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misc cleanup in VQSR 

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5732 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
rpoplin 2011-05-03 18:00:22 +00:00
parent f3bd11a02e
commit 6323fb8673
5 changed files with 14 additions and 23 deletions
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7
java/src/org/broadinstitute/sting/playground/gatk/walkers/variantrecalibration/ContrastiveRecalibrator.java
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@ -210,7 +210,7 @@ public class ContrastiveRecalibrator extends RodWalker<ExpandingArrayList<Varian
public void onTraversalDone( final ExpandingArrayList<VariantDatum> reduceSum ) {
dataManager.setData( reduceSum );
dataManager.normalizeData();
dataManager.normalizeData(); // Each data point is now (x - mean) / standard deviation
final GaussianMixtureModel goodModel = engine.generateModel( dataManager.getTrainingData() );
engine.evaluateData( dataManager.getData(), goodModel, false );
final GaussianMixtureModel badModel = engine.generateModel( dataManager.selectWorstVariants( VRAC.PERCENT_BAD_VARIANTS ) );
@ -250,12 +250,10 @@ public class ContrastiveRecalibrator extends RodWalker<ExpandingArrayList<Varian
createArrangeFunction( stream );
stream.println("pdf(\"" + RSCRIPT_FILE + ".pdf\")");
stream.println("pdf(\"" + RSCRIPT_FILE + ".pdf\")"); // Unfortunately this is a huge pdf file, BUGBUG: need to work on reducing the file size
for(int iii = 0; iii < USE_ANNOTATIONS.length; iii++) {
for( int jjj = iii + 1; jjj < USE_ANNOTATIONS.length; jjj++) {
//stream.println("png(\"" + RSCRIPT_FILE + "." + USE_ANNOTATIONS[iii] + "." + USE_ANNOTATIONS[jjj] + ".png\", type=\"cairo\", width = 960, height = 960)");
//stream.println("pdf(\"" + RSCRIPT_FILE + "." + USE_ANNOTATIONS[iii] + "." + USE_ANNOTATIONS[jjj] + ".pdf\")");
logger.info( "Building " + USE_ANNOTATIONS[iii] + " x " + USE_ANNOTATIONS[jjj] + " plot...");
final ExpandingArrayList<VariantDatum> fakeData = new ExpandingArrayList<VariantDatum>();
@ -266,6 +264,7 @@ public class ContrastiveRecalibrator extends RodWalker<ExpandingArrayList<Varian
minAnn2 = Math.min(minAnn2, datum.annotations[jjj]);
maxAnn2 = Math.max(maxAnn2, datum.annotations[jjj]);
}
// Create a fake set of data which spans the full extent of these two annotation dimensions in order to calculate the model PDF projected to 2D
for(double ann1 = minAnn1; ann1 <= maxAnn1; ann1+=0.1) {
for(double ann2 = minAnn2; ann2 <= maxAnn2; ann2+=0.1) {
final VariantDatum datum = new VariantDatum();

