Merge pull request #1575 from broadinstitute/snf_ReadGMMReport
Read model report and reconstruct GMM from it in VQSR
This commit is contained in:
Samuel Friedman
GitHub
commit
00b7135afe
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@ -86,6 +86,11 @@ public class GaussianMixtureModel {
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gaussians = new ArrayList<>( numGaussians );
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for( int iii = 0; iii < numGaussians; iii++ ) {
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final MultivariateGaussian gaussian = new MultivariateGaussian( numAnnotations );
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gaussian.pMixtureLog10 = Math.log10( 1.0 / ((double)numGaussians) );
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gaussian.sumProb = 1.0 / ((double) numGaussians);
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gaussian.hyperParameter_a = priorCounts;
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gaussian.hyperParameter_b = shrinkage;
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gaussian.hyperParameter_lambda = dirichletParameter;
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gaussians.add( gaussian );
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}
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this.shrinkage = shrinkage;
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@ -105,6 +110,11 @@ public class GaussianMixtureModel {
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this.shrinkage = shrinkage;
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this.dirichletParameter = dirichletParameter;
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this.priorCounts = priorCounts;
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for( final MultivariateGaussian gaussian : gaussians ) {
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gaussian.hyperParameter_a = priorCounts;
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gaussian.hyperParameter_b = shrinkage;
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gaussian.hyperParameter_lambda = dirichletParameter;
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}
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empiricalMu = new double[numAnnotations];
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empiricalSigma = new Matrix(numAnnotations, numAnnotations);
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isModelReadyForEvaluation = false;
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@ -271,4 +271,5 @@ public class MultivariateGaussian {
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resetPVarInGaussian(); // clean up some memory
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}
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}
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@ -277,7 +277,7 @@ public class VariantDataManager {
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}
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}
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logger.info( "Training with worst " + trainingData.size() + " scoring variants --> variants with LOD <= " + String.format("%.4f", VRAC.BAD_LOD_CUTOFF) + "." );
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logger.info( "Selected worst " + trainingData.size() + " scoring variants --> variants with LOD <= " + String.format("%.4f", VRAC.BAD_LOD_CUTOFF) + "." );
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return trainingData;
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}
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@ -66,6 +66,7 @@ import org.broadinstitute.gatk.utils.R.RScriptExecutor;
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import org.broadinstitute.gatk.utils.Utils;
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import org.broadinstitute.gatk.utils.help.HelpConstants;
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import org.broadinstitute.gatk.utils.report.GATKReport;
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import org.broadinstitute.gatk.utils.report.GATKReportColumn;
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import org.broadinstitute.gatk.utils.report.GATKReportTable;
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import org.broadinstitute.gatk.utils.variant.GATKVariantContextUtils;
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import htsjdk.variant.vcf.VCFHeader;
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@ -80,10 +81,15 @@ import htsjdk.variant.variantcontext.writer.VariantContextWriter;
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import java.io.File;
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import java.io.FileNotFoundException;
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import java.io.PrintStream;
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import java.nio.file.Files;
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import java.util.*;
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import Jama.Matrix;
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import java.io.FileWriter;
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import java.io.BufferedWriter;
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import java.io.IOException;
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/**
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* Build a recalibration model to score variant quality for filtering purposes
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*
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@ -272,10 +278,12 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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* to help describe the normalization. The model fit report can be read in with our R gsalib package. Individual
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* model Gaussians can be subset by the value in the "Gaussian" column if desired.
