-Refactoring: make GenotypeCalculationModel constructors empty so that they don't have to be updated every time we add a new parameter; instead put that logic in the super class's initialize method (making everything protected so that only the factory can access them)
-Adding initial version of Multi-sample calculation model. This still needs much work: it needs to be cleaned up and finished. Right now, it (purposely) throws a RuntimeException after completing the EM loop. Also: -Fix logic in GenotypeLikelihoods.setPriors -Add logger to the models for output git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1764 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
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parent
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@ -3,6 +3,7 @@ package org.broadinstitute.sting.gatk.walkers.genotyper;
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import org.broadinstitute.sting.gatk.contexts.AlignmentContext;
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import org.broadinstitute.sting.gatk.refdata.RefMetaDataTracker;
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import org.broadinstitute.sting.utils.genotype.GenotypeWriter;
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import org.apache.log4j.Logger;
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import java.util.*;
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@ -22,35 +23,45 @@ public abstract class GenotypeCalculationModel implements Cloneable {
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protected Set<String> samples;
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protected EmpiricalSubstitutionGenotypeLikelihoods.SequencerPlatform defaultPlatform;
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protected GenotypeWriter out;
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protected Logger logger;
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protected boolean GENOTYPE_MODE;
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protected double LOD_THRESHOLD;
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protected int maxDeletionsInPileup;
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protected boolean VERBOSE;
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/**
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* Create a new GenotypeCalculationModel object with given debugging level
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* Create a new GenotypeCalculationModel object
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*/
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protected GenotypeCalculationModel() {}
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/**
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* Initialize the GenotypeCalculationModel object
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* Assumes that out is not null
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* @param baseModel base model to use
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* @param samples samples in input bam
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* @param platform default platform
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* @param out output writer
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* @param out logger
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* @param genotypeMode genotyping
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* @param lod lod threshold
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* @param maxDeletions max deletions to tolerate
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* @param verbose verbose flag
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*/
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public GenotypeCalculationModel(BaseMismatchModel baseModel,
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Set<String> samples,
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EmpiricalSubstitutionGenotypeLikelihoods.SequencerPlatform platform,
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GenotypeWriter out,
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boolean genotypeMode,
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double lod,
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int maxDeletions,
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boolean verbose) {
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protected void initialize(BaseMismatchModel baseModel,
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Set<String> samples,
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EmpiricalSubstitutionGenotypeLikelihoods.SequencerPlatform platform,
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GenotypeWriter out,
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Logger logger,
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boolean genotypeMode,
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double lod,
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int maxDeletions,
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boolean verbose) {
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this.baseModel = baseModel;
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this.samples = samples;
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defaultPlatform = platform;
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this.out = out;
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this.logger = logger;
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GENOTYPE_MODE = genotypeMode;
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LOD_THRESHOLD = lod;
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maxDeletionsInPileup = maxDeletions;
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@ -68,6 +79,7 @@ public abstract class GenotypeCalculationModel implements Cloneable {
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gcm.samples = new HashSet<String>(samples);
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gcm.defaultPlatform = defaultPlatform;
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gcm.out = out;
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gcm.logger = logger;
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gcm.GENOTYPE_MODE = GENOTYPE_MODE;
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gcm.LOD_THRESHOLD = LOD_THRESHOLD;
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gcm.maxDeletionsInPileup = maxDeletionsInPileup;
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@ -27,6 +27,7 @@ package org.broadinstitute.sting.gatk.walkers.genotyper;
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import static org.broadinstitute.sting.gatk.walkers.genotyper.GenotypeCalculationModel.Model.*;
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import org.broadinstitute.sting.utils.genotype.GenotypeWriter;
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import org.apache.log4j.Logger;
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import java.util.Set;
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@ -47,17 +48,29 @@ public class GenotypeCalculationModelFactory {
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*
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* @param UAC The unified argument collection
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* @param samples samples in bam
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* @param out output
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* @param out output writer
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* @param logger logger
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* @return model
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*/
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public static GenotypeCalculationModel makeGenotypeCalculation(UnifiedArgumentCollection UAC,
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Set<String> samples,
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GenotypeWriter out) {
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GenotypeWriter out,
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Logger logger) {
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GenotypeCalculationModel gcm;
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switch ( UAC.genotypeModel ) {
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case SINGLE_SAMPLE: return new SingleSampleGenotypeCalculationModel(UAC.baseModel, samples, UAC.defaultPlatform, out, UAC.GENOTYPE, UAC.LOD_THRESHOLD, UAC.MAX_DELETIONS, UAC.VERBOSE);
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//case MULTI_SAMPLE_EM: return new MultiSampleEMGenotypeCalculationModel(UAC.baseModel, samples, UAC.defaultPlatform, out, UAC.GENOTYPE, UAC.LOD_THRESHOLD, UAC.MAX_DELETIONS, UAC.VERBOSE);
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case MULTI_SAMPLE_ALL_MAFS: return new MultiSampleAllMAFsGenotypeCalculationModel(UAC.baseModel, samples, UAC.defaultPlatform, out, UAC.GENOTYPE, UAC.LOD_THRESHOLD, UAC.MAX_DELETIONS, UAC.VERBOSE);
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case SINGLE_SAMPLE:
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gcm = new SingleSampleGenotypeCalculationModel();
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break;
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case MULTI_SAMPLE_EM:
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gcm = new MultiSampleEMGenotypeCalculationModel();
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break;
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case MULTI_SAMPLE_ALL_MAFS:
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gcm = new MultiSampleAllMAFsGenotypeCalculationModel();
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break;
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default: throw new RuntimeException("Unexpected GenotypeCalculationModel " + UAC.genotypeModel);
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}
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gcm.initialize(UAC.baseModel, samples, UAC.defaultPlatform, out, logger, UAC.GENOTYPE, UAC.LOD_THRESHOLD, UAC.MAX_DELETIONS, UAC.VERBOSE);
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return gcm;
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}
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}
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@ -192,6 +192,7 @@ public abstract class GenotypeLikelihoods implements Cloneable {
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*/
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public void setPriors(DiploidGenotypePriors priors) {
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this.priors = priors;
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posteriors = zeros.clone();
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for ( DiploidGenotype g : DiploidGenotype.values() ) {
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int i = g.ordinal();
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posteriors[i] = priors.getPriors()[i] + likelihoods[i];
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@ -3,31 +3,13 @@ package org.broadinstitute.sting.gatk.walkers.genotyper;
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import org.broadinstitute.sting.utils.genotype.GenotypeWriter;
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import org.broadinstitute.sting.gatk.contexts.AlignmentContext;
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import org.broadinstitute.sting.gatk.refdata.RefMetaDataTracker;
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import org.apache.log4j.Logger;
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import java.util.Set;
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public class MultiSampleAllMAFsGenotypeCalculationModel extends GenotypeCalculationModel {
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public MultiSampleAllMAFsGenotypeCalculationModel(BaseMismatchModel baseModel,
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Set<String> samples,
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EmpiricalSubstitutionGenotypeLikelihoods.SequencerPlatform platform,
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GenotypeWriter out,
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boolean genotypeMode,
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double lod,
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int maxDeletions,
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boolean verbose) {
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super(baseModel, samples, platform, out, genotypeMode, lod, maxDeletions, verbose);
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}
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/**
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* Cloning of the object
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* @return clone
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* @throws CloneNotSupportedException
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*/
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protected Object clone() throws CloneNotSupportedException {
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return super.clone();
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}
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protected MultiSampleAllMAFsGenotypeCalculationModel() {}
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public boolean calculateGenotype(RefMetaDataTracker tracker, char ref, AlignmentContext context, DiploidGenotypePriors priors) {
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@ -0,0 +1,252 @@
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package org.broadinstitute.sting.gatk.walkers.genotyper;
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import org.broadinstitute.sting.utils.*;
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import org.broadinstitute.sting.utils.genotype.GenotypeWriter;
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import org.broadinstitute.sting.utils.genotype.DiploidGenotype;
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import org.broadinstitute.sting.gatk.contexts.AlignmentContext;
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import org.broadinstitute.sting.gatk.refdata.RefMetaDataTracker;
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import java.util.*;
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import net.sf.samtools.SAMRecord;
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import net.sf.samtools.SAMReadGroupRecord;
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public class MultiSampleEMGenotypeCalculationModel extends GenotypeCalculationModel {
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// We need to set a limit on the EM iterations in case something flukey goes on
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private static final int MAX_EM_ITERATIONS = 6;
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// We consider the EM stable when the MAF doesn't change more than 1/10N
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private static final double EM_STABILITY_METRIC = 0.1;
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protected MultiSampleEMGenotypeCalculationModel() {}
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public boolean calculateGenotype(RefMetaDataTracker tracker, char ref, AlignmentContext context, DiploidGenotypePriors priors) {
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// keep track of the GenotpeLikelihoods for each sample, separated by strand
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HashMap<String, PerSampleGenotypeLikelihoods> GLs = new HashMap<String, PerSampleGenotypeLikelihoods>();
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// keep track of the total counts of each base in the pileup
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int[] baseCounts = new int[4];
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int totalObservedBases = 0;
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// First order of business: create the initial GenotypeLikelihoods objects.
