Refactoring of PairHMM to support reduced reads
This commit is contained in:
Mark DePristo
parent
a6b65d6347
commit
fa0efbc4ca
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@ -28,6 +28,7 @@ package org.broadinstitute.sting.gatk.walkers.indels;
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import net.sf.samtools.Cigar;
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import net.sf.samtools.CigarElement;
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import net.sf.samtools.CigarOperator;
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import net.sf.samtools.SAMRecord;
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import org.broadinstitute.sting.gatk.contexts.ReferenceContext;
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import org.broadinstitute.sting.utils.Haplotype;
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import org.broadinstitute.sting.utils.MathUtils;
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@ -244,31 +245,31 @@ public class PairHMMIndelErrorModel {
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/**
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* For each covariate read in a value and parse it. Associate those values with the data itself (num observation and num mismatches)
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*/
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/*
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private void addCSVData(final File file, final String line) {
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final String[] vals = line.split(",");
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/*
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private void addCSVData(final File file, final String line) {
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final String[] vals = line.split(",");
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// Check if the data line is malformed, for example if the read group string contains a comma then it won't be parsed correctly
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if( vals.length != requestedCovariates.size() + 3 ) { // +3 because of nObservations, nMismatch, and Qempirical
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throw new UserException.MalformedFile(file, "Malformed input recalibration file. Found data line with too many fields: " + line +
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" --Perhaps the read group string contains a comma and isn't being parsed correctly.");
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// Check if the data line is malformed, for example if the read group string contains a comma then it won't be parsed correctly
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if( vals.length != requestedCovariates.size() + 3 ) { // +3 because of nObservations, nMismatch, and Qempirical
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throw new UserException.MalformedFile(file, "Malformed input recalibration file. Found data line with too many fields: " + line +
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" --Perhaps the read group string contains a comma and isn't being parsed correctly.");
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}
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final Object[] key = new Object[requestedCovariates.size()];
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Covariate cov;
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int iii;
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for( iii = 0; iii < requestedCovariates.size(); iii++ ) {
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cov = requestedCovariates.get( iii );
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key[iii] = cov.getValue( vals[iii] );
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}
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// Create a new datum using the number of observations, number of mismatches, and reported quality score
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final RecalDatum datum = new RecalDatum( Long.parseLong( vals[iii] ), Long.parseLong( vals[iii + 1] ), Double.parseDouble( vals[1] ), 0.0 );
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// Add that datum to all the collapsed tables which will be used in the sequential calculation
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dataManager.addToAllTables( key, datum, PRESERVE_QSCORES_LESS_THAN );
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}
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final Object[] key = new Object[requestedCovariates.size()];
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Covariate cov;
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int iii;
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for( iii = 0; iii < requestedCovariates.size(); iii++ ) {
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cov = requestedCovariates.get( iii );
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key[iii] = cov.getValue( vals[iii] );
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}
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// Create a new datum using the number of observations, number of mismatches, and reported quality score
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final RecalDatum datum = new RecalDatum( Long.parseLong( vals[iii] ), Long.parseLong( vals[iii + 1] ), Double.parseDouble( vals[1] ), 0.0 );
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// Add that datum to all the collapsed tables which will be used in the sequential calculation
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dataManager.addToAllTables( key, datum, PRESERVE_QSCORES_LESS_THAN );
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}
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*/
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*/
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public PairHMMIndelErrorModel(double indelGOP, double indelGCP, boolean deb, boolean doCDP, boolean dovit) {
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this(indelGOP, indelGCP, deb, doCDP);
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this.doViterbi = dovit;
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@ -588,7 +589,7 @@ public class PairHMMIndelErrorModel {
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}
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else {
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c = currentGOP[jm1];
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d = currentGCP[jm1];
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d = currentGCP[jm1];
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}
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if (indI == X_METRIC_LENGTH-1)
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c = d = END_GAP_COST;
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@ -707,12 +708,12 @@ public class PairHMMIndelErrorModel {
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}
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}
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public synchronized double[] computeReadHaplotypeLikelihoods(ReadBackedPileup pileup, LinkedHashMap<Allele,Haplotype> haplotypeMap,
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ReferenceContext ref, int eventLength,
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HashMap<PileupElement, LinkedHashMap<Allele,Double>> indelLikelihoodMap){
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ReferenceContext ref, int eventLength,