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*/
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@Argument(fullName="output_model", shortName = "outputModel", doc="If specified, the variant recalibrator will output the VQSR model fit to the file specified by -modelFile or to stdout", required=false)
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private boolean outputModel = false;
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@Output(fullName="model_file", shortName = "modelFile", doc="A GATKReport containing the positive and negative model fits", required=false)
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protected PrintStream modelReport = null;
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@Argument(fullName="output_model", shortName = "outputModel", doc="If specified, the variant recalibrator will output the VQSR model to this file path.", required=false)
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private String outputModel = null;
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@Argument(fullName="input_model", shortName = "inputModel", doc="If specified, the variant recalibrator will read the VQSR model from this file path.", required=false)
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private String inputModel = "";
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//@Output(fullName="model_file", shortName = "modelFile", doc="A GATKReport containing the positive and negative model fits", required=false)
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//protected PrintStream modelReport = null;
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@Hidden
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@Argument(fullName="replicate", shortName="replicate", doc="Used to debug the random number generation inside the VQSR. Do not use.", required=false)
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@ -311,6 +319,8 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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private PrintStream tranchesStream;
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private final Set<String> ignoreInputFilterSet = new TreeSet<>();
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private final VariantRecalibratorEngine engine = new VariantRecalibratorEngine( VRAC );
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private GaussianMixtureModel goodModel = null;
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private GaussianMixtureModel badModel = null;
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//---------------------------------------------------------------------------------------------------------------
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//
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@ -348,6 +358,28 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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throw new UserException.CommandLineException( "No truth set found! Please provide sets of known polymorphic loci marked with the truth=true ROD binding tag. For example, -resource:hapmap,VCF,known=false,training=true,truth=true,prior=12.0 hapmapFile.vcf" );
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}
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final File inputFile = new File(inputModel);
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if (inputFile.exists()) { // Load GMM from a file
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logger.info("Loading model from:" + inputModel);
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final GATKReport reportIn = new GATKReport(inputFile);
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// Read all the tables
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final GATKReportTable nmcTable = reportIn.getTable("NegativeModelCovariances");
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final GATKReportTable nmmTable = reportIn.getTable("NegativeModelMeans");
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final GATKReportTable nPMixTable = reportIn.getTable("BadGaussianPMix");
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final GATKReportTable pmcTable = reportIn.getTable("PositiveModelCovariances");
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final GATKReportTable pmmTable = reportIn.getTable("PositiveModelMeans");
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final GATKReportTable pPMixTable = reportIn.getTable("GoodGaussianPMix");
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final int numAnnotations = dataManager.annotationKeys.size();
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if( numAnnotations != pmmTable.getNumColumns()-1 || numAnnotations != nmmTable.getNumColumns()-1 ) { // -1 because the first column is the gaussian number.
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throw new UserException.CommandLineException( "Annotations specified on the command line do not match annotations in the model report." );
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}
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goodModel = GMMFromTables(pmmTable, pmcTable, pPMixTable, numAnnotations);
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badModel = GMMFromTables(nmmTable, nmcTable, nPMixTable, numAnnotations);
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}
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final Set<VCFHeaderLine> hInfo = new HashSet<>();
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ApplyRecalibration.addVQSRStandardHeaderLines(hInfo);
