrayList<String>();
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//---------------------------------------------------------------------------------------------------------------
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//
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// initialize
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//
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//---------------------------------------------------------------------------------------------------------------
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public void initialize() {
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boolean firstLine = true;
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try {
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for( final String line : new XReadLines(new File( TRANCHE_FILENAME )) ) {
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if( !firstLine ) {
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final String[] vals = line.split(",");
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qCuts.add(Double.parseDouble(vals[2]));
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filterName.add(vals[4]);
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}
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firstLine = false;
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}
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} catch( FileNotFoundException e ) {
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throw new StingException("Can not find input file: " + TRANCHE_FILENAME);
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}
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ALLOWED_FORMAT_FIELDS.add(VCFGenotypeRecord.GENOTYPE_KEY); // copied from VariantsToVCF
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ALLOWED_FORMAT_FIELDS.add(VCFGenotypeRecord.GENOTYPE_QUALITY_KEY);
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ALLOWED_FORMAT_FIELDS.add(VCFGenotypeRecord.DEPTH_KEY);
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ALLOWED_FORMAT_FIELDS.add(VCFGenotypeRecord.GENOTYPE_LIKELIHOODS_KEY);
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// setup the header fields
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final Set<VCFHeaderLine> hInfo = new HashSet<VCFHeaderLine>();
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hInfo.addAll(VCFUtils.getHeaderFields(getToolkit()));
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hInfo.add(new VCFInfoHeaderLine("OQ", 1, VCFInfoHeaderLine.INFO_TYPE.Float, "The original variant quality score"));
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hInfo.add(new VCFHeaderLine("source", "VariantOptimizer"));
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vcfWriter = new VCFWriter( new File(OUTPUT_FILENAME) );
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final TreeSet<String> samples = new TreeSet<String>();
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final List<ReferenceOrderedDataSource> dataSources = this.getToolkit().getRodDataSources();
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for( final ReferenceOrderedDataSource source : dataSources ) {
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final RMDTrack rod = source.getReferenceOrderedData();
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if( rod.getRecordType().equals(VCFRecord.class) ) {
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final VCFReader reader = new VCFReader(rod.getFile());
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final Set<String> vcfSamples = reader.getHeader().getGenotypeSamples();
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samples.addAll(vcfSamples);
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reader.close();
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}
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}
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// BUGBUG : why doesn't this work? VCFFilterHeaderLine is protected?
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//for( int iii = 1; iii < filterName.size(); iii++ ) {
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// hInfo.add(new VCFFilterHeaderLine(filterName.get(iii)), String.format("FDR tranche level at qual " + qCuts.get(iii)));
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//}
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//hInfo.add(new VCFFilterHeaderLine(filterName.get(0)+"+"), String.format("FDR tranche level at qual > " + qCuts.get(filterName.size()-1)));
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final VCFHeader vcfHeader = new VCFHeader(hInfo, samples);
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vcfWriter.writeHeader(vcfHeader);
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}
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//---------------------------------------------------------------------------------------------------------------
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//
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// map
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//
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//---------------------------------------------------------------------------------------------------------------
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public Integer map( RefMetaDataTracker tracker, ReferenceContext ref, AlignmentContext context ) {
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if( tracker == null ) { // For some reason RodWalkers get map calls with null trackers
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return 1;
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}
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for( final VariantContext vc : tracker.getAllVariantContexts(ref, null, context.getLocation(), false, false) ) {
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if( vc != null && !vc.getName().equals(DbSNPHelper.STANDARD_DBSNP_TRACK_NAME) && vc.isSNP() ) {
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final VCFRecord vcf = VariantContextAdaptors.toVCF(vc, ref.getBase(), ALLOWED_FORMAT_FIELDS, false, false);
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if( !vc.isFiltered() ) {
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final double qual = vc.getPhredScaledQual();
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boolean setFilter = false;
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for( int tranche = qCuts.size() - 1; tranche >= 0; tranche-- ) {
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if( qual >= qCuts.get(tranche) ) {
