AnalyzeAnnotations creates a plot of dbsnp rate as a function of the annotations.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@2711 348d0f76-0448-11de-a6fe-93d51630548a
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rpoplin
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@ -7,13 +7,8 @@ input = args[1]
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annotationName = args[2]
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minBinCutoff = as.numeric(args[3])
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c <- read.table(input, header=T)
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#
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# Plot TiTv ratio as a function of the annotation
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#
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all = c[c$numVariants>minBinCutoff & c$category=="all",]
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novel = c[c$numVariants>minBinCutoff & c$category=="novel",]
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dbsnp = c[c$numVariants>minBinCutoff & c$category=="dbsnp",]
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@ -24,10 +19,14 @@ ymax = max(d$titv)
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xmin = min(d$value)
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xmax = max(d$value)
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#
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# Plot TiTv ratio as a function of the annotation
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#
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outfile = paste(input, ".TiTv.", annotationName, ".pdf", sep="")
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pdf(outfile, height=7, width=7)
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par(cex=1.1)
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plot(all$value,all$titv,xlab=annotationName,ylab="Ti/Tv ratio",pch=20,ylim=c(ymin,ymax),xaxt="n",ps=14);
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plot(all$value,all$titv,xlab=annotationName,ylab="Ti/Tv Ratio",pch=20,ylim=c(ymin,ymax),xaxt="n",ps=14);
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axis(1,axTicks(1), format(axTicks(1), scientific=F))
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m = weighted.mean(all$value,all$numVariants/sum(all$numVariants))
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ma = all[all$value > m,]
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@ -42,10 +41,14 @@ points(dbsnp$value,dbsnp$titv,col="blue",pch=20)
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legend("topleft", c("all","novel","dbsnp"),col=c("black","green","blue"),pch=c(20,20,20))
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dev.off()
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#
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# Plot TiTv ratio as a function of the annotation, log scale on the x-axis
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#
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outfile = paste(input, ".TiTv_log.", annotationName, ".pdf", sep="")
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pdf(outfile, height=7, width=7)
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par(cex=1.1)
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plot(all$value,all$titv,xlab=annotationName,log="x",ylab="Ti/Tv ratio",pch=20,ylim=c(ymin,ymax),xaxt="n",ps=14);
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plot(all$value,all$titv,xlab=annotationName,log="x",ylab="Ti/Tv Ratio",pch=20,ylim=c(ymin,ymax),xaxt="n",ps=14);
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axis(1,axTicks(1), format(axTicks(1), scientific=F))
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abline(v=m,lty=2)
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abline(v=m75,lty=2)
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@ -55,10 +58,46 @@ points(dbsnp$value,dbsnp$titv,col="blue",pch=20)
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legend("topleft", c("all","novel","dbsnp"),col=c("black","green","blue"),pch=c(20,20,20))
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dev.off()
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#
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# Plot dbsnp rate as a function of the annotation
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#
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outfile = paste(input, ".dbsnpRate.", annotationName, ".pdf", sep="")
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pdf(outfile, height=7, width=7)
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par(cex=1.1)
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plot(all$value,all$dbsnp,xlab=annotationName,ylab="DBsnp Rate",pch=20,xaxt="n",ps=14);
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axis(1,axTicks(1), format(axTicks(1), scientific=F))
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m = weighted.mean(all$value,all$numVariants/sum(all$numVariants))
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ma = all[all$value > m,]
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mb = all[all$value < m,]
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m75 = weighted.mean(ma$value,ma$numVariants/sum(ma$numVariants))
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m25 = weighted.mean(mb$value,mb$numVariants/sum(mb$numVariants))
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abline(v=m,lty=2)
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abline(v=m75,lty=2)
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abline(v=m25,lty=2)
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dev.off()
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#
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# Plot dbsnp rate as a function of the annotation, log scale on the x-axis
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#
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outfile = paste(input, ".dbsnpRate_log.", annotationName, ".pdf", sep="")
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pdf(outfile, height=7, width=7)
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par(cex=1.1)
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plot(all$value,all$dbsnp,xlab=annotationName,log="x",ylab="DBsnp Rate",pch=20,xaxt="n",ps=14);
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axis(1,axTicks(1), format(axTicks(1), scientific=F))
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abline(v=m,lty=2)
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abline(v=m75,lty=2)
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abline(v=m25,lty=2)
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dev.off()
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#
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# Plot histogram of the annotation's value
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#
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outfile = paste(input, "TiTv_hist.", annotationName, ".pdf", sep="")
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pdf(outfile, height=7, width=7)
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par(cex=1.1)
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plot(all$value,all$numVariants,xlab=annotationName,ylab="num variants in bin",type="h",xaxt="n",ps=14);
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plot(all$value,all$numVariants,xlab=annotationName,ylab="Num variants in bin",type="h",xaxt="n",ps=14);
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axis(1,axTicks(1), format(axTicks(1), scientific=F))
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dev.off()
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@ -73,7 +73,7 @@ public class AnnotationDataManager {
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data.put( annotationKey, treeSet );
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}
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AnnotationDatum datum = new AnnotationDatum( value );
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if( treeSet.contains(datum) ) { // contains uses AnnotationDatum's equals function, so it only checks if the value field is already present
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if( treeSet.contains(datum) ) { // contains() uses AnnotationDatum's equals function, so it only checks if the value field is already present
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datum = treeSet.tailSet(datum).first();
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} else {
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treeSet.add(datum);
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@ -95,39 +95,42 @@ public class AnnotationDataManager {
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final int MIN_VARIANTS_PER_BIN, final int MAX_VARIANTS_PER_BIN ) {
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final AnnotationDatum thisAnnotationBin = new AnnotationDatum();
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System.out.println( "\nExecuting RScript commands:" );
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System.out.println( "\nFinished reading variants into memory. Executing RScript commands:" );
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// For each annotation we've seen
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for( String annotationKey : data.keySet() ) {
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PrintStream output;
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try {
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output = new PrintStream(OUTPUT_PREFIX + annotationKey + ".dat"); // Create the data file for this annotation
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output = new PrintStream(OUTPUT_PREFIX + annotationKey + ".dat"); // Create the intermediate data file for this annotation
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} catch ( FileNotFoundException e ) {
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throw new StingException("Can't create intermediate output annotation data file. Does the output directory exist? " +
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OUTPUT_PREFIX + annotationKey + ".dat");
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}
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// Output a header line
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output.println("value\ttitv\tnumVariants\tcategory");
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output.println("value\ttitv\tdbsnp\tnumVariants\tcategory");
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// Bin SNPs and calculate truth metrics for each bin
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thisAnnotationBin.clearBin();
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for( AnnotationDatum datum : data.get( annotationKey ) ) {
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thisAnnotationBin.combine( datum );
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if( thisAnnotationBin.numVariants( AnnotationDatum.FULL_SET ) >= MAX_VARIANTS_PER_BIN ) { // This annotation bin is full
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.FULL_SET ) + "\t" + thisAnnotationBin.numVariants( AnnotationDatum.FULL_SET ) + "\tall");
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.NOVEL_SET ) + "\t" + thisAnnotationBin.numVariants( AnnotationDatum.NOVEL_SET ) + "\tnovel");
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.DBSNP_SET ) + "\t" + thisAnnotationBin.numVariants( AnnotationDatum.DBSNP_SET ) + "\tdbsnp");
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.FULL_SET ) + "\t" + thisAnnotationBin.calcDBsnpRate() +
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"\t" + thisAnnotationBin.numVariants( AnnotationDatum.FULL_SET ) + "\tall");
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.NOVEL_SET ) + "\t0.0\t" + thisAnnotationBin.numVariants( AnnotationDatum.NOVEL_SET ) + "\tnovel");
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.DBSNP_SET ) + "\t0.0\t" + thisAnnotationBin.numVariants( AnnotationDatum.DBSNP_SET ) + "\tdbsnp");
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thisAnnotationBin.clearBin();
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}
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// else, continue accumulating variants because this bin isn't full yet
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}
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if( thisAnnotationBin.numVariants( AnnotationDatum.FULL_SET ) != 0 ) { // One final bin that may not have been dumped out
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.FULL_SET ) + "\t" + thisAnnotationBin.numVariants( AnnotationDatum.FULL_SET ) + "\tall");
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.NOVEL_SET ) + "\t" + thisAnnotationBin.numVariants( AnnotationDatum.NOVEL_SET ) + "\tnovel");
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.DBSNP_SET ) + "\t" + thisAnnotationBin.numVariants( AnnotationDatum.DBSNP_SET ) + "\tdbsnp");
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.FULL_SET ) + "\t" + thisAnnotationBin.calcDBsnpRate() +
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"\t" + thisAnnotationBin.numVariants( AnnotationDatum.FULL_SET ) + "\tall");
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.NOVEL_SET ) + "\t0.0\t" + thisAnnotationBin.numVariants( AnnotationDatum.NOVEL_SET ) + "\tnovel");
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output.println( thisAnnotationBin.value + "\t" + thisAnnotationBin.calcTiTv( AnnotationDatum.DBSNP_SET ) + "\t0.0\t" + thisAnnotationBin.numVariants( AnnotationDatum.DBSNP_SET ) + "\tdbsnp");
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thisAnnotationBin.clearBin();
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}
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@ -127,13 +127,23 @@ public class AnnotationDatum implements Comparator<AnnotationDatum> {
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}
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}
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final public float calcDBsnpRate() {
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if( ti[NOVEL_SET] + tv[NOVEL_SET] + ti[DBSNP_SET] + tv[DBSNP_SET] == 0 ) { // Don't divide by zero
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return 0.0f;
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}
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return 100.0f * ((float) ti[DBSNP_SET] + tv[DBSNP_SET]) /
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((float) ti[NOVEL_SET] + tv[NOVEL_SET] + ti[DBSNP_SET] + tv[DBSNP_SET]);
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}
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final public float calcTPrate() {
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if( ti[NOVEL_SET] + tv[NOVEL_SET] + ti[DBSNP_SET] + tv[DBSNP_SET] == 0 ) { // Don't divide by zero
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return 0.0f;
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}
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return ((float) ti[TRUTH_SET] + tv[TRUTH_SET]) /
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return 100.0f * ((float) ti[TRUTH_SET] + tv[TRUTH_SET]) /
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((float) ti[NOVEL_SET] + tv[NOVEL_SET] + ti[DBSNP_SET] + tv[DBSNP_SET]);
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}
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