Merge pull request #1247 from broadinstitute/ldg_VQSRmodelOutput
Add optional argument for VQSR to output the model to a file as a GAT…
This commit is contained in:
ldgauthier
commit
1d72ab099c
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@ -58,6 +58,7 @@ import org.broadinstitute.gatk.utils.Utils;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.Collections;
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import java.util.List;
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/**
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@ -70,7 +71,7 @@ public class GaussianMixtureModel {
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protected final static Logger logger = Logger.getLogger(GaussianMixtureModel.class);
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private final ArrayList<MultivariateGaussian> gaussians;
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private final List<MultivariateGaussian> gaussians;
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private final double shrinkage;
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private final double dirichletParameter;
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private final double priorCounts;
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@ -97,6 +98,21 @@ public class GaussianMixtureModel {
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empiricalSigma.setMatrix(0, empiricalMu.length - 1, 0, empiricalMu.length - 1, Matrix.identity(empiricalMu.length, empiricalMu.length).times(200.0).inverse());
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}
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//this is used for the model output unit test
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protected GaussianMixtureModel(final List<MultivariateGaussian> gaussians, final double shrinkage, final double dirichletParameter, final double priorCounts ) {
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this.gaussians = gaussians;
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final int numAnnotations = gaussians.get(0).mu.length;
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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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empiricalMu = new double[numAnnotations];
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empiricalSigma = new Matrix(numAnnotations, numAnnotations);
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isModelReadyForEvaluation = false;
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Arrays.fill(empiricalMu, 0.0);
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empiricalSigma.setMatrix(0, empiricalMu.length - 1, 0, empiricalMu.length - 1, Matrix.identity(empiricalMu.length, empiricalMu.length).times(200.0).inverse());
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}
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public void initializeRandomModel( final List<VariantDatum> data, final int numKMeansIterations ) {
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// initialize random Gaussian means // BUGBUG: this is broken up this way to match the order of calls to rand.nextDouble() in the old code
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@ -295,4 +311,8 @@ public class GaussianMixtureModel {
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}
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return Math.log10( sumPVarInGaussian / ((double) numRandomDraws) );
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}
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protected List<MultivariateGaussian> getModelGaussians() {return Collections.unmodifiableList(gaussians);}
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protected int getNumAnnotations() {return empiricalMu.length;}
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}
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@ -148,6 +148,14 @@ public class VariantDataManager {
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logger.info("Annotations are now ordered by their information content: " + annotationKeys.toString());
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}
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public double[] getMeanVector() {
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return meanVector;
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}
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public double[] getVarianceVector() {
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return varianceVector;
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}
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/**
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* Get a list of indices which give the ascending sort order of the data array
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* @param inputVector the data to consider
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@ -64,6 +64,8 @@ import org.broadinstitute.gatk.utils.QualityUtils;
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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.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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import htsjdk.variant.vcf.VCFHeaderLine;
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@ -79,6 +81,8 @@ import java.io.FileNotFoundException;
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import java.io.PrintStream;
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import java.util.*;
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import Jama.Matrix;
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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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@ -197,6 +201,14 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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@Output(fullName="tranches_file", shortName="tranchesFile", doc="The output tranches file used by ApplyRecalibration", required=true)
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protected File TRANCHES_FILE;
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/**
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* This GATKReport gives information to describe the VQSR model fit. Normalized means for the positive model are concatenated as one table and negative model normalized means as another table.
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* Covariances are also concatenated for postive and negative models, respectively. Tables of annotation means and standard deviations are provided to help describe the normalization.
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* The model fit report can be read in with our R gsalib package. Individual model Gaussians can be subset by the value in the "Gaussian" column if desired.
