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package org.broadinstitute.gatk.tools.walkers.variantrecalibration;
import org.apache.commons.lang.ArrayUtils;
import org.apache.log4j.Logger;
import org.broadinstitute.gatk.engine.GenomeAnalysisEngine;
import org.broadinstitute.gatk.engine.refdata.RefMetaDataTracker;
import org.broadinstitute.gatk.utils.GenomeLoc;
import org.broadinstitute.gatk.utils.MathUtils;
import htsjdk.variant.vcf.VCFConstants;
import org.broadinstitute.gatk.utils.exceptions.ReviewedGATKException;
import htsjdk.variant.variantcontext.writer.VariantContextWriter;
import org.broadinstitute.gatk.utils.help.HelpConstants;
import org.broadinstitute.gatk.utils.collections.ExpandingArrayList;
import org.broadinstitute.gatk.utils.exceptions.UserException;
import htsjdk.variant.variantcontext.Allele;
import htsjdk.variant.variantcontext.VariantContext;
import htsjdk.variant.variantcontext.VariantContextBuilder;
import java.util.*;
/**
* Created by IntelliJ IDEA.
* User: rpoplin
* Date: Mar 4, 2011
*/
public class VariantDataManager {
private List<VariantDatum> data = Collections.emptyList();
private double[] meanVector;
private double[] varianceVector; // this is really the standard deviation
public List<String> annotationKeys;
private final VariantRecalibratorArgumentCollection VRAC;
protected final static Logger logger = Logger.getLogger(VariantDataManager.class);
protected final List<TrainingSet> trainingSets;
public VariantDataManager( final List<String> annotationKeys, final VariantRecalibratorArgumentCollection VRAC ) {
this.data = Collections.emptyList();
this.annotationKeys = new ArrayList<>( annotationKeys );
this.VRAC = VRAC;
meanVector = new double[this.annotationKeys.size()];
varianceVector = new double[this.annotationKeys.size()];
trainingSets = new ArrayList<>();
}
public void setData( final List<VariantDatum> data ) {
this.data = data;
}
public List<VariantDatum> getData() {
return data;
}
public void normalizeData() {
boolean foundZeroVarianceAnnotation = false;
for( int iii = 0; iii < meanVector.length; iii++ ) {
final double theMean = mean(iii, true);
final double theSTD = standardDeviation(theMean, iii, true);
logger.info( annotationKeys.get(iii) + String.format(": \t mean = %.2f\t standard deviation = %.2f", theMean, theSTD) );
if( Double.isNaN(theMean) ) {
throw new UserException.BadInput("Values for " + annotationKeys.get(iii) + " annotation not detected for ANY training variant in the input callset. VariantAnnotator may be used to add these annotations. See " + HelpConstants.forumPost("discussion/49/using-variant-annotator"));
}
foundZeroVarianceAnnotation = foundZeroVarianceAnnotation || (theSTD < 1E-5);
meanVector[iii] = theMean;
varianceVector[iii] = theSTD;
for( final VariantDatum datum : data ) {
// Transform each data point via: (x - mean) / standard deviation
datum.annotations[iii] = ( datum.isNull[iii] ? 0.1 * GenomeAnalysisEngine.getRandomGenerator().nextGaussian() : ( datum.annotations[iii] - theMean ) / theSTD );
}
}
if( foundZeroVarianceAnnotation ) {
throw new UserException.BadInput( "Found annotations with zero variance. They must be excluded before proceeding." );
}
// trim data by standard deviation threshold and mark failing data for exclusion later
for( final VariantDatum datum : data ) {
boolean remove = false;
for( final double val : datum.annotations ) {
remove = remove || (Math.abs(val) > VRAC.STD_THRESHOLD);
}
datum.failingSTDThreshold = remove;
}
// re-order the data by increasing standard deviation so that the results don't depend on the order things were specified on the command line
// standard deviation over the training points is used as a simple proxy for information content, perhaps there is a better thing to use here
final List<Integer> theOrder = calculateSortOrder(meanVector);
annotationKeys = reorderList(annotationKeys, theOrder);
varianceVector = ArrayUtils.toPrimitive(reorderArray(ArrayUtils.toObject(varianceVector), theOrder));