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recalWriter.writeHeader( new VCFHeader(hInfo) );
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@ -359,8 +391,11 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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// collect the actual rod bindings into a list for use later
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for ( final RodBindingCollection<VariantContext> inputCollection : inputCollections )
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input.addAll(inputCollection.getRodBindings());
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}
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//---------------------------------------------------------------------------------------------------------------
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//
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// map
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@ -479,24 +514,37 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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dataManager.setData(reduceSum);
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dataManager.normalizeData(); // Each data point is now (x - mean) / standard deviation
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// Generate the positive model using the training data and evaluate each variant
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final List<VariantDatum> positiveTrainingData = dataManager.getTrainingData();
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final GaussianMixtureModel goodModel = engine.generateModel(positiveTrainingData, VRAC.MAX_GAUSSIANS);
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engine.evaluateData(dataManager.getData(), goodModel, false);
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final List<VariantDatum> negativeTrainingData;
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if (goodModel != null && badModel != null){ // GMMs were loaded from a file
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logger.info("Using serialized GMMs from file...");
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engine.evaluateData(dataManager.getData(), goodModel, false);
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negativeTrainingData = dataManager.selectWorstVariants();
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} else { // Generate the GMMs from scratch
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// Generate the positive model using the training data and evaluate each variant
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goodModel = engine.generateModel(positiveTrainingData, VRAC.MAX_GAUSSIANS);
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engine.evaluateData(dataManager.getData(), goodModel, false);
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// Generate the negative model using the worst performing data and evaluate each variant contrastively
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final List<VariantDatum> negativeTrainingData = dataManager.selectWorstVariants();
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final GaussianMixtureModel badModel = engine.generateModel(negativeTrainingData, Math.min(VRAC.MAX_GAUSSIANS_FOR_NEGATIVE_MODEL, VRAC.MAX_GAUSSIANS));
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dataManager.dropAggregateData(); // Don't need the aggregate data anymore so let's free up the memory
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engine.evaluateData(dataManager.getData(), badModel, true);
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negativeTrainingData = dataManager.selectWorstVariants();
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badModel = engine.generateModel(negativeTrainingData, Math.min(VRAC.MAX_GAUSSIANS_FOR_NEGATIVE_MODEL, VRAC.MAX_GAUSSIANS));
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if (badModel.failedToConverge || goodModel.failedToConverge) {
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throw new UserException("NaN LOD value assigned. Clustering with this few variants and these annotations is unsafe. Please consider " + (badModel.failedToConverge ? "raising the number of variants used to train the negative model (via --minNumBadVariants 5000, for example)." : "lowering the maximum number of Gaussians allowed for use in the model (via --maxGaussians 4, for example)."));
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}
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if (outputModel) {
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}
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dataManager.dropAggregateData(); // Don't need the aggregate data anymore so let's free up the memory
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engine.evaluateData(dataManager.getData(), badModel, true);
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if (outputModel != null) {
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try (PrintStream modelReporter = new PrintStream(outputModel)) {
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GATKReport report = writeModelReport(goodModel, badModel, USE_ANNOTATIONS);
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report.print(modelReport);
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report.print(modelReporter);
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} catch (FileNotFoundException e){
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throw new UserException("Could not open output model file:" + outputModel);