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HashMap<PileupElement, LinkedHashMap<Allele,Double>> indelLikelihoodMap){
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int numHaplotypes = haplotypeMap.size();
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double[][] haplotypeLikehoodMatrix = new double[numHaplotypes][numHaplotypes];
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double readLikelihoods[][] = new double[pileup.getReads().size()][numHaplotypes];
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final double readLikelihoods[][] = new double[pileup.size()][numHaplotypes];
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final int readCounts[] = new int[pileup.size()];
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int readIdx=0;
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LinkedHashMap<Allele,double[]> gapOpenProbabilityMap = new LinkedHashMap<Allele,double[]>();
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@ -751,6 +752,9 @@ public class PairHMMIndelErrorModel {
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}
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}
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for (PileupElement p: pileup) {
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// > 1 when the read is a consensus read representing multiple independent observations
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final boolean isReduced = ReadUtils.isReducedRead(p.getRead());
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readCounts[readIdx] = isReduced ? p.getReducedCount() : 1;
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// check if we've already computed likelihoods for this pileup element (i.e. for this read at this location)
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if (indelLikelihoodMap.containsKey(p)) {
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@ -762,61 +766,65 @@ public class PairHMMIndelErrorModel {
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}
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else {
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//System.out.format("%d %s\n",p.getRead().getAlignmentStart(), p.getRead().getClass().getName());
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GATKSAMRecord read = ReadUtils.hardClipAdaptorSequence(p.getRead());
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SAMRecord read = ReadUtils.hardClipAdaptorSequence(p.getRead());
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if (read == null)
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continue;
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if ( isReduced ) {
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read = ReadUtils.reducedReadWithReducedQuals(read);
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}
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if(ReadUtils.is454Read(read) && !getGapPenaltiesFromFile) {
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continue;
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}
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double[] recalQuals = null;
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/*
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if (getGapPenaltiesFromFile) {
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RecalDataManager.parseSAMRecord( read, RAC );
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/*
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if (getGapPenaltiesFromFile) {
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RecalDataManager.parseSAMRecord( read, RAC );
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recalQuals = new double[read.getReadLength()];
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recalQuals = new double[read.getReadLength()];
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//compute all covariate values for this read
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final Comparable[][] covariateValues_offset_x_covar =
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RecalDataManager.computeCovariates((GATKSAMRecord) read, requestedCovariates);
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// For each base in the read
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for( int offset = 0; offset < read.getReadLength(); offset++ ) {
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//compute all covariate values for this read
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final Comparable[][] covariateValues_offset_x_covar =
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RecalDataManager.computeCovariates((GATKSAMRecord) read, requestedCovariates);
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// For each base in the read
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for( int offset = 0; offset < read.getReadLength(); offset++ ) {
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final Object[] fullCovariateKey = covariateValues_offset_x_covar[offset];
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final Object[] fullCovariateKey = covariateValues_offset_x_covar[offset];
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Byte qualityScore = (Byte) qualityScoreByFullCovariateKey.get(fullCovariateKey);
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if(qualityScore == null)
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{
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qualityScore = performSequentialQualityCalculation( fullCovariateKey );
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qualityScoreByFullCovariateKey.put(qualityScore, fullCovariateKey);
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}
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Byte qualityScore = (Byte) qualityScoreByFullCovariateKey.get(fullCovariateKey);
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if(qualityScore == null)
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{
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qualityScore = performSequentialQualityCalculation( fullCovariateKey );
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qualityScoreByFullCovariateKey.put(qualityScore, fullCovariateKey);
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}
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recalQuals[offset] = -((double)qualityScore)/10.0;
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}
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recalQuals[offset] = -((double)qualityScore)/10.0;
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}
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// for each read/haplotype combination, compute likelihoods, ie -10*log10(Pr(R | Hi))
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// = sum_j(-10*log10(Pr(R_j | Hi) since reads are assumed to be independent
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if (DEBUG) {
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System.out.format("\n\nStarting read:%s S:%d US:%d E:%d UE:%d C:%s\n",read.getReadName(),
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read.getAlignmentStart(),
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read.getUnclippedStart(), read.getAlignmentEnd(), read.getUnclippedEnd(),
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read.getCigarString());
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// for each read/haplotype combination, compute likelihoods, ie -10*log10(Pr(R | Hi))
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// = sum_j(-10*log10(Pr(R_j | Hi) since reads are assumed to be independent
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if (DEBUG) {