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if(tranche == qCuts.size() - 1) {
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vcf.setFilterString(VCFRecord.PASSES_FILTERS);
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setFilter = true;
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} else {
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vcf.setFilterString(filterName.get(tranche));
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setFilter = true;
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}
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break;
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}
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}
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if( !setFilter ) {
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vcf.setFilterString(filterName.get(0)+"+");
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}
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}
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vcfWriter.addRecord( vcf );
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}
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}
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return 1; // This value isn't used for anything
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}
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//---------------------------------------------------------------------------------------------------------------
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//
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// reduce
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//
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//---------------------------------------------------------------------------------------------------------------
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public Integer reduceInit() {
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return 1;
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}
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public Integer reduce( final Integer mapValue, final Integer reduceSum ) {
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return 1;
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}
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public void onTraversalDone( ExpandingArrayList<VariantDatum> reduceSum ) {
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vcfWriter.close();
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}
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}
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@ -102,6 +102,8 @@ public class GenerateVariantClustersWalker extends RodWalker<ExpandingArrayList<
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//---------------------------------------------------------------------------------------------------------------
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public void initialize() {
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if( !PATH_TO_RESOURCES.endsWith("/") ) { PATH_TO_RESOURCES = PATH_TO_RESOURCES + "/"; }
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annotationKeys = new ExpandingArrayList<String>(Arrays.asList(USE_ANNOTATIONS));
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if( IGNORE_INPUT_FILTERS != null ) {
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@ -367,7 +367,8 @@ public final class VariantGaussianMixtureModel extends VariantOptimizationModel
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// BUGBUG: next output the actual cluster on top by integrating out every other annotation
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}
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public final void outputOptimizationCurve( final VariantDatum[] data, final String outputPrefix, final int desiredNumVariants ) {
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public final void outputOptimizationCurve( final VariantDatum[] data, final String outputPrefix,
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final int desiredNumVariants, final Double[] FDRtranches ) {
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final int numVariants = data.length;
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final boolean[] markedVariant = new boolean[numVariants];
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@ -382,6 +383,11 @@ public final class VariantGaussianMixtureModel extends VariantOptimizationModel
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final double novelTiTvAtCut[] = new double[NUM_BINS];
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final double theCut[] = new double[NUM_BINS];
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final double fdrCutAsTiTv[] = new double[FDRtranches.length];
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for( int iii = 0; iii < FDRtranches.length; iii++ ) {
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fdrCutAsTiTv[iii] = (1.0 - FDRtranches[iii] / 100.0) * (targetTITV - 0.5) + 0.5;
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}
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for( int iii = 0; iii < numVariants; iii++ ) {
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markedVariant[iii] = false;
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}
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@ -392,6 +398,13 @@ public final class VariantGaussianMixtureModel extends VariantOptimizationModel
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} catch (Exception e) {
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throw new StingException( "Unable to create output file: " + outputPrefix + ".dat" );
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}
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PrintStream tranchesOutputFile;
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try {
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tranchesOutputFile = new PrintStream( outputPrefix + ".dat.tranches" );
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tranchesOutputFile.println("FDRtranche,novelTITV,pCut,numNovel,filterName");
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} catch (Exception e) {
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throw new StingException( "Unable to create output file: " + outputPrefix + ".dat.tranches" );
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}
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int numKnown = 0;
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int numNovel = 0;
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@ -447,7 +460,16 @@ public final class VariantGaussianMixtureModel extends VariantOptimizationModel