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}
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}
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engine.calculateWorstPerformingAnnotation(dataManager.getData(), goodModel, badModel);
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@ -537,8 +585,91 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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}
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}
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/**
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* Rebuild a Gaussian Mixture Model from gaussian means and co-variates stored in a GATKReportTables
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* @param muTable Table of Gaussian means
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* @param sigmaTable Table of Gaussian co-variates
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* @param pmixTable Table of PMixLog10 values
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* @param numAnnotations Number of annotations, i.e. Dimension of the annotation space in which the Gaussians live
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* @return a GaussianMixtureModel whose state reflects the state recorded in the tables.
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*/
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protected GaussianMixtureModel GMMFromTables(final GATKReportTable muTable, final GATKReportTable sigmaTable, final GATKReportTable pmixTable, final int numAnnotations){
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List<MultivariateGaussian> gaussianList = new ArrayList<>();
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int curAnnotation = 0;
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for (GATKReportColumn reportColumn : muTable.getColumnInfo() ) {
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if (!reportColumn.getColumnName().equals("Gaussian")) {
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for (int row = 0; row < muTable.getNumRows(); row++) {
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if (gaussianList.size() <= row){
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MultivariateGaussian mg = new MultivariateGaussian(numAnnotations);
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gaussianList.add(mg);
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}
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gaussianList.get(row).mu[curAnnotation] = (Double) muTable.get(row, reportColumn.getColumnName());
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}
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curAnnotation++;
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}
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}
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for (GATKReportColumn reportColumn : pmixTable.getColumnInfo() ) {
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if (reportColumn.getColumnName().equals("pMixLog10")) {
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for (int row = 0; row < pmixTable.getNumRows(); row++) {
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gaussianList.get(row).pMixtureLog10 = (Double) pmixTable.get(row, reportColumn.getColumnName());
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}
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}
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}
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int curJ = 0;
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for (GATKReportColumn reportColumn : sigmaTable.getColumnInfo() ) {
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if (reportColumn.getColumnName().equals("Gaussian")) continue;
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if (reportColumn.getColumnName().equals("Annotation")) continue;
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for (int row = 0; row < sigmaTable.getNumRows(); row++) {
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int curGaussian = row / numAnnotations;
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int curI = row % numAnnotations;
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double curVal = (Double) sigmaTable.get(row, reportColumn.getColumnName());
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gaussianList.get(curGaussian).sigma.set(curI, curJ, curVal);
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}
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curJ++;
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}
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return new GaussianMixtureModel(gaussianList, VRAC.SHRINKAGE, VRAC.DIRICHLET_PARAMETER, VRAC.PRIOR_COUNTS);
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}
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private double[] getStandardDeviationsFromTable(GATKReportTable astdTable){
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double[] stdVector = {};
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for (GATKReportColumn reportColumn : astdTable.getColumnInfo() ) {
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if (reportColumn.getColumnName().equals("Standarddeviation")) {
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stdVector = new double[astdTable.getNumRows()];
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for (int row = 0; row < astdTable.getNumRows(); row++) {