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System.out.format("\n\nStarting read:%s S:%d US:%d E:%d UE:%d C:%s\n",read.getReadName(),
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read.getAlignmentStart(),
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read.getUnclippedStart(), read.getAlignmentEnd(), read.getUnclippedEnd(),
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read.getCigarString());
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byte[] bases = read.getReadBases();
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for (int k = 0; k < recalQuals.length; k++) {
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System.out.format("%c",bases[k]);
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}
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System.out.println();
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byte[] bases = read.getReadBases();
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for (int k = 0; k < recalQuals.length; k++) {
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System.out.format("%c",bases[k]);
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}
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System.out.println();
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for (int k = 0; k < recalQuals.length; k++) {
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System.out.format("%.0f ",recalQuals[k]);
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}
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System.out.println();
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}
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} */
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for (int k = 0; k < recalQuals.length; k++) {
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System.out.format("%.0f ",recalQuals[k]);
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}
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System.out.println();
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}
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} */
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// get bases of candidate haplotypes that overlap with reads
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final int trailingBases = 3;
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@ -971,7 +979,7 @@ public class PairHMMIndelErrorModel {
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System.out.println(new String(haplotypeBases));
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}
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Double readLikelihood = 0.0;
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double readLikelihood = 0.0;
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if (useAffineGapModel) {
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double[] currentContextGOP = null;
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@ -979,14 +987,14 @@ public class PairHMMIndelErrorModel {
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if (doContextDependentPenalties) {
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if (getGapPenaltiesFromFile) {
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readLikelihood = computeReadLikelihoodGivenHaplotypeAffineGaps(haplotypeBases, readBases, readQuals, recalCDP, null);
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if (getGapPenaltiesFromFile) {
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readLikelihood = computeReadLikelihoodGivenHaplotypeAffineGaps(haplotypeBases, readBases, readQuals, recalCDP, null);
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} else {
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currentContextGOP = Arrays.copyOfRange(gapOpenProbabilityMap.get(a), (int)indStart, (int)indStop);
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currentContextGCP = Arrays.copyOfRange(gapContProbabilityMap.get(a), (int)indStart, (int)indStop);
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readLikelihood = computeReadLikelihoodGivenHaplotypeAffineGaps(haplotypeBases, readBases, readQuals, currentContextGOP, currentContextGCP);
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}
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} else {
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currentContextGOP = Arrays.copyOfRange(gapOpenProbabilityMap.get(a), (int)indStart, (int)indStop);
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currentContextGCP = Arrays.copyOfRange(gapContProbabilityMap.get(a), (int)indStart, (int)indStop);
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readLikelihood = computeReadLikelihoodGivenHaplotypeAffineGaps(haplotypeBases, readBases, readQuals, currentContextGOP, currentContextGCP);
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}
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}
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}
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@ -1004,7 +1012,7 @@ public class PairHMMIndelErrorModel {
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if (DEBUG) {
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System.out.println("\nLikelihood summary");
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for (readIdx=0; readIdx < pileup.getReads().size(); readIdx++) {
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for (readIdx=0; readIdx < pileup.size(); readIdx++) {
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System.out.format("Read Index: %d ",readIdx);
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for (int i=0; i < readLikelihoods[readIdx].length; i++)
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System.out.format("L%d: %f ",i,readLikelihoods[readIdx][i]);
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@ -1012,36 +1020,35 @@ public class PairHMMIndelErrorModel {
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}
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}
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return getHaplotypeLikelihoods(numHaplotypes, readCounts, readLikelihoods);
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}
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private final static double[] getHaplotypeLikelihoods(final int numHaplotypes, final int readCounts[], final double readLikelihoods[][]) {
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final double[][] haplotypeLikehoodMatrix = new double[numHaplotypes][numHaplotypes];
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// todo: MAD 09/26/11 -- I'm almost certain this calculation can be simplied to just a single loop without the intermediate NxN matrix
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for (int i=0; i < numHaplotypes; i++) {
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for (int j=i; j < numHaplotypes; j++){
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// combine likelihoods of haplotypeLikelihoods[i], haplotypeLikelihoods[j]
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// L(Hi, Hj) = sum_reads ( Pr(R|Hi)/2 + Pr(R|Hj)/2)
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//readLikelihoods[k][j] has log10(Pr(R_k) | H[j] )
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for (readIdx=0; readIdx < pileup.getReads().size(); readIdx++) {
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for (int readIdx = 0; readIdx < readLikelihoods.length; readIdx++) {
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// Compute log10(10^x1/2 + 10^x2/2) = log10(10^x1+10^x2)-log10(2)
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// First term is approximated by Jacobian log with table lookup.