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// Also, confirm that there aren't too many deletions in the pileup.
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int deletionsInPile = 0;
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List<SAMRecord> reads = context.getReads();
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List<Integer> offsets = context.getOffsets();
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for (int i = 0; i < reads.size(); i++) {
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// set up the base data
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SAMRecord read = reads.get(i);
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int offset = offsets.get(i);
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char base = read.getReadString().charAt(offset);
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// don't use bad bases
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int baseIndex = BaseUtils.simpleBaseToBaseIndex(base);
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if ( baseIndex == -1 ) {
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// are there too many deletions in the pileup?
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if ( ++deletionsInPile > maxDeletionsInPileup )
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return false;
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continue;
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}
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// create the GL holder object if this is the first time we're seeing a base for this sample
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String readGroup = read.getAttribute("RG").toString(); // can't be null because those are filtered out
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String sample = read.getHeader().getReadGroup(readGroup).getSample();
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PerSampleGenotypeLikelihoods myGLs = GLs.get(sample);
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if ( myGLs == null ) {
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myGLs = new PerSampleGenotypeLikelihoods();
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GLs.put(sample, myGLs);
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}
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// assign the base to the appropriate strand
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myGLs.addObservedBase(base, read, offset);
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// update the base counts
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baseCounts[baseIndex]++;
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totalObservedBases++;
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}
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// optimization: if all bases are ref, return
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if ( baseCounts[BaseUtils.simpleBaseToBaseIndex(ref)] == totalObservedBases )
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return true;
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// TODO -- Do we quit if there isn't X% of total samples represented in pileup?
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// Next, we need to create initial allele frequencies.
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// An intelligent guess would be the observed base ratios (plus some small number to account for sampling issues).
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// TODO --- This needs to be broken out into a separate model as we might want to split up the calculation for each minor allele
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double[] alleleFrequencies = new double[4];
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for (int i = 0; i < 4; i++)
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alleleFrequencies[i] = ((double)baseCounts[i] + DiploidGenotypePriors.HUMAN_HETEROZYGOSITY) / (double)totalObservedBases;
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DiploidGenotypePriors AFPriors = calculateAlleleFreqBasedPriors(alleleFrequencies);
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for ( PerSampleGenotypeLikelihoods SGL : GLs.values() )
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SGL.createGLs(AFPriors);
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// debugging output
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for (int i = 0; i < 4; i++)
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logger.debug("Base count and initial allele frequency for " + BaseUtils.baseIndexToSimpleBase(i) + ": " + baseCounts[i] + ", " + alleleFrequencies[i]);
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// The EM loop:
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// we want to continue until the calculation is stable, but we need some max on the number of iterations
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int iterations = 0;
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// We consider the EM stable when the MAF doesn't change more than EM_STABILITY_METRIC/N
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double EM_STABILITY_VALUE = EM_STABILITY_METRIC / (2.0 * (double)GLs.size());
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boolean isStable = false;
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while ( !isStable && iterations < MAX_EM_ITERATIONS ) {
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// calculate the posterior-weighted allele frequencies and modify the priors accordingly
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double[] newAlleleFrequencies = getPosteriorWeightedFrequencies(GLs.values());
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AFPriors = calculateAlleleFreqBasedPriors(newAlleleFrequencies);
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for ( PerSampleGenotypeLikelihoods SGL : GLs.values() )
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SGL.setPriors(AFPriors);
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// determine whether we're stable
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double AF_delta = 0.0;
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for (int i = 0; i < 4; i++) {
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AF_delta += Math.abs(alleleFrequencies[i] - newAlleleFrequencies[i]);
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logger.debug("Previous allele frequency for " + BaseUtils.baseIndexToSimpleBase(i) + ": " + alleleFrequencies[i] + ", vs. new frequency: " + newAlleleFrequencies[i]);
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}
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isStable = AF_delta < EM_STABILITY_VALUE;
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iterations++;
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alleleFrequencies = newAlleleFrequencies;
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}
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logger.debug("EM loop took " + iterations + " iterations");
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if (true)
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throw new RuntimeException("DEBUGGING");
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/*
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ClassicGenotypeLikelihoods[] G = em_result.genotype_likelihoods;
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pD = Compute_pD(G, em_result.sample_weights);
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pNull = Compute_pNull(contexts, em_result.sample_weights);
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// Apply p(f).
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double pVar = 0.0;
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for (int i = 1; i < em_result.EM_N; i++) { pVar += THETA/(double)i; }
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double p0 = Math.log10(1 - pVar);
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double pF;
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double MAF = Compute_alt_freq(ref, em_result.allele_likelihoods);
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if (MAF < 1/(2.0*em_result.EM_N)) { pF = p0; }
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else { pF = Math.log10(THETA/(2.0*em_result.EM_N * MAF)); }
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pD = pD + pF;
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pNull = pNull + p0;
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lod = pD - pNull;
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return lod;
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*/
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// apply prior of theta / MAF
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// It turns out that the appropriate prior is theta, for any sample size from 1 to ~1,000
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//return new SSGenotypeCall(context.getLocation(), ref,gl, pileup);
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return true;
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}
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double[] getPosteriorWeightedFrequencies(Collection<PerSampleGenotypeLikelihoods> SGLs) {
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double[] newAlleleLikelihoods = new double[4];
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for ( PerSampleGenotypeLikelihoods SGL : SGLs ) {
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// calculate the posterior weighted frequencies for this sample
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double[] personalAllelePosteriors = new double[4];
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double sum = 0;
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for ( DiploidGenotype g : DiploidGenotype.values() ) {
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double posterior = Math.pow(10, SGL.totalGL.getPosterior(g));
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personalAllelePosteriors[BaseUtils.simpleBaseToBaseIndex(g.base1)] += posterior;
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personalAllelePosteriors[BaseUtils.simpleBaseToBaseIndex(g.base2)] += posterior;
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sum += 2.0 * posterior;
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}
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// normalize
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for (int i = 0; i < 4; i++)
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personalAllelePosteriors[i] /= sum;
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for (int i = 0; i < 4; i++)
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newAlleleLikelihoods[i] += personalAllelePosteriors[i];
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}
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// normalize
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double sum = 0;
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for (int i = 0; i < 4; i++)
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sum += newAlleleLikelihoods[i];
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for (int i = 0; i < 4; i++)
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newAlleleLikelihoods[i] /= sum;
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return newAlleleLikelihoods;
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}
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private DiploidGenotypePriors calculateAlleleFreqBasedPriors(double[] alleleFrequencies) {
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// convert to log-space
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double[] log10Freqs = new double[4];
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for (int i = 0; i < 4; i++)
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log10Freqs[i] = Math.log10(alleleFrequencies[i]);
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double[] alleleFreqPriors = new double[10];
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// this is the Hardy-Weinberg based allele frequency (p^2, q^2, 2pq)
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for ( DiploidGenotype g : DiploidGenotype.values() ) {
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alleleFreqPriors[g.ordinal()] = log10Freqs[BaseUtils.simpleBaseToBaseIndex(g.base1)] + log10Freqs[BaseUtils.simpleBaseToBaseIndex(g.base2)];
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// add a factor of 2 for the 2pq case
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if ( g.isHet() )
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alleleFreqPriors[g.ordinal()] += Math.log10(2);
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}
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return new DiploidGenotypePriors(alleleFreqPriors);
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}
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private class PerSampleGenotypeLikelihoods {
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GenotypeLikelihoods forwardStrandGL;
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GenotypeLikelihoods reverseStrandGL;
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GenotypeLikelihoods totalGL;
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public PerSampleGenotypeLikelihoods() {
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forwardStrandGL = GenotypeLikelihoodsFactory.makeGenotypeLikelihoods(baseModel, new DiploidGenotypePriors(), defaultPlatform);
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forwardStrandGL.setVerbose(VERBOSE);
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reverseStrandGL = GenotypeLikelihoodsFactory.makeGenotypeLikelihoods(baseModel, new DiploidGenotypePriors(), defaultPlatform);
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reverseStrandGL.setVerbose(VERBOSE);
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}
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public void addObservedBase(char base, SAMRecord read, int offset) {
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if ( !read.getReadNegativeStrandFlag() )
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forwardStrandGL.add(base, read.getBaseQualities()[offset], read, offset);
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else
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reverseStrandGL.add(base, read.getBaseQualities()[offset], read, offset);
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}
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public void createGLs(DiploidGenotypePriors priors) {
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setPriors(priors);
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// GL_i = GL+_i + GL-_i
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totalGL = GenotypeLikelihoods.combineLikelihoods(forwardStrandGL, reverseStrandGL);
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totalGL.setVerbose(VERBOSE);
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if ( VERBOSE )
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totalGL.validate();
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}
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public void setPriors(DiploidGenotypePriors priors) {
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if ( forwardStrandGL != null )
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forwardStrandGL.setPriors(priors);
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if ( reverseStrandGL != null )
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reverseStrandGL.setPriors(priors);
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if ( totalGL != null )
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totalGL.setPriors(priors);
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}
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}
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}
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@ -4,38 +4,31 @@ import org.broadinstitute.sting.gatk.contexts.AlignmentContext;
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import org.broadinstitute.sting.gatk.refdata.RefMetaDataTracker;
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import org.broadinstitute.sting.utils.*;
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import org.broadinstitute.sting.utils.genotype.GenotypeWriter;
|
||||
import org.apache.log4j.Logger;
|
||||
|
||||
import java.util.Set;
|
||||
|
||||
|
||||
public class SingleSampleGenotypeCalculationModel extends GenotypeCalculationModel {
|
||||
|
||||
private CallResult callsMetrics;
|
||||
private CallResult callsMetrics = new CallResult();
|
||||
|
||||
public SingleSampleGenotypeCalculationModel(BaseMismatchModel baseModel,
|
||||
Set<String> samples,
|
||||
EmpiricalSubstitutionGenotypeLikelihoods.SequencerPlatform platform,
|
||||
GenotypeWriter out,
|
||||
boolean genotypeMode,
|
||||
double lod,
|
||||
int maxDeletions,
|
||||
boolean verbose) {
|
||||
super(baseModel, samples, platform, out, genotypeMode, lod, maxDeletions, verbose);
|
||||
protected SingleSampleGenotypeCalculationModel() {}
|
||||
|
||||
public void initialize(BaseMismatchModel baseModel,
|
||||
Set<String> samples,
|
||||
EmpiricalSubstitutionGenotypeLikelihoods.SequencerPlatform platform,
|
||||
GenotypeWriter out,
|
||||
Logger logger,
|
||||
boolean genotypeMode,
|
||||
double lod,
|
||||
int maxDeletions,
|
||||
boolean verbose) {
|
||||
super.initialize(baseModel, samples, platform, out, logger, genotypeMode, lod, maxDeletions, verbose);
|
||||
|
||||
// check that this truly is a single-sample bam
|
||||
if ( samples.size() > 1 )
|
||||
throw new StingException("Single-sample genotype calculation model used on multi-sample bam... aborting");
|
||||
|
||||
callsMetrics = new CallResult();
|
||||
}
|
||||
|
||||
/**
|
||||
* Cloning of the object
|
||||
* @return clone
|
||||
* @throws CloneNotSupportedException
|
||||
*/
|
||||
protected Object clone() throws CloneNotSupportedException {
|
||||
return super.clone();
|
||||
}
|
||||
|
||||
public boolean calculateGenotype(RefMetaDataTracker tracker, char ref, AlignmentContext context, DiploidGenotypePriors priors) {
|
||||
|
|
|
|||
|
|
@ -95,7 +95,7 @@ public class UnifiedGenotyper extends LocusWalker<Integer, Integer> {
|
|||
else
|
||||
writer = GenotypeWriterFactory.create(VAR_FORMAT, GenomeAnalysisEngine.instance.getSAMFileHeader(), out);
|
||||
|
||||
gcm = GenotypeCalculationModelFactory.makeGenotypeCalculation(UAC, samples, writer);
|
||||
gcm = GenotypeCalculationModelFactory.makeGenotypeCalculation(UAC, samples, writer, logger);
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
|
|||
Loading…
Reference in New Issue