CalibrateGenotypeLikelihoods now emits a molten data set with REF and ALT alleles, so that GL calibration can be evaluated as a function of the REF/ALT bases. DigestTable is a stand-alone Rscript that digests the multi-GB molten data table into a tiny table that shows reported vs. empirical GLs, as a function of a variety of features of the data, like REF/ALT, comp GT, eval GT, and GL itself.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5833 348d0f76-0448-11de-a6fe-93d51630548a
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depristo
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6a49e8df34
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@ -2,60 +2,27 @@ require("lattice")
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require("ggplot2")
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require("splines")
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READ_DATA = F
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if ( READ_DATA ) {
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d = subset(read.table("~/Dropbox/Analysis/genotypeAccuracy/cgl.hiseq.table", header=T), rg != "ALL")
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d$technology <- factor(1, levels=c("HiSeq-paper", "GA2-1000G", "HiSeq-recent"))
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d$technology[grepl("ERR.*", d$rg)] <- "GA2-1000G"
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d$technology[grepl("20.*", d$rg)] <- "HiSeq-paper"
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d$technology[grepl("B00EG.*", d$rg)] <- "HiSeq-recent"
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print(summary(d$technology))
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#d = read.table("~/Desktop/broadLocal/GATK/trunk/foo", header=T)
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ymax = xmax = 30
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HAVE_RAW_DATA = F
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if ( HAVE_RAW_DATA ) {
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inputDataFile = "~/Dropbox/Analysis/genotypeAccuracy/NA12878.hm3.vcf.cgl.table"
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#inputDataFile = "~/Dropbox/Analysis/genotypeAccuracy/cgl.table.gz"
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r <- digestTable(inputDataFile)
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d = r$d
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eByComp = r$eByComp
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countsByTech = addEmpiricalPofG(ddply(d, .(ref, alt, technology, pGGivenDType, pGGivenD), genotypeCounts))
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print(qplot(pGGivenD, EmpiricalPofGQ, data=subset(countsByTech, technology=="HiSeq-paper" & pGGivenDType == "QofABGivenD"), facets = alt ~ ref, color=alt, geom=c("point"), group=alt, xlim=c(0,xmax), ylim=c(0,ymax))
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+ geom_abline(slope=1, linetype=2))
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# + geom_smooth(se=T, size=1.5, aes(weight=Sum)))
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} else {
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eByComp = read.table("~/Dropbox/Analysis/genotypeAccuracy/NA12878.hm3.vcf.cgl.table.eByComp.tsv", header=T)
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}
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#moltenCD = melt(d, id.vars=c("comp", "rg"), measure.vars=c("QofAAGivenD", "QofABGivenD", "QofBBGivenD"))
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#moltenCD$log10value = round(-10*log10(1-10^moltenCD$value))
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genotypeCounts <- function(x) {
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#print(table(x$comp))
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type = unique(x$variable)[1]
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#print(type)
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t = addmargins(table(x$comp))
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return(t)
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}
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addEmpiricalPofG <- function(d) {
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r = c()
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#
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# TODO -- this is a really naive estimate of the accuracy, as it assumes the comp
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# track is perfect. In reality the chip is at best Q30 accurate (replicate samples have
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# level than this level of concordance). At low incoming confidence, we can effectively
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# ignore this term but when the incoming Q is near or above Q30 this approximation clearly
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# breaks down.
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#
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for ( i in 1:dim(d)[1] ) {
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row = d[i,]
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if ( row$pGGivenDType == "QofAAGivenD" ) v = row$HOM_REF
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if ( row$pGGivenDType == "QofABGivenD" ) v = row$HET
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if ( row$pGGivenDType == "QofBBGivenD" ) v = row$HOM_VAR
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r = c(r, v / row$Sum)
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}
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#print(length(r))
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d$EmpiricalPofG = r
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d$EmpiricalPofGQ = round(-10*log10(1-r))
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return(d)
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}
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eByComp <- addEmpiricalPofG(ddply(d, .(rg, technology, pGGivenDType, pGGivenD), genotypeCounts))
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countsByTech = addEmpiricalPofG(ddply(d, .(technology, pGGivenDType, pGGivenD), genotypeCounts))
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print(subset(countsByTech, pGGivenD > 18 & pGGivenD < 22 & pGGivenDType == "QofABGivenD"))
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#print(subset(countsByTech, pGGivenD > 18 & pGGivenD < 22 & pGGivenDType == "QofABGivenD"))
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#print(subset(eByComp, EmpiricalPofGQ < Inf))
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goodEByComp = subset(eByComp, Sum > 10 & EmpiricalPofGQ < Inf)
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ymax = xmax = 30
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print(qplot(pGGivenD, EmpiricalPofGQ, data=goodEByComp, size=log10(Sum), facets = pGGivenDType ~ technology, color=pGGivenDType, geom=c("point", "smooth"), group=pGGivenDType, xlim=c(0,xmax), ylim=c(0,ymax)) + geom_abline(slope=1, linetype=2))
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print(qplot(pGGivenD, EmpiricalPofGQ, data=goodEByComp, facets = pGGivenDType ~ technology, color=rg, geom=c("blank"), group=rg, xlim=c(0,xmax), ylim=c(0,ymax))
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@ -71,4 +38,3 @@ print(qplot(pGGivenD, EmpiricalPofGQ, data=goodEByComp, facets = pGGivenDType ~
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+ geom_abline(slope=1, linetype=2)
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+ geom_smooth(se=T, size=1.5, aes(weight=Sum)))
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@ -0,0 +1,62 @@
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#!/bin/env Rscript
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require("ggplot2")
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args <- commandArgs(TRUE)
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verbose = TRUE
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inputDataFile = args[1]
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onCmdLine = ! is.na(inputDataFile)
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addEmpiricalPofG <- function(d) {
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r = c()
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#
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# TODO -- this is a really naive estimate of the accuracy, as it assumes the comp
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# track is perfect. In reality the chip is at best Q30 accurate (replicate samples have
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# level than this level of concordance). At low incoming confidence, we can effectively
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# ignore this term but when the incoming Q is near or above Q30 this approximation clearly
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# breaks down.
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#
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for ( i in 1:dim(d)[1] ) {
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row = d[i,]
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if ( row$pGGivenDType == "QofAAGivenD" ) v = row$HOM_REF
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if ( row$pGGivenDType == "QofABGivenD" ) v = row$HET
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if ( row$pGGivenDType == "QofBBGivenD" ) v = row$HOM_VAR
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r = c(r, v / row$Sum)
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}
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#print(length(r))
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d$EmpiricalPofG = r
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d$EmpiricalPofGQ = round(-10*log10(1-r))
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return(d)
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}
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genotypeCounts <- function(x) {
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type = unique(x$variable)[1]
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t = addmargins(table(x$comp))
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return(t)
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}
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digestTable <- function(inputDataFile) {
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d = subset(read.table(inputDataFile, header=T), rg != "ALL")
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d$technology <- factor(1, levels=c("HiSeq-paper", "GA2-1000G", "HiSeq-recent"))
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d$technology[grepl("ERR.*", d$rg)] <- "GA2-1000G"
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d$technology[grepl("20.*", d$rg)] <- "HiSeq-paper"
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d$technology[grepl("B00EG.*", d$rg)] <- "HiSeq-recent"
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print(summary(d$technology))
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eByComp = addEmpiricalPofG(ddply(d, .(rg, technology, pGGivenDType, pGGivenD), genotypeCounts))
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return(list(d=d, eByComp = eByComp))
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#countsByTech = addEmpiricalPofG(ddply(d, .(technology, pGGivenDType, pGGivenD), genotypeCounts))
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}
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writeMyTable <- function(t, name) {
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write.table(t,file=paste(inputDataFile, ".", name, ".tsv", sep=""))
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}
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if ( onCmdLine ) {
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r <- digestTable(inputDataFile)
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writeMyTable(r$eByComp, "eByComp")
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}
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@ -97,6 +97,7 @@ public class CalibrateGenotypeLikelihoods extends RodWalker<CalibrateGenotypeLik
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public static class Datum implements Comparable<Datum> {
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String rgID, sample;
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GenotypeLikelihoods pl;
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String ref, alt;
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VariantContext.Type siteType;
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Genotype.Type genotypeType;
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@ -107,7 +108,9 @@ public class CalibrateGenotypeLikelihoods extends RodWalker<CalibrateGenotypeLik
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return bySample != 0 ? bySample : byRG;
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}
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public Datum(String sample, String rgID, GenotypeLikelihoods pl, VariantContext.Type siteType, Genotype.Type genotypeType) {
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public Datum(String ref, String alt, String sample, String rgID, GenotypeLikelihoods pl, VariantContext.Type siteType, Genotype.Type genotypeType) {
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this.ref = ref;
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this.alt = alt;
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this.sample = sample;
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this.rgID = rgID;
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this.pl = pl;
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@ -196,7 +199,8 @@ public class CalibrateGenotypeLikelihoods extends RodWalker<CalibrateGenotypeLik
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Genotype rgGT = call.getGenotype(sample);
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if ( rgGT != null && ! rgGT.isNoCall() && rgGT.getLikelihoods().getAsVector() != null ) {
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Datum d = new Datum(sample, rgAC.getKey().getReadGroupId(), rgGT.getLikelihoods(), vcComp.getType(), compGT.getType());
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Datum d = new Datum(vcComp.getReference().getBaseString(), vcComp.getAlternateAllele(0).getBaseString(),
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sample, rgAC.getKey().getReadGroupId(), rgGT.getLikelihoods(), vcComp.getType(), compGT.getType());
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data.values.add(d);
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}
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}
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@ -242,7 +246,7 @@ public class CalibrateGenotypeLikelihoods extends RodWalker<CalibrateGenotypeLik
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public void onTraversalDone(Data data) {
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// print the header
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List<String> pGNames = Arrays.asList("QofAAGivenD", "QofABGivenD", "QofBBGivenD");
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List<String> fields = Arrays.asList("sample", "rg", "siteType", "pls", "comp", "pGGivenDType", "pGGivenD");
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List<String> fields = Arrays.asList("sample", "rg", "ref", "alt", "siteType", "pls", "comp", "pGGivenDType", "pGGivenD");
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out.println(Utils.join("\t", fields));
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double[] counts = new double[]{1, 1, 1};
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@ -260,8 +264,8 @@ public class CalibrateGenotypeLikelihoods extends RodWalker<CalibrateGenotypeLik
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for ( int i = 0; i < pGNames.size(); i++ ) {
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int q = QualityUtils.probToQual(pOfGGivenD[i], Math.pow(10.0, -9.9));
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if ( q > 1 ) { // tons of 1s, and not interesting
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out.printf("%s\t%s\t%s\t%s\t%s\t%s\t%d%n",
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d.sample, d.rgID, d.siteType, d.pl.getAsString(), d.genotypeType.toString(),
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out.printf("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%d%n",
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d.sample, d.rgID, d.ref, d.alt, d.siteType, d.pl.getAsString(), d.genotypeType.toString(),
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pGNames.get(i), q);
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}
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}
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