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// loop back through the data points looking for appropriate places to cut the data to get the target novel titv ratio
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int checkQuantile = 0;
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int tranche = FDRtranches.length - 1;
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for( jjj = NUM_BINS-1; jjj >= 0; jjj-- ) {
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if( tranche >= 0 && novelTiTvAtCut[jjj] >= fdrCutAsTiTv[tranche] ) {
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tranchesOutputFile.println(String.format("%.2f,%.2f,%.2f,%d,FDRtranche%.2fto%.2f",
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FDRtranches[tranche],novelTiTvAtCut[jjj],theCut[jjj],numNovelAtCut[jjj],
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(tranche == 0 ? 0.0 : FDRtranches[tranche-1]) ,FDRtranches[tranche]));
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tranche--;
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}
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boolean foundCut = false;
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if( checkQuantile == 0 ) {
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if( novelTiTvAtCut[jjj] >= 0.9 * targetTITV ) {
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@ -480,10 +502,12 @@ public final class VariantGaussianMixtureModel extends VariantOptimizationModel
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String.format("%.2f",((double) numKnownAtCut[jjj] * 100.0) / ((double) numKnownAtCut[jjj] + numNovelAtCut[jjj]) ) + "%)");
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logger.info("\t" + String.format("%.4f known Ti/Tv ratio", knownTiTvAtCut[jjj]));
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logger.info("\t" + String.format("%.4f novel Ti/Tv ratio", novelTiTvAtCut[jjj]));
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logger.info("\t" + String.format("--> with an implied novel FDR of %.2f percent", Math.abs(100.0 * (1.0-((novelTiTvAtCut[jjj] - 0.5) / (targetTITV - 0.5))))));
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}
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}
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outputFile.close();
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tranchesOutputFile.close();
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}
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@ -81,6 +81,8 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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private String OUTPUT_PREFIX = "optimizer";
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@Argument(fullName="clusterFile", shortName="clusterFile", doc="The output cluster file", required=true)
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private String CLUSTER_FILENAME = "optimizer.cluster";
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@Argument(fullName="FDRtranche", shortName="tranche", doc="The levels of novel false discovery rate (FDR, implied by ti/tv) at which to slice the data. (in percent, that is 1.0 for 1 percent)", required=false)
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private Double[] FDR_TRANCHES = null;
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//@Argument(fullName = "optimization_model", shortName = "om", doc = "Optimization calculation model to employ -- GAUSSIAN_MIXTURE_MODEL is currently the default, while K_NEAREST_NEIGHBORS is also available for small callsets.", required = false)
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private VariantOptimizationModel.Model OPTIMIZATION_MODEL = VariantOptimizationModel.Model.GAUSSIAN_MIXTURE_MODEL;
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@Argument(fullName = "path_to_Rscript", shortName = "Rscript", doc = "The path to your implementation of Rscript. For Broad users this is probably /broad/tools/apps/R-2.6.0/bin/Rscript", required = false)
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@ -156,6 +158,19 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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if(!foundDBSNP) {
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throw new StingException("dbSNP track is required. This calculation is critically dependent on being able to distinguish known and novel sites.");
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}
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// Set up default values for the FDR tranches if necessary
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if( FDR_TRANCHES == null ) {
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FDR_TRANCHES = new Double[3];
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FDR_TRANCHES[0] = 12.5;
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FDR_TRANCHES[1] = 20.0;
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FDR_TRANCHES[2] = 30.0;
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//FDR_TRANCHES[0] = 1.0;
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//FDR_TRANCHES[1] = 5.0;
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//FDR_TRANCHES[2] = 10.0;
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//FDR_TRANCHES[3] = 20.0;
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//FDR_TRANCHES[4] = 30.0;
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}
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}
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//---------------------------------------------------------------------------------------------------------------
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@ -226,7 +241,7 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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final VariantDataManager dataManager = new VariantDataManager( reduceSum, theModel.dataManager.annotationKeys );
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reduceSum.clear(); // Don't need this ever again, clean up some memory
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theModel.outputOptimizationCurve( dataManager.data, OUTPUT_PREFIX, DESIRED_NUM_VARIANTS );
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theModel.outputOptimizationCurve( dataManager.data, OUTPUT_PREFIX, DESIRED_NUM_VARIANTS, FDR_TRANCHES );
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// Execute Rscript command to plot the optimization curve
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// Print out the command line to make it clear to the user what is being executed and how one might modify it
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