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if (Double.isInfinite(readLikelihoods[readIdx][i]) && Double.isInfinite(readLikelihoods[readIdx][j]))
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continue;
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haplotypeLikehoodMatrix[i][j] += ( MathUtils.softMax(readLikelihoods[readIdx][i],
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readLikelihoods[readIdx][j]) + LOG_ONE_HALF);
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final double li = readLikelihoods[readIdx][i];
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final double lj = readLikelihoods[readIdx][j];
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final int readCount = readCounts[readIdx];
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haplotypeLikehoodMatrix[i][j] += readCount * (MathUtils.softMax(li, lj) + LOG_ONE_HALF);
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}
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}
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}
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return getHaplotypeLikelihoods(haplotypeLikehoodMatrix);
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}
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public static double[] getHaplotypeLikelihoods(double[][] haplotypeLikehoodMatrix) {
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int hSize = haplotypeLikehoodMatrix.length;
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double[] genotypeLikelihoods = new double[hSize*(hSize+1)/2];
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final double[] genotypeLikelihoods = new double[numHaplotypes*(numHaplotypes+1)/2];
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int k=0;
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for (int j=0; j < hSize; j++) {
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for (int j=0; j < numHaplotypes; j++) {
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for (int i=0; i <= j; i++){
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genotypeLikelihoods[k++] = haplotypeLikehoodMatrix[i][j];
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}
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@ -1066,63 +1073,63 @@ public class PairHMMIndelErrorModel {
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* @param key The list of Comparables that were calculated from the covariates
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* @return A recalibrated quality score as a byte
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*/
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/*
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private byte performSequentialQualityCalculation( final Object... key ) {
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/*
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private byte performSequentialQualityCalculation( final Object... key ) {
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final byte qualFromRead = (byte)Integer.parseInt(key[1].toString());
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final Object[] readGroupCollapsedKey = new Object[1];
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final Object[] qualityScoreCollapsedKey = new Object[2];
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final Object[] covariateCollapsedKey = new Object[3];
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final byte qualFromRead = (byte)Integer.parseInt(key[1].toString());
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final Object[] readGroupCollapsedKey = new Object[1];
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final Object[] qualityScoreCollapsedKey = new Object[2];
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final Object[] covariateCollapsedKey = new Object[3];
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// The global quality shift (over the read group only)
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readGroupCollapsedKey[0] = key[0];
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final RecalDatum globalRecalDatum = ((RecalDatum)dataManager.getCollapsedTable(0).get( readGroupCollapsedKey ));
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double globalDeltaQ = 0.0;
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if( globalRecalDatum != null ) {
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final double globalDeltaQEmpirical = globalRecalDatum.getEmpiricalQuality();
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final double aggregrateQReported = globalRecalDatum.getEstimatedQReported();
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globalDeltaQ = globalDeltaQEmpirical - aggregrateQReported;
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}
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// The shift in quality between reported and empirical
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qualityScoreCollapsedKey[0] = key[0];
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qualityScoreCollapsedKey[1] = key[1];
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final RecalDatum qReportedRecalDatum = ((RecalDatum)dataManager.getCollapsedTable(1).get( qualityScoreCollapsedKey ));
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double deltaQReported = 0.0;
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if( qReportedRecalDatum != null ) {
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final double deltaQReportedEmpirical = qReportedRecalDatum.getEmpiricalQuality();
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deltaQReported = deltaQReportedEmpirical - qualFromRead - globalDeltaQ;
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}
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// The shift in quality due to each covariate by itself in turn