8
java/src/org/broadinstitute/sting/playground/gatk/walkers/variantrecalibration/GaussianMixtureModel.java
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@ -42,9 +42,6 @@ public class GaussianMixtureModel {
empiricalMu = new double[numAnnotations];
empiricalSigma = new Matrix(numAnnotations, numAnnotations);
isModelReadyForEvaluation = false;
}
public void cacheEmpiricalStats() {
Arrays.fill(empiricalMu, 0.0);
empiricalSigma.setMatrix(0, empiricalMu.length - 1, 0, empiricalMu.length - 1, Matrix.identity(empiricalMu.length, empiricalMu.length).times(200.0).inverse());
}
@ -75,6 +72,7 @@ public class GaussianMixtureModel {
int ttt = 0;
while( ttt++ < numIterations ) {
// Estep: assign each variant to the nearest cluster
for( final VariantDatum datum : data ) {
double minDistance = Double.MAX_VALUE;
MultivariateGaussian minGaussian = null;
@ -89,6 +87,7 @@ public class GaussianMixtureModel {
datum.assignment = minGaussian;
}
// Mstep: update gaussian means based on assigned variants
for( final MultivariateGaussian gaussian : gaussians ) {
gaussian.zeroOutMu();
int numAssigned = 0;
@ -188,7 +187,7 @@ public class GaussianMixtureModel {
for( final MultivariateGaussian gaussian : gaussians ) {
pVarInGaussianLog10[gaussianIndex++] = gaussian.pMixtureLog10 + gaussian.evaluateDatumLog10( datum );
}
return MathUtils.log10sumLog10(pVarInGaussianLog10);
return MathUtils.log10sumLog10(pVarInGaussianLog10); // Sum(pi_k * p(v|n,k))
}
public double evaluateDatumMarginalized( final VariantDatum datum ) {
@ -197,6 +196,7 @@ public class GaussianMixtureModel {
int numIter = 10;
final double[] pVarInGaussianLog10 = new double[gaussians.size()];
for( int iii = 0; iii < datum.annotations.length; iii++ ) {
// marginalize over the missing dimension by drawing X random values for the missing annotation and averaging the lod
if( datum.isNull[iii] ) {
for( int ttt = 0; ttt < numIter; ttt++ ) {
datum.annotations[iii] = Normal.staticNextDouble(0.0, 1.0);

1
java/src/org/broadinstitute/sting/playground/gatk/walkers/variantrecalibration/MultivariateGaussian.java
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@ -94,6 +94,7 @@ public class MultivariateGaussian {
}
public void precomputeDenominatorForVariationalBayes( final double sumHyperParameterLambda ) {
// Variational Bayes calculations from Bishop
cachedSigmaInverse = sigma.inverse();
cachedSigmaInverse.timesEquals( hyperParameter_a );
double sum = 0.0;

2
java/src/org/broadinstitute/sting/playground/gatk/walkers/variantrecalibration/VariantDataManager.java
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@ -73,7 +73,7 @@ public class VariantDataManager {
throw new UserException.BadInput( "Found annotations with zero variance. They must be excluded before proceeding." );
}
// trim data by standard deviation threshold and place into two sets: data and failingData
// 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 ) {

19
java/src/org/broadinstitute/sting/playground/gatk/walkers/variantrecalibration/VariantRecalibratorEngine.java
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@ -29,7 +29,6 @@ public class VariantRecalibratorEngine {
public VariantRecalibratorEngine( final VariantRecalibratorArgumentCollection VRAC ) {
this.VRAC = VRAC;
initialize( this.VRAC );
}
public GaussianMixtureModel generateModel( final List<VariantDatum> data ) {
@ -50,36 +49,28 @@ public class VariantRecalibratorEngine {
}
}
/////////////////////////////
// Private Methods used for initialization
/////////////////////////////
private void initialize( final VariantRecalibratorArgumentCollection VRAC ) {
}
/////////////////////////////
// Private Methods used for generating a GaussianMixtureModel
/////////////////////////////
private void variationalBayesExpectationMaximization( final GaussianMixtureModel model, final List<VariantDatum> data ) {
model.cacheEmpiricalStats();
model.initializeRandomModel( data, VRAC.NUM_KMEANS_ITERATIONS );
// The VBEM loop
model.normalizePMixtureLog10();
model.expectationStep( data );
double currentLikelihood;
double currentChangeInMixtureCoefficients;
int iteration = 0;
logger.info("Finished iteration " + iteration );
while( iteration < VRAC.MAX_ITERATIONS ) {
iteration++;
model.maximizationStep( data );
currentLikelihood = model.normalizePMixtureLog10();
model.expectationStep( data );
logger.info("Current change in mixture coefficients = " + String.format("%.5f", currentLikelihood));
currentChangeInMixtureCoefficients = model.normalizePMixtureLog10();
model.expectationStep(data);
logger.info("Current change in mixture coefficients = " + String.format("%.5f", currentChangeInMixtureCoefficients));
logger.info("Finished iteration " + iteration );
if( iteration > 2 && currentLikelihood < MIN_PROB_CONVERGENCE ) {
if( iteration > 2 && currentChangeInMixtureCoefficients < MIN_PROB_CONVERGENCE ) {
logger.info("Convergence!");
break;
}