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*/
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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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/////////////////////////////
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// Additional Command Line Arguments
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/////////////////////////////
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@ -352,7 +364,7 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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datum.isTransition = datum.isSNP && GATKVariantContextUtils.isTransition(vc);
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datum.isAggregate = !isInput;
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// Loop through the training data sets and if they overlap this loci then update the prior and training status appropriately
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// Loop through the training data sets and if they overlap this locus (and allele, if applicable) then update the prior and training status appropriately
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dataManager.parseTrainingSets( tracker, context.getLocation(), vc, datum, TRUST_ALL_POLYMORPHIC );
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final double priorFactor = QualityUtils.qualToProb( datum.prior );
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datum.prior = Math.log10( priorFactor ) - Math.log10( 1.0 - priorFactor );
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@ -414,6 +426,11 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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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 (modelReport != null) {
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GATKReport report = writeModelReport(goodModel, badModel, USE_ANNOTATIONS);
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report.print(modelReport);
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}
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engine.calculateWorstPerformingAnnotation( dataManager.getData(), goodModel, badModel );
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// Find the VQSLOD cutoff values which correspond to the various tranches of calls requested by the user
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@ -443,6 +460,93 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
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}
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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 GATKReport report = new GATKReport();
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if (dataManager != null) { //for unit test
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final double[] meanVector = dataManager.getMeanVector();
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GATKReportTable annotationMeans = makeVectorTable("AnnotationMeans", "Mean for each annotation, used to normalize data", dataManager.annotationKeys, meanVector, "Mean", formatString);
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report.addTable(annotationMeans);
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final double[] varianceVector = dataManager.getVarianceVector(); //"varianceVector" is actually stdev
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GATKReportTable annotationVariances = makeVectorTable("AnnotationStdevs", "Standard deviation for each annotation, used to normalize data", dataManager.annotationKeys, varianceVector, "Standard deviation", formatString);
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report.addTable(annotationVariances);
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}
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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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report.addTable(positiveMeans);
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GATKReportTable positiveCovariance = makeCovariancesTable("PositiveModelCovariances", "Matrix to describe the (normalized) covariance for each Gaussian in the positive model; covariance matrices are joined by row", annotationList, goodModel, formatString);
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report.addTable(positiveCovariance);
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//do the same for the negative model means
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GATKReportTable negativeMeans = makeMeansTable("NegativeModelMeans", "Vector of annotation values to describe the (normalized) mean for each Gaussian in the negative model", annotationList, badModel, formatString);
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report.addTable(negativeMeans);
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GATKReportTable negativeCovariance = makeCovariancesTable("NegativeModelCovariances", "Matrix to describe the (normalized) covariance for each Gaussian in the negative model; covariance matrices are joined by row", annotationList, badModel, formatString);
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report.addTable(negativeCovariance);
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return report;
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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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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(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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vectorTable.set(i, 1, perAnnotationValues[i]);
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}
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return vectorTable;
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}
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private GATKReportTable makeMeansTable(final String tableName, final String tableDescription, final List<String> annotationList, final GaussianMixtureModel model, final String formatString) {
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GATKReportTable meansTable = new GATKReportTable(tableName, tableDescription, annotationList.size(), GATKReportTable.TableSortingWay.DO_NOT_SORT);
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meansTable.addColumn("Gaussian");
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for (final String annotationName : annotationList) {
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meansTable.addColumn(annotationName, formatString);
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}
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final List<MultivariateGaussian> modelGaussians = model.getModelGaussians();
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for (int i = 0; i < modelGaussians.size(); i++) {
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final MultivariateGaussian gaussian = modelGaussians.get(i);
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final double[] meanVec = gaussian.mu;
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if (meanVec.length != annotationList.size())
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throw new IllegalStateException("Gaussian mean vector does not have the same size as the list of annotations");
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meansTable.addRowIDMapping(i, i, true);
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for (int j = 0; j < annotationList.size(); j++)
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meansTable.set(i, annotationList.get(j), meanVec[j]);
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}
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return meansTable;
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}
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private GATKReportTable makeCovariancesTable(final String tableName, final String tableDescription, final List<String> annotationList, final GaussianMixtureModel model, final String formatString) {
