FindContaminatingReadGroupsWalker lists read groups in a single-sample BAM file that appear to be contaminants by searching for evidence of systematic underperformance at likely homozygous-variant sites.
Procedure: 1. Sites that are likely homozygous-variant but are called as heterozygous are identified. 2. For each site and read group, we compute the proportion of bases in the pileup supporting an alternate allele. 3. A one-sample, left-tailed t-test is performed with the null hypothesis being that the alternate allele distribution has a mean of 0.95 and the alternate hypothesis being that the true mean is statistically significantly less than expected (pValue < 1e-9). git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@1989 348d0f76-0448-11de-a6fe-93d51630548a
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package org.broadinstitute.sting.playground.gatk.walkers.contamination;
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import org.broadinstitute.sting.gatk.walkers.LocusWalker;
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import org.broadinstitute.sting.gatk.walkers.genotyper.UnifiedGenotyper;
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import org.broadinstitute.sting.gatk.walkers.genotyper.UnifiedArgumentCollection;
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import org.broadinstitute.sting.gatk.walkers.genotyper.GenotypeCalculationModel;
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import org.broadinstitute.sting.gatk.refdata.RefMetaDataTracker;
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import org.broadinstitute.sting.gatk.contexts.ReferenceContext;
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import org.broadinstitute.sting.gatk.contexts.AlignmentContext;
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import org.broadinstitute.sting.utils.genotype.Genotype;
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import org.broadinstitute.sting.utils.genotype.GenotypeLocusData;
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import org.broadinstitute.sting.utils.Pair;
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import org.broadinstitute.sting.utils.BaseUtils;
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import org.broadinstitute.sting.utils.ReadBackedPileup;
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import org.broadinstitute.sting.utils.cmdLine.Argument;
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import org.broadinstitute.sting.playground.utils.NamedTable;
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import net.sf.samtools.SAMRecord;
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import net.sf.samtools.SAMReadGroupRecord;
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import java.util.List;
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import cern.jet.stat.Probability;
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/**
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* FindContaminatingReadGroupsWalker lists read groups in a single-sample BAM file that appear
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* to be contaminants by searching for evidence of systematic underperformance at likely
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* homozygous-variant sites. First, sites that are likely homozygous-variant but are called
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* as heterozygous are identified. Next, per each site and read group, we compute the proportion
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* of bases in the pileup supporting an alternate allele. Finally, a one-sample, left-tailed
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* t-test is performed with the null hypothesis being that the alternate allele distribution has
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* a mean of 0.95 and the alternate hypothesis being that the true mean is statistically
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* significantly less than expected.
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*
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* @author Kiran Garimella
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*/
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public class FindContaminatingReadGroupsWalker extends LocusWalker<Integer, Integer> {
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@Argument(fullName="verbose", shortName="V", doc="Prints information for all loci, not just the suspected contaminating read groups", required=false)
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private Boolean VERBOSE = false;
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@Argument(fullName="balance", shortName="bal", doc="The expected alternate allele balance for homozygous-variant sites", required=false)
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private Double BALANCE = 0.95;
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@Argument(fullName="limit", shortName="lim", doc="The pValue limit for which a read group will be deemed to be a contaminant", required=false)
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private Double LIMIT = 1e-9;
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private UnifiedArgumentCollection uac;
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private UnifiedGenotyper ug;
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private NamedTable altTable;
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public void initialize() {
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uac = new UnifiedArgumentCollection();
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uac.genotypeModel = GenotypeCalculationModel.Model.EM_POINT_ESTIMATE;
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uac.CONFIDENCE_THRESHOLD = 50;
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ug = new UnifiedGenotyper();
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ug.initialize();
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ug.setUnifiedArgumentCollection(uac);
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altTable = new NamedTable();
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}
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/**
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* Identify likely homozygous-variant sites that are called as
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* heterozygous, so that we can isolate our inspection to these sites.
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*
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* @param tracker the meta-data tracker
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* @param ref information regarding the reference
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* @param context information regarding the reads
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* @return true if this site is a suspicious het, false if otherwise
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*/
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public boolean filter(RefMetaDataTracker tracker, ReferenceContext ref, AlignmentContext context) {
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int altCount = 0;
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int totalCount = 0;
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ReadBackedPileup pileup = new ReadBackedPileup(ref.getBase(), context);
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int refIndex = BaseUtils.simpleBaseToBaseIndex(ref.getBase());
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for (byte base : pileup.getBases().getBytes()) {
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int baseIndex = BaseUtils.simpleBaseToBaseIndex((char) base);
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if (baseIndex != refIndex) {
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altCount++;
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}
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totalCount++;
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}
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double altBalance = ((double) altCount)/((double) totalCount);
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if (altBalance > 0.70) {
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Pair<List<Genotype>, GenotypeLocusData> ugResult = ug.map(tracker, ref, context);
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if (ugResult != null && ugResult.first != null) {
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return ugResult.first.get(0).isHet();
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}
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}
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return false;
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}
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/**
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* For each read group represented in the pileup, determine the fraction of bases supporting the alternate allele
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*
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* @param tracker the meta-data tracker
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* @param ref information regarding the reference
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* @param context information regarding the reads
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* @return 1
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*/
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public Integer map(RefMetaDataTracker tracker, ReferenceContext ref, AlignmentContext context) {
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NamedTable alleleCounts = new NamedTable();
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int refIndex = BaseUtils.simpleBaseToBaseIndex(ref.getBase());
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String colName = String.format("%s.%d", context.getContig(), context.getPosition());
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for (int i = 0; i < context.numReads(); i++) {
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SAMRecord read = context.getReads().get(i);
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int offset = context.getOffsets().get(i);
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SAMReadGroupRecord rg = read.getReadGroup();
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int alleleIndex = BaseUtils.simpleBaseToBaseIndex((char) read.getReadBases()[offset]);
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alleleCounts.increment(rg.getReadGroupId(), (alleleIndex == refIndex) ? "ref" : "alt");
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}
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for (String rg : alleleCounts.getRowNames()) {
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double altCount = alleleCounts.get(rg, "alt");
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double refCount = alleleCounts.get(rg, "ref");
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altTable.set(rg, colName, altCount / (altCount + refCount));
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}
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return 1;
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}
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/**
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* Provide an initial value for reduce computations.
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*
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* @return Initial value of reduce.
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*/
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public Integer reduceInit() {
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return null;
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}
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/**
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* Reduces a single map with the accumulator provided as the ReduceType.
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*
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* @param value result of the map.
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* @param sum accumulator for the reduce.
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* @return accumulator with result of the map taken into account.
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*/
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public Integer reduce(Integer value, Integer sum) {
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return null;
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}
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/**
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* Perform the t-test and list the read groups that are significant underperformers.
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*
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* @param result the number of suspicious sites we're inspecting (this argument is ignored)
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*/
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public void onTraversalDone(Integer result) {
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if (VERBOSE) { out.println("#readgroup\tpvalue\tbalances"); }
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for (String rg : altTable.getRowNames()) {
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String balances = "";
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// Compute mean
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double sum = 0.0, total = 0.0;
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for (String locus : altTable.getColumnNames()) {
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double value = altTable.get(rg, locus);
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sum += value;
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total += 1.0;
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balances += String.format("%2.2f,", value);
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}
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double mean = sum/total;
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// Compute stdev
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double squareSumOfMeanDifferences = 0.0;
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for (String locus : altTable.getColumnNames()) {
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double value = altTable.get(rg, locus);
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squareSumOfMeanDifferences += Math.pow(value - mean, 2.0);
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}
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double stdev = Math.sqrt(squareSumOfMeanDifferences/total);
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// Compute standard error of the mean (SEM)
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double sem = stdev/Math.sqrt(total);
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// Compute test statistic t
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double t = (mean - BALANCE) / sem;
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// Degrees of freedom
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double dof = total - 1.0;
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// Compute pValue
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double pValue = Probability.studentT(dof, t);
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if (pValue < LIMIT) {
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out.printf("%s\t%e\t[%s]\n", rg, pValue, balances);
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} else {
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if (VERBOSE) { out.printf("#%s\t%e\t[%s]\n", rg, pValue, balances); }
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}
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}
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}
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}
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