meanVector = ArrayUtils.toPrimitive(reorderArray(ArrayUtils.toObject(meanVector), theOrder));
for( final VariantDatum datum : data ) {
datum.annotations = ArrayUtils.toPrimitive(reorderArray(ArrayUtils.toObject(datum.annotations), theOrder));
datum.isNull = ArrayUtils.toPrimitive(reorderArray(ArrayUtils.toObject(datum.isNull), theOrder));
}
logger.info("Annotations are now ordered by their information content: " + annotationKeys.toString());
}
/**
* Get a list of indices which give the ascending sort order of the data array
* @param inputVector the data to consider
* @return a non-null list of integers with length matching the length of the input array
*/
protected List<Integer> calculateSortOrder(final double[] inputVector) {
final List<Integer> theOrder = new ArrayList<>(inputVector.length);
final List<MyDoubleForSorting> toBeSorted = new ArrayList<>(inputVector.length);
int count = 0;
for( int iii = 0; iii < inputVector.length; iii++ ) {
toBeSorted.add(new MyDoubleForSorting(-1.0 * Math.abs(inputVector[iii] - mean(iii, false)), count++));
}
Collections.sort(toBeSorted);
for( final MyDoubleForSorting d : toBeSorted ) {
theOrder.add(d.originalIndex); // read off the sort order by looking at the index field
}
return theOrder;
}
// small private class to assist in reading off the new ordering of the annotation array
private class MyDoubleForSorting implements Comparable<MyDoubleForSorting> {
final Double myData;
final int originalIndex;
public MyDoubleForSorting(final double myData, final int originalIndex) {
this.myData = myData;
this.originalIndex = originalIndex;
}
@Override
public int compareTo(final MyDoubleForSorting other) {
return myData.compareTo(other.myData);
}
}
/**
* Convenience connector method to work with arrays instead of lists. See ##reorderList##
*/
private <T> T[] reorderArray(final T[] data, final List<Integer> order) {
return reorderList(Arrays.asList(data), order).toArray(data);
}
/**
* Reorder the given data list to be in the specified order
* @param data the data to reorder
* @param order the new order to use
* @return a reordered list of data
*/
private <T> List<T> reorderList(final List<T> data, final List<Integer> order) {
final List<T> returnList = new ArrayList<>(data.size());
for( final int index : order ) {
returnList.add( data.get(index) );
}
return returnList;
}
/**
* Convert a normalized point to it's original annotation value
*
* norm = (orig - mu) / sigma
* orig = norm * sigma + mu
*
* @param normalizedValue the normalized value of the ith annotation
* @param annI the index of the annotation value
* @return the denormalized value for the annotation
*/
public double denormalizeDatum(final double normalizedValue, final int annI) {
final double mu = meanVector[annI];
final double sigma = varianceVector[annI];
return normalizedValue * sigma + mu;
}
public void addTrainingSet( final TrainingSet trainingSet ) {
trainingSets.add( trainingSet );
}
public List<String> getAnnotationKeys() {
return annotationKeys;
}
public boolean checkHasTrainingSet() {
for( final TrainingSet trainingSet : trainingSets ) {
if( trainingSet.isTraining ) { return true; }
}
return false;
}
public boolean checkHasTruthSet() {
for( final TrainingSet trainingSet : trainingSets ) {
if( trainingSet.isTruth ) { return true; }
}
return false;
}
public List<VariantDatum> getTrainingData() {
final List<VariantDatum> trainingData = new ExpandingArrayList<>();
for( final VariantDatum datum : data ) {
if( datum.atTrainingSite && !datum.failingSTDThreshold ) {
trainingData.add( datum );
}
}
logger.info( "Training with " + trainingData.size() + " variants after standard deviation thresholding." );
if( trainingData.size() < VRAC.MIN_NUM_BAD_VARIANTS ) {
logger.warn( "WARNING: Training with very few variant sites! Please check the model reporting PDF to ensure the quality of the model is reliable." );
} else if( trainingData.size() > VRAC.MAX_NUM_TRAINING_DATA ) {
logger.warn( "WARNING: Very large training set detected. Downsampling to " + VRAC.MAX_NUM_TRAINING_DATA + " training variants." );
Collections.shuffle(trainingData, GenomeAnalysisEngine.getRandomGenerator());
return trainingData.subList(0, VRAC.MAX_NUM_TRAINING_DATA);
}
return trainingData;
}
public List<VariantDatum> selectWorstVariants() {
final List<VariantDatum> trainingData = new ExpandingArrayList<>();
for( final VariantDatum datum : data ) {
if( datum != null && !datum.failingSTDThreshold && !Double.isInfinite(datum.lod) && datum.lod < VRAC.BAD_LOD_CUTOFF ) {
datum.atAntiTrainingSite = true;
trainingData.add( datum );
}
}
logger.info( "Training with worst " + trainingData.size() + " scoring variants --> variants with LOD <= " + String.format("%.4f", VRAC.BAD_LOD_CUTOFF) + "." );
return trainingData;
}
public List<VariantDatum> getEvaluationData() {
final List<VariantDatum> evaluationData = new ExpandingArrayList<>();
for( final VariantDatum datum : data ) {
if( datum != null && !datum.failingSTDThreshold && !datum.atTrainingSite && !datum.atAntiTrainingSite ) {
evaluationData.add( datum );
}
}
return evaluationData;
}
/**
* Remove all VariantDatum's from the data list which are marked as aggregate data
*/
public void dropAggregateData() {
final Iterator<VariantDatum> iter = data.iterator();
while (iter.hasNext()) {
final VariantDatum datum = iter.next();
if( datum.isAggregate ) {
iter.remove();
}
}
}
public List<VariantDatum> getRandomDataForPlotting( final int numToAdd, final List<VariantDatum> trainingData, final List<VariantDatum> antiTrainingData, final List<VariantDatum> evaluationData ) {
final List<VariantDatum> returnData = new ExpandingArrayList<>();
Collections.shuffle(trainingData, GenomeAnalysisEngine.getRandomGenerator());
Collections.shuffle(antiTrainingData, GenomeAnalysisEngine.getRandomGenerator());
Collections.shuffle(evaluationData, GenomeAnalysisEngine.getRandomGenerator());
returnData.addAll(trainingData.subList(0, Math.min(numToAdd, trainingData.size())));
returnData.addAll(antiTrainingData.subList(0, Math.min(numToAdd, antiTrainingData.size())));
returnData.addAll(evaluationData.subList(0, Math.min(numToAdd, evaluationData.size())));
Collections.shuffle(returnData, GenomeAnalysisEngine.getRandomGenerator());
return returnData;
}
protected double mean( final int index, final boolean trainingData ) {
double sum = 0.0;
int numNonNull = 0;
for( final VariantDatum datum : data ) {
if( (trainingData == datum.atTrainingSite) && !datum.isNull[index] ) { sum += datum.annotations[index]; numNonNull++; }
}
return sum / ((double) numNonNull);
}
protected double standardDeviation( final double mean, final int index, final boolean trainingData ) {
double sum = 0.0;
int numNonNull = 0;
for( final VariantDatum datum : data ) {
if( (trainingData == datum.atTrainingSite) && !datum.isNull[index] ) { sum += ((datum.annotations[index] - mean)*(datum.annotations[index] - mean)); numNonNull++; }
}
return Math.sqrt( sum / ((double) numNonNull) );
}
public void decodeAnnotations( final VariantDatum datum, final VariantContext vc, final boolean jitter ) {
final double[] annotations = new double[annotationKeys.size()];
final boolean[] isNull = new boolean[annotationKeys.size()];
int iii = 0;
for( final String key : annotationKeys ) {
isNull[iii] = false;
annotations[iii] = decodeAnnotation( key, vc, jitter );
if( Double.isNaN(annotations[iii]) ) { isNull[iii] = true; }
iii++;
}
datum.annotations = annotations;
datum.isNull = isNull;
}
private static double decodeAnnotation( final String annotationKey, final VariantContext vc, final boolean jitter ) {
double value;
final double LOG_OF_TWO = 0.6931472;
try {
value = vc.getAttributeAsDouble( annotationKey, Double.NaN );
if( Double.isInfinite(value) ) { value = Double.NaN; }
if( jitter && annotationKey.equalsIgnoreCase("HaplotypeScore") && MathUtils.compareDoubles(value, 0.0, 0.01) == 0 ) { value += 0.01 * GenomeAnalysisEngine.getRandomGenerator().nextGaussian(); }
if( jitter && annotationKey.equalsIgnoreCase("FS") && MathUtils.compareDoubles(value, 0.0, 0.01) == 0 ) { value += 0.01 * GenomeAnalysisEngine.getRandomGenerator().nextGaussian(); }
if( jitter && annotationKey.equalsIgnoreCase("InbreedingCoeff") && MathUtils.compareDoubles(value, 0.0, 0.01) == 0 ) { value += 0.01 * GenomeAnalysisEngine.getRandomGenerator().nextGaussian(); }
if( jitter && annotationKey.equalsIgnoreCase("SOR") && MathUtils.compareDoubles(value, LOG_OF_TWO, 0.01) == 0 ) { value += 0.01 * GenomeAnalysisEngine.getRandomGenerator().nextGaussian(); } //min SOR is 2.0, then we take ln
} catch( Exception e ) {
value = Double.NaN; // The VQSR works with missing data by marginalizing over the missing dimension when evaluating the Gaussian mixture model
}
return value;
}
public void parseTrainingSets( final RefMetaDataTracker tracker, final GenomeLoc genomeLoc, final VariantContext evalVC, final VariantDatum datum, final boolean TRUST_ALL_POLYMORPHIC ) {
datum.isKnown = false;
datum.atTruthSite = false;
datum.atTrainingSite = false;
datum.atAntiTrainingSite = false;
datum.prior = 2.0;
for( final TrainingSet trainingSet : trainingSets ) {
for( final VariantContext trainVC : tracker.getValues(trainingSet.rodBinding, genomeLoc) ) {
if( isValidVariant( evalVC, trainVC, TRUST_ALL_POLYMORPHIC ) ) {
datum.isKnown = datum.isKnown || trainingSet.isKnown;
datum.atTruthSite = datum.atTruthSite || trainingSet.isTruth;
datum.atTrainingSite = datum.atTrainingSite || trainingSet.isTraining;
datum.prior = Math.max( datum.prior, trainingSet.prior );
datum.consensusCount += ( trainingSet.isConsensus ? 1 : 0 );
}
if( trainVC != null ) {
datum.atAntiTrainingSite = datum.atAntiTrainingSite || trainingSet.isAntiTraining;
}
}
}
}
private boolean isValidVariant( final VariantContext evalVC, final VariantContext trainVC, final boolean TRUST_ALL_POLYMORPHIC) {
return trainVC != null && trainVC.isNotFiltered() && trainVC.isVariant() && checkVariationClass( evalVC, trainVC ) &&
(TRUST_ALL_POLYMORPHIC || !trainVC.hasGenotypes() || trainVC.isPolymorphicInSamples());
}
protected static boolean checkVariationClass( final VariantContext evalVC, final VariantContext trainVC ) {
switch( trainVC.getType() ) {
case SNP:
case MNP:
return checkVariationClass( evalVC, VariantRecalibratorArgumentCollection.Mode.SNP );
case INDEL:
case MIXED:
case SYMBOLIC:
return checkVariationClass( evalVC, VariantRecalibratorArgumentCollection.Mode.INDEL );
default:
return false;
}
}
protected static boolean checkVariationClass( final VariantContext evalVC, final VariantRecalibratorArgumentCollection.Mode mode ) {
switch( mode ) {
case SNP:
return evalVC.isSNP() || evalVC.isMNP();
case INDEL:
return evalVC.isStructuralIndel() || evalVC.isIndel() || evalVC.isMixed() || evalVC.isSymbolic();
case BOTH:
return true;
default:
throw new ReviewedGATKException( "Encountered unknown recal mode: " + mode );
}
}
public void writeOutRecalibrationTable( final VariantContextWriter recalWriter ) {
// we need to sort in coordinate order in order to produce a valid VCF
Collections.sort( data, new Comparator<VariantDatum>() {
public int compare(VariantDatum vd1, VariantDatum vd2) {
return vd1.loc.compareTo(vd2.loc);
}} );
// create dummy alleles to be used
final List<Allele> alleles = Arrays.asList(Allele.create("N", true), Allele.create("<VQSR>", false));
for( final VariantDatum datum : data ) {
VariantContextBuilder builder = new VariantContextBuilder("VQSR", datum.loc.getContig(), datum.loc.getStart(), datum.loc.getStop(), alleles);
builder.attribute(VCFConstants.END_KEY, datum.loc.getStop());
builder.attribute(VariantRecalibrator.VQS_LOD_KEY, String.format("%.4f", datum.lod));
builder.attribute(VariantRecalibrator.CULPRIT_KEY, (datum.worstAnnotation != -1 ? annotationKeys.get(datum.worstAnnotation) : "NULL"));
if ( datum.atTrainingSite ) builder.attribute(VariantRecalibrator.POSITIVE_LABEL_KEY, true);
if ( datum.atAntiTrainingSite ) builder.attribute(VariantRecalibrator.NEGATIVE_LABEL_KEY, true);
recalWriter.add(builder.make());
}
}
}