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stdVector[row] = (Double) astdTable.get(row, reportColumn.getColumnName());
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}
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}
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}
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return stdVector;
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}
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private double[] getMeansFromTable(GATKReportTable amTable){
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double[] meanVector = {};
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for (GATKReportColumn reportColumn : amTable.getColumnInfo() ) {
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if (reportColumn.getColumnName().equals("Mean")) {
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meanVector = new double[amTable.getNumRows()];
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for (int row = 0; row < amTable.getNumRows(); row++) {
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meanVector[row] = (Double) amTable.get(row, reportColumn.getColumnName());
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}
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}
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}
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return meanVector;
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}
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protected GATKReport writeModelReport(final GaussianMixtureModel goodModel, final GaussianMixtureModel badModel, List<String> annotationList) {
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final String formatString = "%.3f";
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final String formatString = "%.8E";
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final GATKReport report = new GATKReport();
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if (dataManager != null) { //for unit test
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@ -551,6 +682,30 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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report.addTable(annotationVariances);
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}
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List<String> gaussianStrings = new ArrayList<>();
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final double[] pMixtureLog10s = new double[goodModel.getModelGaussians().size()];
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int idx = 0;
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for( final MultivariateGaussian gaussian : goodModel.getModelGaussians() ) {
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pMixtureLog10s[idx] = gaussian.pMixtureLog10;
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gaussianStrings.add(Integer.toString(idx++) );
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}
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GATKReportTable goodPMix = makeVectorTable("GoodGaussianPMix", "Pmixture log 10 used to evaluate model", gaussianStrings, pMixtureLog10s, "pMixLog10", formatString, "Gaussian");
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report.addTable(goodPMix);
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gaussianStrings.clear();
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final double[] pMixtureLog10sBad = new double[badModel.getModelGaussians().size()];
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idx = 0;
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for( final MultivariateGaussian gaussian : badModel.getModelGaussians() ) {
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pMixtureLog10sBad[idx] = gaussian.pMixtureLog10;
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gaussianStrings.add(Integer.toString(idx++));
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}
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GATKReportTable badPMix = makeVectorTable("BadGaussianPMix", "Pmixture log 10 used to evaluate model", gaussianStrings, pMixtureLog10sBad, "pMixLog10", formatString, "Gaussian");
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report.addTable(badPMix);
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//The model and Gaussians don't know what the annotations are, so get them from this class
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//VariantDataManager keeps the annotation in the same order as the argument list
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GATKReportTable positiveMeans = makeMeansTable("PositiveModelMeans", "Vector of annotation values to describe the (normalized) mean for each Gaussian in the positive model", annotationList, goodModel, formatString);
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@ -570,8 +725,12 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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}
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protected GATKReportTable makeVectorTable(final String tableName, final String tableDescription, final List<String> annotationList, final double[] perAnnotationValues, final String columnName, final String formatString) {
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return makeVectorTable(tableName, tableDescription, annotationList, perAnnotationValues, columnName, formatString, "Annotation");
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}
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protected GATKReportTable makeVectorTable(final String tableName, final String tableDescription, final List<String> annotationList, final double[] perAnnotationValues, final String columnName, final String formatString, final String firstColumn) {
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GATKReportTable vectorTable = new GATKReportTable(tableName, tableDescription, annotationList.size(), GATKReportTable.TableSortingWay.DO_NOT_SORT);
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vectorTable.addColumn("Annotation");
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vectorTable.addColumn(firstColumn);
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vectorTable.addColumn(columnName, formatString);
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for (int i = 0; i < perAnnotationValues.length; i++) {
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vectorTable.addRowIDMapping(annotationList.get(i), i, true);
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@ -98,6 +98,7 @@ public class VariantRecalibratorEngine {
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try {
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model.precomputeDenominatorForEvaluation();
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} catch( Exception e ) {
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logger.warn("Model could not pre-compute denominators.");
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model.failedToConverge = true;
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return;
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}
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@ -107,6 +108,7 @@ public class VariantRecalibratorEngine {
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for( final VariantDatum datum : data ) {
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final double thisLod = evaluateDatum( datum, model );
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if( Double.isNaN(thisLod) ) {
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logger.warn("Evaluate datum returned a NaN.");
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model.failedToConverge = true;
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return;
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}
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@ -142,7 +144,7 @@ public class VariantRecalibratorEngine {
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// Private Methods used for generating a GaussianMixtureModel
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/////////////////////////////
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private void variationalBayesExpectationMaximization( final GaussianMixtureModel model, final List<VariantDatum> data ) {
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protected void variationalBayesExpectationMaximization(final GaussianMixtureModel model, final List<VariantDatum> data) {
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model.initializeRandomModel( data, VRAC.NUM_KMEANS_ITERATIONS );
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@ -51,127 +51,130 @@
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package org.broadinstitute.gatk.tools.walkers.variantrecalibration;
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import static org.testng.Assert.*;
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import Jama.Matrix;
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import org.apache.commons.lang.StringUtils;
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import org.apache.log4j.Logger;
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import org.broadinstitute.gatk.utils.BaseTest;
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import org.broadinstitute.gatk.utils.report.GATKReport;
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import org.broadinstitute.gatk.utils.report.GATKReportTable;
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import org.testng.Assert;
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import org.testng.annotations.Test;
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import java.io.File;
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import java.io.FileNotFoundException;
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import java.io.PrintStream;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Random;
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public class VariantRecalibratorModelOutputUnitTest {
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public class VariantRecalibratorModelOutputUnitTest extends BaseTest {
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protected final static Logger logger = Logger.getLogger(VariantRecalibratorModelOutputUnitTest.class);
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private final boolean printTables = true;
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private final int numAnnotations = 6;
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private final double shrinkage = 1.0;
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private final double dirichlet = 0.001;
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private final double priorCounts = 20.0;
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private final double epsilon = 1e-6;
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private final String modelReportName = "vqsr_model.report";
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@Test
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public void testVQSRModelOutput() {
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final int numAnnotations = 6;
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final double shrinkage = 1.0;
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final double dirichlet = 0.001;
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final double priorCounts = 20.0;
|
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final int numGoodGaussians = 2;
|
||||
final int numBadGaussians = 1;
|
||||
final double epsilon = 1e-6;
|
||||
|
||||
Random rand = new Random(12878);
|
||||
MultivariateGaussian goodGaussian1 = new MultivariateGaussian(numAnnotations);
|
||||
goodGaussian1.initializeRandomMu(rand);
|
||||
goodGaussian1.initializeRandomSigma(rand);
|
||||
|
||||
MultivariateGaussian goodGaussian2 = new MultivariateGaussian(numAnnotations);
|
||||
goodGaussian2.initializeRandomMu(rand);
|
||||
goodGaussian2.initializeRandomSigma(rand);
|
||||
|
||||
MultivariateGaussian badGaussian1 = new MultivariateGaussian(numAnnotations);
|
||||
badGaussian1.initializeRandomMu(rand);
|
||||
badGaussian1.initializeRandomSigma(rand);
|
||||
|
||||
List<MultivariateGaussian> goodGaussianList = new ArrayList<>();
|
||||
goodGaussianList.add(goodGaussian1);
|
||||
goodGaussianList.add(goodGaussian2);
|
||||
|
||||
List<MultivariateGaussian> badGaussianList = new ArrayList<>();
|
||||
badGaussianList.add(badGaussian1);
|
||||
|
||||
GaussianMixtureModel goodModel = new GaussianMixtureModel(goodGaussianList, shrinkage, dirichlet, priorCounts);
|
||||
GaussianMixtureModel badModel = new GaussianMixtureModel(badGaussianList, shrinkage, dirichlet, priorCounts);
|
||||
GaussianMixtureModel goodModel = getGoodGMM();
|
||||
GaussianMixtureModel badModel = getBadGMM();
|
||||
|
||||
if (printTables) {
|
||||
System.out.println("Good model mean matrix:");
|
||||
System.out.println(vectorToString(goodGaussian1.mu));
|
||||
System.out.println(vectorToString(goodGaussian2.mu));
|
||||
System.out.println(vectorToString(goodModel.getModelGaussians().get(0).mu));
|
||||
System.out.println(vectorToString(goodModel.getModelGaussians().get(1).mu));
|
||||
System.out.println("\n\n");
|
||||
|
||||
System.out.println("Good model covariance matrices:");
|
||||
goodGaussian1.sigma.print(10, 3);
|
||||
goodGaussian2.sigma.print(10, 3);
|
||||
goodModel.getModelGaussians().get(0).sigma.print(10, 3);
|
||||
goodModel.getModelGaussians().get(1).sigma.print(10, 3);
|
||||
System.out.println("\n\n");
|
||||
|
||||
System.out.println("Bad model mean matrix:\n");
|
||||
System.out.println(vectorToString(badGaussian1.mu));
|
||||
System.out.println(vectorToString(badModel.getModelGaussians().get(0).mu));
|
||||
System.out.println("\n\n");
|
||||
|
||||
System.out.println("Bad model covariance matrix:");
|
||||
badGaussian1.sigma.print(10, 3);
|
||||
badModel.getModelGaussians().get(0).sigma.print(10, 3);
|
||||
}
|
||||
|
||||
VariantRecalibrator vqsr = new VariantRecalibrator();
|
||||
List<String> annotationList = new ArrayList<>();
|
||||
annotationList.add("QD");
|
||||
annotationList.add("MQ");
|
||||
annotationList.add("FS");
|
||||
annotationList.add("SOR");
|
||||
annotationList.add("ReadPosRankSum");
|
||||
annotationList.add("MQRankSum");
|
||||
|
||||
List<String> annotationList = getAnnotationList();
|
||||
|
||||
GATKReport report = vqsr.writeModelReport(goodModel, badModel, annotationList);
|
||||
if(printTables)
|
||||
report.print(System.out);
|
||||
if(printTables) {
|
||||
try {
|
||||
PrintStream modelReporter = new PrintStream(this.privateTestDir+this.modelReportName);
|
||||
report.print(modelReporter);
|
||||
} catch (FileNotFoundException e) {
|
||||
e.printStackTrace();
|
||||
}
|
||||
}
|
||||
|
||||
//Check values for Gaussian means
|
||||
GATKReportTable goodMus = report.getTable("PositiveModelMeans");
|
||||
for(int i = 0; i < annotationList.size(); i++) {
|
||||
Assert.assertEquals(goodGaussian1.mu[i], (Double)goodMus.get(0,annotationList.get(i)), epsilon);
|
||||
Assert.assertEquals(goodModel.getModelGaussians().get(0).mu[i], (Double)goodMus.get(0,annotationList.get(i)), epsilon);
|
||||
}
|
||||
for(int i = 0; i < annotationList.size(); i++) {
|
||||
Assert.assertEquals(goodGaussian2.mu[i], (Double)goodMus.get(1,annotationList.get(i)), epsilon);
|
||||
Assert.assertEquals(goodModel.getModelGaussians().get(1).mu[i], (Double)goodMus.get(1,annotationList.get(i)), epsilon);
|
||||
}
|
||||
|
||||
GATKReportTable badMus = report.getTable("NegativeModelMeans");
|
||||
for(int i = 0; i < annotationList.size(); i++) {
|
||||
Assert.assertEquals(badGaussian1.mu[i], (Double)badMus.get(0,annotationList.get(i)), epsilon);
|
||||
Assert.assertEquals(badModel.getModelGaussians().get(0).mu[i], (Double)badMus.get(0,annotationList.get(i)), epsilon);
|
||||
}
|
||||
|
||||
//Check values for Gaussian covariances
|
||||
GATKReportTable goodSigma = report.getTable("PositiveModelCovariances");
|
||||
for(int i = 0; i < annotationList.size(); i++) {
|
||||
for(int j = 0; j < annotationList.size(); j++) {
|
||||
Assert.assertEquals(goodGaussian1.sigma.get(i,j), (Double)goodSigma.get(i,annotationList.get(j)), epsilon);
|
||||
Assert.assertEquals(goodModel.getModelGaussians().get(0).sigma.get(i,j), (Double)goodSigma.get(i,annotationList.get(j)), epsilon);
|
||||
}
|
||||
}
|
||||
|
||||
//add annotationList.size() to row indexes for second Gaussian because the matrices are concatenated by row in the report
|
||||
for(int i = 0; i < annotationList.size(); i++) {
|
||||
for(int j = 0; j < annotationList.size(); j++) {
|
||||