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double deltaQCovariates = 0.0;
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double deltaQCovariateEmpirical;
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covariateCollapsedKey[0] = key[0];
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covariateCollapsedKey[1] = key[1];
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for( int iii = 2; iii < key.length; iii++ ) {
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covariateCollapsedKey[2] = key[iii]; // The given covariate
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final RecalDatum covariateRecalDatum = ((RecalDatum)dataManager.getCollapsedTable(iii).get( covariateCollapsedKey ));
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if( covariateRecalDatum != null ) {
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deltaQCovariateEmpirical = covariateRecalDatum.getEmpiricalQuality();
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deltaQCovariates += ( deltaQCovariateEmpirical - qualFromRead - (globalDeltaQ + deltaQReported) );
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// The global quality shift (over the read group only)
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readGroupCollapsedKey[0] = key[0];
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final RecalDatum globalRecalDatum = ((RecalDatum)dataManager.getCollapsedTable(0).get( readGroupCollapsedKey ));
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double globalDeltaQ = 0.0;
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if( globalRecalDatum != null ) {
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final double globalDeltaQEmpirical = globalRecalDatum.getEmpiricalQuality();
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final double aggregrateQReported = globalRecalDatum.getEstimatedQReported();
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globalDeltaQ = globalDeltaQEmpirical - aggregrateQReported;
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}
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// The shift in quality between reported and empirical
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qualityScoreCollapsedKey[0] = key[0];
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qualityScoreCollapsedKey[1] = key[1];
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final RecalDatum qReportedRecalDatum = ((RecalDatum)dataManager.getCollapsedTable(1).get( qualityScoreCollapsedKey ));
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double deltaQReported = 0.0;
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if( qReportedRecalDatum != null ) {
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final double deltaQReportedEmpirical = qReportedRecalDatum.getEmpiricalQuality();
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deltaQReported = deltaQReportedEmpirical - qualFromRead - globalDeltaQ;
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}
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// The shift in quality due to each covariate by itself in turn
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double deltaQCovariates = 0.0;
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double deltaQCovariateEmpirical;
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covariateCollapsedKey[0] = key[0];
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covariateCollapsedKey[1] = key[1];
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for( int iii = 2; iii < key.length; iii++ ) {
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covariateCollapsedKey[2] = key[iii]; // The given covariate
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final RecalDatum covariateRecalDatum = ((RecalDatum)dataManager.getCollapsedTable(iii).get( covariateCollapsedKey ));
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if( covariateRecalDatum != null ) {
|
||||
deltaQCovariateEmpirical = covariateRecalDatum.getEmpiricalQuality();
|
||||
deltaQCovariates += ( deltaQCovariateEmpirical - qualFromRead - (globalDeltaQ + deltaQReported) );
|
||||
}
|
||||
}
|
||||
|
||||
final double newQuality = qualFromRead + globalDeltaQ + deltaQReported + deltaQCovariates;
|
||||
return QualityUtils.boundQual( (int)Math.round(newQuality), (byte)MAX_QUALITY_SCORE );
|
||||
|
||||
// Verbose printouts used to validate with old recalibrator
|
||||
//if(key.contains(null)) {
|
||||
// System.out.println( key + String.format(" => %d + %.2f + %.2f + %.2f + %.2f = %d",
|
||||
// qualFromRead, globalDeltaQ, deltaQReported, deltaQPos, deltaQDinuc, newQualityByte));
|
||||
//}
|
||||
//else {
|
||||
// System.out.println( String.format("%s %s %s %s => %d + %.2f + %.2f + %.2f + %.2f = %d",
|
||||
// key.get(0).toString(), key.get(3).toString(), key.get(2).toString(), key.get(1).toString(), qualFromRead, globalDeltaQ, deltaQReported, deltaQPos, deltaQDinuc, newQualityByte) );
|
||||
//}
|
||||
|
||||
//return newQualityByte;
|
||||
|
||||
}
|
||||
|
||||
final double newQuality = qualFromRead + globalDeltaQ + deltaQReported + deltaQCovariates;
|
||||
return QualityUtils.boundQual( (int)Math.round(newQuality), (byte)MAX_QUALITY_SCORE );
|
||||
|
||||
// Verbose printouts used to validate with old recalibrator
|
||||
//if(key.contains(null)) {
|
||||
// System.out.println( key + String.format(" => %d + %.2f + %.2f + %.2f + %.2f = %d",
|
||||
// qualFromRead, globalDeltaQ, deltaQReported, deltaQPos, deltaQDinuc, newQualityByte));
|
||||
//}
|
||||
//else {
|
||||
// System.out.println( String.format("%s %s %s %s => %d + %.2f + %.2f + %.2f + %.2f = %d",
|
||||
// key.get(0).toString(), key.get(3).toString(), key.get(2).toString(), key.get(1).toString(), qualFromRead, globalDeltaQ, deltaQReported, deltaQPos, deltaQDinuc, newQualityByte) );
|
||||
//}
|
||||
|
||||
//return newQualityByte;
|
||||
|
||||
}
|
||||
*/
|
||||
*/
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue