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In preparation for Ryan's jumping into SLOD: getting rid of bad hack to ensure P(AF=i) is calculated in the strand-specific cases. With Mark's recent changes this is no longer necessary and just makes the code slower. 

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@4620 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
ebanks 2010-11-03 03:44:59 +00:00
parent 22d64f77ff
commit 4e109f58bf
4 changed files with 7 additions and 24 deletions
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4
java/src/org/broadinstitute/sting/playground/gatk/walkers/genotyper/AlleleFrequencyCalculationModel.java
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@ -81,14 +81,12 @@ public abstract class AlleleFrequencyCalculationModel implements Cloneable {
* @param GLs genotype likelihoods
* @param log10AlleleFrequencyPriors priors
* @param log10AlleleFrequencyPosteriors array (pre-allocated) to store results
* @param minFrequencyToCalculate the minimum frequency which needs to be calculated
*/
protected abstract void getLog10PNonRef(RefMetaDataTracker tracker,
ReferenceContext ref,
Map<String, BiallelicGenotypeLikelihoods> GLs,
double[] log10AlleleFrequencyPriors,
double[] log10AlleleFrequencyPosteriors,
int minFrequencyToCalculate);
double[] log10AlleleFrequencyPosteriors);
/**
* Can be overridden by concrete subclasses

11
java/src/org/broadinstitute/sting/playground/gatk/walkers/genotyper/ExactAFCalculationModel.java
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@ -59,8 +59,7 @@ public class ExactAFCalculationModel extends AlleleFrequencyCalculationModel {
ReferenceContext ref,
Map<String, BiallelicGenotypeLikelihoods> GLs,
double[] log10AlleleFrequencyPriors,
double[] log10AlleleFrequencyPosteriors,
int minFrequencyToCalculate) {
double[] log10AlleleFrequencyPosteriors) {
int numSamples = GLs.size();
@ -148,14 +147,6 @@ public class ExactAFCalculationModel extends AlleleFrequencyCalculationModel {
for (int k=0; k <= numChr; k++)
log10AlleleFrequencyPosteriors[k] = logYMatrix[j][k] + log10AlleleFrequencyPriors[k];
// TODO: we really need to get rid of this and the minFrequencyToCalculate argument
// it's possible that we need to calculate higher frequencies
int maxAlleleFrequencyToTest = numChr;
for (int i = maxAlleleFrequencyToTest; i <= minFrequencyToCalculate; i++)
log10AlleleFrequencyPosteriors[i] = log10AlleleFrequencyPosteriors[maxAlleleFrequencyToTest];
}

10
java/src/org/broadinstitute/sting/playground/gatk/walkers/genotyper/GridSearchAFEstimation.java
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@ -57,8 +57,7 @@ public class GridSearchAFEstimation extends AlleleFrequencyCalculationModel {
ReferenceContext ref,
Map<String, BiallelicGenotypeLikelihoods> GLs,
double[] log10AlleleFrequencyPriors,
double[] log10AlleleFrequencyPosteriors,
int minFrequencyToCalculate) {
double[] log10AlleleFrequencyPosteriors) {
initializeAFMatrix(GLs);
// first, calculate for AF=0 (no change to matrix)
@ -77,17 +76,12 @@ public class GridSearchAFEstimation extends AlleleFrequencyCalculationModel {
// an optimization to speed up the calculation: if we are beyond the local maximum such
// that subsequent likelihoods won't factor into the confidence score, just quit
if ( i >= minFrequencyToCalculate && maxLikelihoodSeen - log10AlleleFrequencyPosteriors[i] > LOG10_OPTIMIZATION_EPSILON )
if ( maxLikelihoodSeen - log10AlleleFrequencyPosteriors[i] > LOG10_OPTIMIZATION_EPSILON )
return;
if ( log10AlleleFrequencyPosteriors[i] > maxLikelihoodSeen )
maxLikelihoodSeen = log10AlleleFrequencyPosteriors[i];
}
// TODO: we really need to get rid of this and the minFrequencyToCalculate argument
// it's possible that we need to calculate higher frequencies
for (int i = maxAlleleFrequencyToTest; i <= minFrequencyToCalculate; i++)
log10AlleleFrequencyPosteriors[i] = log10AlleleFrequencyPosteriors[maxAlleleFrequencyToTest];
}
/**

6
java/src/org/broadinstitute/sting/playground/gatk/walkers/genotyper/UnifiedGenotyperEngine.java
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@ -162,7 +162,7 @@ public class UnifiedGenotyperEngine {
// 'zero' out the AFs (so that we don't have to worry if not all samples have reads at this position)
clearAFarray(log10AlleleFrequencyPosteriors.get());
afcm.get().getLog10PNonRef(tracker, refContext, GLs, log10AlleleFrequencyPriors, log10AlleleFrequencyPosteriors.get(), 0);
afcm.get().getLog10PNonRef(tracker, refContext, GLs, log10AlleleFrequencyPriors, log10AlleleFrequencyPosteriors.get());
// find the most likely frequency
int bestAFguess = MathUtils.maxElementIndex(log10AlleleFrequencyPosteriors.get());
@ -239,7 +239,7 @@ public class UnifiedGenotyperEngine {
GLs.clear();
glcm.get().getLikelihoods(tracker, refContext, stratifiedContexts, StratifiedAlignmentContext.StratifiedContextType.FORWARD, genotypePriors, GLs);
clearAFarray(log10AlleleFrequencyPosteriors.get());
afcm.get().getLog10PNonRef(tracker, refContext, GLs, log10AlleleFrequencyPriors, log10AlleleFrequencyPosteriors.get(), bestAFguess);
afcm.get().getLog10PNonRef(tracker, refContext, GLs, log10AlleleFrequencyPriors, log10AlleleFrequencyPosteriors.get());
//double[] normalizedLog10Posteriors = MathUtils.normalizeFromLog10(log10AlleleFrequencyPosteriors.get(), true);
double forwardLog10PofNull = log10AlleleFrequencyPosteriors.get()[0];
double forwardLog10PofF = MathUtils.log10sum(log10AlleleFrequencyPosteriors.get(), 1);
@ -249,7 +249,7 @@ public class UnifiedGenotyperEngine {
GLs.clear();
glcm.get().getLikelihoods(tracker, refContext, stratifiedContexts, StratifiedAlignmentContext.StratifiedContextType.REVERSE, genotypePriors, GLs);
clearAFarray(log10AlleleFrequencyPosteriors.get());
afcm.get().getLog10PNonRef(tracker, refContext, GLs, log10AlleleFrequencyPriors, log10AlleleFrequencyPosteriors.get(), bestAFguess);
afcm.get().getLog10PNonRef(tracker, refContext, GLs, log10AlleleFrequencyPriors, log10AlleleFrequencyPosteriors.get());
//normalizedLog10Posteriors = MathUtils.normalizeFromLog10(log10AlleleFrequencyPosteriors.get(), true);
double reverseLog10PofNull = log10AlleleFrequencyPosteriors.get()[0];
double reverseLog10PofF = MathUtils.log10sum(log10AlleleFrequencyPosteriors.get(), 1);