Assert.assertEquals(goodGaussian2.sigma.get(i,j), (Double)goodSigma.get(annotationList.size()+i,annotationList.get(j)), epsilon);
|
||||
Assert.assertEquals(goodModel.getModelGaussians().get(1).sigma.get(i,j), (Double)goodSigma.get(annotationList.size()+i,annotationList.get(j)), epsilon);
|
||||
}
|
||||
}
|
||||
|
||||
GATKReportTable badSigma = report.getTable("NegativeModelCovariances");
|
||||
for(int i = 0; i < annotationList.size(); i++) {
|
||||
for(int j = 0; j < annotationList.size(); j++) {
|
||||
Assert.assertEquals(badGaussian1.sigma.get(i,j), (Double)badSigma.get(i,annotationList.get(j)), epsilon);
|
||||
Assert.assertEquals(badModel.getModelGaussians().get(0).sigma.get(i,j), (Double)badSigma.get(i,annotationList.get(j)), epsilon);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@Test
|
||||
public void testVQSRModelInput(){
|
||||
final File inputFile = new File(this.privateTestDir + this.modelReportName);
|
||||
final GATKReport report = new GATKReport(inputFile);
|
||||
|
||||
// Now test model report reading
|
||||
// Read all the tables
|
||||
final GATKReportTable badMus = report.getTable("NegativeModelMeans");
|
||||
final GATKReportTable badSigma = report.getTable("NegativeModelCovariances");
|
||||
final GATKReportTable nPMixTable = report.getTable("BadGaussianPMix");
|
||||
|
||||
final GATKReportTable goodMus = report.getTable("PositiveModelMeans");
|
||||
final GATKReportTable goodSigma = report.getTable("PositiveModelCovariances");
|
||||
final GATKReportTable pPMixTable = report.getTable("GoodGaussianPMix");
|
||||
|
||||
List<String> annotationList = getAnnotationList();
|
||||
VariantRecalibrator vqsr = new VariantRecalibrator();
|
||||
|
||||
GaussianMixtureModel goodModelFromFile = vqsr.GMMFromTables(goodMus, goodSigma, pPMixTable, annotationList.size());
|
||||
GaussianMixtureModel badModelFromFile = vqsr.GMMFromTables(badMus, badSigma, nPMixTable, annotationList.size());
|
||||
|
||||
testGMMsForEquality(getGoodGMM(), goodModelFromFile, epsilon);
|
||||
testGMMsForEquality(getBadGMM(), badModelFromFile, epsilon);
|
||||
}
|
||||
|
||||
@Test
|
||||
//This is tested separately to avoid setting up a VariantDataManager and populating it with fake data
|
||||
public void testAnnotationNormalizationOutput() {
|
||||
|
|
@ -211,4 +214,66 @@ public class VariantRecalibratorModelOutputUnitTest {
|
|||
return returnString;
|
||||
}
|
||||
|
||||
private void testGMMsForEquality(GaussianMixtureModel gmm1, GaussianMixtureModel gmm2, double epsilon){
|
||||
Assert.assertEquals(gmm1.getModelGaussians().size(), gmm2.getModelGaussians().size(), 0);
|
||||
|
||||
for(int k = 0; k < gmm1.getModelGaussians().size(); k++) {
|
||||
final MultivariateGaussian g = gmm1.getModelGaussians().get(k);
|
||||
final MultivariateGaussian gFile = gmm2.getModelGaussians().get(k);
|
||||
|
||||
Assert.assertEquals(g.pMixtureLog10, gFile.pMixtureLog10);
|
||||
|
||||
for(int i = 0; i < g.mu.length; i++){
|
||||
Assert.assertEquals(g.mu[i], gFile.mu[i], epsilon);
|
||||
}
|
||||
|
||||
for(int i = 0; i < g.sigma.getRowDimension(); i++) {
|
||||
for (int j = 0; j < g.sigma.getColumnDimension(); j++) {
|
||||
Assert.assertEquals(g.sigma.get(i, j), gFile.sigma.get(i, j), epsilon);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private List<String> getAnnotationList(){
|
||||
List<String> annotationList = new ArrayList<>();
|
||||
annotationList.add("QD");
|
||||
annotationList.add("MQ");
|
||||
annotationList.add("FS");
|
||||
annotationList.add("SOR");
|
||||
annotationList.add("ReadPosRankSum");
|
||||
annotationList.add("MQRankSum");
|
||||
return annotationList;
|
||||
}
|
||||
|
||||
private GaussianMixtureModel getGoodGMM(){
|
||||
Random rand = new Random(12878);
|
||||
MultivariateGaussian goodGaussian1 = new MultivariateGaussian(numAnnotations);
|
||||
goodGaussian1.initializeRandomMu(rand);
|
||||
goodGaussian1.initializeRandomSigma(rand);
|
||||
|
||||
MultivariateGaussian goodGaussian2 = new MultivariateGaussian(numAnnotations);
|
||||
goodGaussian2.initializeRandomMu(rand);
|
||||
goodGaussian2.initializeRandomSigma(rand);
|
||||
|
||||
List<MultivariateGaussian> goodGaussianList = new ArrayList<>();
|
||||
goodGaussianList.add(goodGaussian1);
|
||||
goodGaussianList.add(goodGaussian2);
|
||||
|
||||
return new GaussianMixtureModel(goodGaussianList, shrinkage, dirichlet, priorCounts);
|
||||
}
|
||||
|
||||
private GaussianMixtureModel getBadGMM(){
|
||||
Random rand = new Random(12878);
|
||||
MultivariateGaussian badGaussian1 = new MultivariateGaussian(numAnnotations);
|
||||
|
||||
badGaussian1.initializeRandomMu(rand);
|
||||
badGaussian1.initializeRandomSigma(rand);
|
||||
|
||||
List<MultivariateGaussian> badGaussianList = new ArrayList<>();
|
||||
badGaussianList.add(badGaussian1);
|
||||
|
||||
return new GaussianMixtureModel(badGaussianList, shrinkage, dirichlet, priorCounts);
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -150,10 +150,8 @@ public class GATKReportTable {
|
|||
// read a data line
|
||||
final String dataLine = reader.readLine();
|
||||
final List<String> lineSplits = Arrays.asList(TextFormattingUtils.splitFixedWidth(dataLine, columnStarts));
|
||||
|
||||
underlyingData.add(new Object[nColumns]);
|
||||
for ( int columnIndex = 0; columnIndex < nColumns; columnIndex++ ) {
|
||||
|
||||
final GATKReportDataType type = columnInfo.get(columnIndex).getDataType();
|
||||
final String columnName = columnNames[columnIndex];
|
||||
set(i, columnName, type.Parse(lineSplits.get(columnIndex)));
|
||||
|
|
|
|||
Loading…
Reference in New Issue