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More UserExceptions and warnings in VQSR. 

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5813 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
rpoplin 2011-05-16 19:03:21 +00:00
parent 541b5f7a80
commit d698c87bbf
2 changed files with 15 additions and 2 deletions
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7
java/src/org/broadinstitute/sting/gatk/walkers/variantrecalibration/VariantDataManager.java
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@ -27,9 +27,10 @@ public class VariantDataManager {
public final ArrayList<String> annotationKeys;
private final ExpandingArrayList<TrainingSet> trainingSets;
private final VariantRecalibratorArgumentCollection VRAC;
private static boolean warnedUserMissingValue = false;
protected final static Logger logger = Logger.getLogger(VariantDataManager.class);
public VariantDataManager( final List<String> annotationKeys, final VariantRecalibratorArgumentCollection VRAC ) {
this.data = null;
this.annotationKeys = new ArrayList<String>( annotationKeys );
@ -214,6 +215,10 @@ public class VariantDataManager {
if(annotationKey.equals("HaplotypeScore") && MathUtils.compareDoubles(value, 0.0, 0.0001) == 0 ) { value = -0.2 + 0.4*GenomeAnalysisEngine.getRandomGenerator().nextDouble(); }
} catch( final Exception e ) {
value = Double.NaN; // The VQSR works with missing data now by marginalizing over the missing dimension when evaluating clusters.
if( !warnedUserMissingValue ) {
logger.warn("WARNING: Missing value detected for " + annotationKey + ". The VQSR will work with missing data by marginalizing over this dimension for this variant. This warning message is only shown once but there may be other annotations missing as well.");
warnedUserMissingValue = true;
}
}
}
return value;

10
java/src/org/broadinstitute/sting/gatk/walkers/variantrecalibration/VariantRecalibratorEngine.java
View File

@ -1,6 +1,7 @@
package org.broadinstitute.sting.gatk.walkers.variantrecalibration;
import org.apache.log4j.Logger;
import org.broadinstitute.sting.utils.exceptions.UserException;
import java.util.List;
@ -45,6 +46,13 @@ public class VariantRecalibratorEngine {
logger.info("Evaluating full set of " + data.size() + " variants...");
for( final VariantDatum datum : data ) {
final double thisLod = evaluateDatum( datum, model );
if( Double.isNaN(thisLod) ) {
if( evaluateContrastively ) {
throw new UserException("NaN LOD value assigned. Clustering with this few variants and these annotations is unsafe. Please consider raising the number of variants used to train the negative model (via --percentBadVariants 0.05, for example) or lowering the maximum number of Gaussians to use in the model (via --maxGaussians 4, for example)");
} else {
throw new UserException("NaN LOD value assigned. Clustering with this few variants and these annotations is unsafe.");
}
}
datum.lod = ( evaluateContrastively ? (datum.prior + datum.lod - thisLod) : thisLod );
}
}
@ -62,7 +70,7 @@ public class VariantRecalibratorEngine {
model.expectationStep( data );
double currentChangeInMixtureCoefficients;
int iteration = 0;
logger.info("Finished iteration " + iteration );
logger.info("Finished iteration " + iteration + ".");
while( iteration < VRAC.MAX_ITERATIONS ) {
iteration++;
model.maximizationStep( data );