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GATKReportTable modelCovariances = new GATKReportTable(tableName, tableDescription, annotationList.size()+2, GATKReportTable.TableSortingWay.DO_NOT_SORT); //+2 is for Gaussian and Annotation columns
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modelCovariances.addColumn("Gaussian");
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modelCovariances.addColumn("Annotation");
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for (final String annotationName : annotationList) {
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modelCovariances.addColumn(annotationName, formatString);
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}
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final List<MultivariateGaussian> modelGaussians = model.getModelGaussians();
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for (int i = 0; i < modelGaussians.size(); i++) {
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final MultivariateGaussian gaussian = modelGaussians.get(i);
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final Matrix covMat = gaussian.sigma;
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if (covMat.getRowDimension() != annotationList.size() || covMat.getColumnDimension() != annotationList.size())
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throw new IllegalStateException("Gaussian covariance matrix does not have the same size as the list of annotations");
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for (int j = 0; j < annotationList.size(); j++) {
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modelCovariances.set(j + i * annotationList.size(), "Gaussian", i);
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modelCovariances.set(j + i * annotationList.size(), "Annotation", annotationList.get(j));
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for (int k = 0; k < annotationList.size(); k++) {
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modelCovariances.set(j + i * annotationList.size(), annotationList.get(k), covMat.get(j, k));
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}
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}
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}
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return modelCovariances;
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}
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private void createVisualizationScript( final List<VariantDatum> randomData, final GaussianMixtureModel goodModel, final GaussianMixtureModel badModel, final double lodCutoff, final String[] annotationKeys ) {
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PrintStream stream;
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try {
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@ -0,0 +1,214 @@
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/*
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* By downloading the PROGRAM you agree to the following terms of use:
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*
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* BROAD INSTITUTE
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* SOFTWARE LICENSE AGREEMENT
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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.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.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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protected final static Logger logger = Logger.getLogger(VariantRecalibratorModelOutputUnitTest.class);
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private final boolean printTables = true;
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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;
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final int numBadGaussians = 1;
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final double epsilon = 1e-6;
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Random rand = new Random(12878);
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MultivariateGaussian goodGaussian1 = new MultivariateGaussian(numAnnotations);
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goodGaussian1.initializeRandomMu(rand);
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goodGaussian1.initializeRandomSigma(rand);
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MultivariateGaussian goodGaussian2 = new MultivariateGaussian(numAnnotations);
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goodGaussian2.initializeRandomMu(rand);
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goodGaussian2.initializeRandomSigma(rand);
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MultivariateGaussian badGaussian1 = new MultivariateGaussian(numAnnotations);
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badGaussian1.initializeRandomMu(rand);
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badGaussian1.initializeRandomSigma(rand);
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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);
|
||||
|
||||
if (printTables) {
|
||||
System.out.println("Good model mean matrix:");
|
||||
System.out.println(vectorToString(goodGaussian1.mu));
|
||||
System.out.println(vectorToString(goodGaussian2.mu));
|
||||
System.out.println("\n\n");
|
||||
|
||||
System.out.println("Good model covariance matrices:");
|
||||
goodGaussian1.sigma.print(10, 3);
|
||||
goodGaussian2.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("\n\n");
|
||||
|
||||
System.out.println("Bad model covariance matrix:");
|
||||
badGaussian1.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");
|
||||
|
||||
|
||||
GATKReport report = vqsr.writeModelReport(goodModel, badModel, annotationList);
|
||||
if(printTables)
|
||||
report.print(System.out);
|
||||
|
||||
//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);
|
||||
}
|
||||
for(int i = 0; i < annotationList.size(); i++) {
|
||||
Assert.assertEquals(goodGaussian2.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);
|
||||
}
|
||||
|
||||
//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);
|
||||
}
|
||||
}
|
||||
|
||||
//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);
|
||||
}
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@Test
|
||||
//This is tested separately to avoid setting up a VariantDataManager and populating it with fake data
|
||||
public void testAnnotationNormalizationOutput() {
|
||||
final VariantRecalibrator vqsr = new VariantRecalibrator();
|
||||
final List<String> annotationList = new ArrayList<>();
|
||||
annotationList.add("QD");
|
||||
annotationList.add("FS");
|
||||
annotationList.add("ReadPosRankSum");
|
||||
annotationList.add("MQ");
|
||||
annotationList.add("MQRankSum");
|
||||
annotationList.add("SOR");
|
||||
|
||||
final double epsilon = 1e-6;
|
||||
|
||||
double[] meanVector = {16.13, 2.45, 0.37, 59.08, 0.14, 0.91};
|
||||
final String columnName = "Mean";
|
||||
final String formatString = "%.3f";
|
||||
GATKReportTable vectorTable = vqsr.makeVectorTable("AnnotationMeans", "Mean for each annotation, used to normalize data", annotationList, meanVector, columnName, formatString);
|
||||
for(int i = 0; i < annotationList.size(); i++) {
|
||||
Assert.assertEquals(meanVector[i], (Double)vectorTable.get(i, columnName), epsilon);
|
||||
}
|
||||
|
||||
if (printTables) {
|
||||
final GATKReport report = new GATKReport();
|
||||
report.addTable(vectorTable);
|
||||
report.print(System.out);
|
||||
}
|
||||
}
|
||||
|
||||
private String vectorToString(double[] meanVec) {
|
||||
String returnString = "";
|
||||
for (int j = 0; j < meanVec.length; j++) {
|
||||
returnString += String.format("%.3f", meanVec[j]);
|
||||
if (j < meanVec.length-1)
|
||||
returnString += ",";
|
||||
}
|
||||
return returnString;
|
||||
}
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue