A very nice way of automatically plotting the results of a VariantEval run. All of the hard work is actually in the common R repository, gsacommons.R, including methods for creating a Venn diagram. It also provides a mechanism for the output of a VariantEval run to be loaded into a single list object.
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@3828 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
kiran
parent
250ab70fed
commit
b990a22bac
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source(paste(Sys.getenv("STING_DIR"), "/R/gsacommons.R", sep=""));
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args = commandArgs(TRUE);
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evalRoot = args[1];
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#if (is.na(evalRoot)) { evalRoot = "/home/radon01/kiran/scr1/projects/ESPGabrielDownsampling/results/v1/merged.vcf.eval120/eval"; }
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if (is.na(evalRoot)) { evalRoot = "/home/radon01/kiran/scr1/projects/ESPGabrielDownsampling/results/v1/merged.vcf.eval.v2/eval"; }
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plotRoot = args[2];
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if (is.na(plotRoot)) { plotRoot = "plot"; }
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eval = read.eval(evalRoot);
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# Venn diagram
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plot.begin(plotRoot, "venn");
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plot.callsetConcordance(eval);
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plot.end(plotRoot);
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# Venn by AC
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plot.begin(plotRoot, "venn_by_ac.all", width=12, height=8);
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plot.callsetConcordanceByAC(eval, novelty_name="all");
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plot.end(plotRoot);
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plot.begin(plotRoot, "venn_by_ac.known", width=12, height=8);
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plot.callsetConcordanceByAC(eval, novelty_name="known");
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plot.end(plotRoot);
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plot.begin(plotRoot, "venn_by_ac.novel", width=12, height=8);
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plot.callsetConcordanceByAC(eval, novelty_name="novel");
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plot.end(plotRoot);
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# Allele count spectrum
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plot.begin(plotRoot, "acs.all", width=12, height=8);
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plot.alleleCountSpectrum(eval, novelty_name="all");
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plot.end(plotRoot);
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plot.begin(plotRoot, "acs.known", width=12, height=8);
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plot.alleleCountSpectrum(eval, novelty_name="known");
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plot.end(plotRoot);
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plot.begin(plotRoot, "acs.novel", width=12, height=8);
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plot.alleleCountSpectrum(eval, novelty_name="novel");
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plot.end(plotRoot);
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# Ti/Tv spectrum
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plot.begin(plotRoot, "titv.all", width=12, height=8);
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plot.titvSpectrum(eval, novelty_name="all");
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plot.end(plotRoot);
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plot.begin(plotRoot, "titv.known", width=12, height=8);
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plot.titvSpectrum(eval, novelty_name="known");
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plot.end(plotRoot);
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plot.begin(plotRoot, "titv.novel", width=12, height=8);
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plot.titvSpectrum(eval, novelty_name="novel");
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plot.end(plotRoot);
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# Per-sample
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plot.begin(plotRoot, "variants_per_sample", width=12, height=8);
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plot.variantsPerSample(eval);
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plot.end(plotRoot);
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plot.begin <- function(plotRoot, name, width=10, height=10) {
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if (!is.na(plotRoot)) {
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filename = paste(plotRoot, ".", name, ".pdf", sep="");
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print(sprintf("Plotting '%s' to '%s'", name, filename));
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pdf(filename, width=width, height=height);
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} else {
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print(sprintf("Plotting '%s' to X11", name));
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x11(width=width, height=height);
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}
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}
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plot.end <- function(plotRoot) {
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if (!is.na(plotRoot)) {
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dev.off();
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}
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}
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plot.venn <- function(a, b, c=0, a_and_b, a_and_c=0, b_and_c=0,
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col=c("#FF6342", "#63C6DE", "#ADDE63"),
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pos=c(0.20, 0.20, 0.80, 0.82),
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debug=0
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) {
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library(png);
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library(graphics);
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# Set up properties
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for (i in 1:length(col)) {
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rgbcol = col2rgb(col[i]);
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col[i] = sprintf("%02X%02X%02X", rgbcol[1], rgbcol[2], rgbcol[3]);
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}
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chco = paste(col[1], col[2], col[3], sep=",");
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chd = paste(a, b, c, a_and_b, a_and_c, b_and_c, sep=",");
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props = c(
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'cht=v',
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'chs=525x525',
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'chds=0,10000000000',
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paste('chco=', chco, sep=""),
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paste('chd=t:', chd, sep="")
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);
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proplist = paste(props[1], props[2], props[3], props[4], props[5], sep='&');
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# Get the venn diagram (as a temporary file)
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filename = tempfile("venn");
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cmd = paste("wget -O ", filename, " 'http://chart.apis.google.com/chart?", proplist, "' > /dev/null 2>&1", sep="");
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if (debug == 1) {
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print(cmd);
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}
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system(cmd);
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# Render the temp png file into a plotting frame
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a = readPNG(filename);
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plot(0, 0, type="n", xaxt="n", yaxt="n", bty="n", xlim=c(0, 1), ylim=c(0, 1), xlab="", ylab="");
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rasterImage(a, pos[1], pos[2], pos[3], pos[4]);
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# Clean up!
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unlink(filename);
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}
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read.eval <- function(evalRoot) {
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fileAlleleCountStats = paste(evalRoot, ".AlleleCountStats.csv", sep="");
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fileCompOverlap = paste(evalRoot, ".Comp_Overlap.csv", sep="");
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fileCountVariants = paste(evalRoot, ".Count_Variants.csv", sep="");
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fileGenotypeConcordance = paste(evalRoot, ".Genotype_Concordance.csv", sep="");
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fileMetricsByAc = paste(evalRoot, ".MetricsByAc.csv", sep="");
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fileMetricsBySample = paste(evalRoot, ".MetricsBySample.csv", sep="");
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fileQuality_Metrics_by_allele_count = paste(evalRoot, ".Quality_Metrics_by_allele_count.csv", sep="");
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fileQualityScoreHistogram = paste(evalRoot, ".QualityScoreHistogram.csv", sep="");
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fileSampleStatistics = paste(evalRoot, ".Sample_Statistics.csv", sep="");
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fileSampleSummaryStatistics = paste(evalRoot, ".Sample_Summary_Statistics.csv", sep="");
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fileTi_slash_Tv_Variant_Evaluator = paste(evalRoot, ".Ti_slash_Tv_Variant_Evaluator.csv", sep="");
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fileTiTvStats = paste(evalRoot, ".TiTvStats.csv", sep="");
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fileVariant_Quality_Score = paste(evalRoot, ".Variant_Quality_Score.csv", sep="");
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eval = list(
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AlleleCountStats = read.csv(fileAlleleCountStats, header=TRUE, comment.char="#"),
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CompOverlap = read.csv(fileCompOverlap, header=TRUE, comment.char="#"),
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CountVariants = read.csv(fileCountVariants, header=TRUE, comment.char="#"),
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GenotypeConcordance = read.csv(fileGenotypeConcordance, header=TRUE, comment.char="#"),
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MetricsByAc = read.csv(fileMetricsByAc, header=TRUE, comment.char="#"),
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MetricsBySample = read.csv(fileMetricsBySample, header=TRUE, comment.char="#"),
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Quality_Metrics_by_allele_count = read.csv(fileQuality_Metrics_by_allele_count, header=TRUE, comment.char="#"),
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QualityScoreHistogram = read.csv(fileQualityScoreHistogram, header=TRUE, comment.char="#"),
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SampleStatistics = read.csv(fileSampleStatistics, header=TRUE, comment.char="#"),
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SampleSummaryStatistics = read.csv(fileSampleSummaryStatistics, header=TRUE, comment.char="#"),
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TiTv = read.csv(fileTi_slash_Tv_Variant_Evaluator, header=TRUE, comment.char="#"),
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TiTvStats = read.csv(fileTiTvStats, header=TRUE, comment.char="#"),
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Variant_Quality_Score = read.csv(fileVariant_Quality_Score, header=TRUE, comment.char="#"),
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CallsetNames = c(),
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CallsetOnlyNames = c(),
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CallsetFilteredNames = c()
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);
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uniqueJexlExpressions = unique(eval$TiTv$jexl_expression);
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eval$CallsetOnlyNames = as.vector(uniqueJexlExpressions[grep("Filtered|Intersection|none", uniqueJexlExpressions, invert=TRUE, ignore.case=TRUE)]);
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eval$CallsetNames = as.vector(gsub("-only", "", eval$CallsetOnlyNames));
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eval$CallsetFilteredNames = as.vector(c(paste(eval$CallsetNames[1], "-filteredIn", eval$CallsetNames[2], sep=""), paste(eval$CallsetNames[2], "-filteredIn", eval$CallsetNames[1], sep="")));
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eval;
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}
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eval.getMetrics <- function(eval, jexl_expression) {
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callset.counts = eval$CountVariants[which(eval$CountVariants$evaluation_name == "eval" & eval$CountVariants$comparison_name == "dbsnp" & eval$CountVariants$jexl_expression == jexl_expression),];
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callset.counts.titv = eval$TiTv[which(eval$TiTv$evaluation_name == "eval" & eval$TiTv$comparison_name == "dbsnp" & eval$TiTv$jexl_expression == jexl_expression),];
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callset.calledCounts = callset.counts[which(callset.counts$filter_name == "called" & callset.counts$novelty_name == "all"),]$nVariantLoci;
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callset.calledCounts.titv = callset.counts.titv[which(callset.counts.titv$filter_name == "called" & callset.counts.titv$novelty_name == "all"),]$ti.tv.ratio;
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callset.knownCounts = callset.counts[which(callset.counts$filter_name == "called" & callset.counts$novelty_name == "known"),]$nVariantLoci;
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callset.knownCounts.titv = callset.counts.titv[which(callset.counts.titv$filter_name == "called" & callset.counts.titv$novelty_name == "known"),]$ti.tv.ratio;
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callset.novelCounts = callset.counts[which(callset.counts$filter_name == "called" & callset.counts$novelty_name == "novel"),]$nVariantLoci;
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callset.novelCounts.titv = callset.counts.titv[which(callset.counts.titv$filter_name == "called" & callset.counts.titv$novelty_name == "novel"),]$ti.tv.ratio;
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callset.filteredCounts = callset.counts[which(callset.counts$filter_name == "filtered" & callset.counts$novelty_name == "all"),]$nVariantLoci;
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callset.filteredCounts.titv = callset.counts.titv[which(callset.counts.titv$filter_name == "filtered" & callset.counts.titv$novelty_name == "all"),]$ti.tv.ratio;
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metrics = list(
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all = callset.calledCounts,
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all.titv = callset.calledCounts.titv,
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known = callset.knownCounts,
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known.titv = callset.knownCounts.titv,
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novel = callset.novelCounts,
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novel.titv = callset.novelCounts.titv,
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filtered = callset.filteredCounts,
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filtered.titv = callset.filteredCounts.titv
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);
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}
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.plot.callsetConcordance.getLabelText <- function(name, metrics, union) {
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text = sprintf("%s (%0.1f%% of union)\nAll: %d, Ti/Tv: %0.1f\nKnown: %d, Ti/Tv: %0.1f\nNovel: %d, Ti/Tv: %0.1f\n",
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name, 100.0*metrics$all/union$all,
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metrics$all, metrics$all.titv,
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metrics$known, metrics$known.titv,
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metrics$novel, metrics$novel.titv
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);
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}
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plot.callsetConcordance <- function(eval, col=c("#FF6342", "#63C6DE", "#ADDE63")) {
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aonly = eval.getMetrics(eval, eval$CallsetOnlyNames[1]);
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intersection = eval.getMetrics(eval, "Intersection");
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bonly = eval.getMetrics(eval, eval$CallsetOnlyNames[2]);
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union = list(
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all = intersection$all + aonly$all + bonly$all,
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known = intersection$known + aonly$known + bonly$known,
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novel = intersection$novel + aonly$novel + bonly$novel,
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filtered = intersection$filtered + aonly$filtered + bonly$filtered
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);
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par.def = par(no.readonly = TRUE);
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par(mar=c(0.1, 0.1, 0.1, 0.1));
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plot.venn(aonly$all + intersection$all, bonly$all + intersection$all, 0, intersection$all, 0, 0, pos=c(0.32, 0.32, 0.68, 0.70), col=col);
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text(0, 0.5, cex=1.2, pos=4, .plot.callsetConcordance.getLabelText(eval$CallsetNames[1], aonly, union));
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text(0.5, 0.75, cex=1.2, adj=c(0.5, 0.33), .plot.callsetConcordance.getLabelText("Intersection", intersection, union));
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text(1, 0.5, cex=1.2, pos=2, .plot.callsetConcordance.getLabelText(eval$CallsetNames[2], bonly, union));
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par(par.def);
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}
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plot.callsetConcordanceByAC <- function(eval, normalize=TRUE, novelty_name="all", col=c("#FF6342", "#5C92A4", "#55BBFF")) {
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aonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[1]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),];
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intersection = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & eval$MetricsByAc$jexl_expression == "Intersection" & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),];
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bonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[2]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),];
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title = paste("Callset concordance per allele count (", novelty_name, " variants)", sep="");
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if (normalize == TRUE) {
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norm = aonly$n + intersection$n + bonly$n;
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matnorm = rbind(aonly$n/norm, intersection$n/norm, bonly$n/norm);
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barplot(matnorm, col=col, xlab="Allele count", ylab="Fraction", main=title, names.arg=intersection$AC, ylim=c(0, 1.3), border=NA);
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} else {
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mat = rbind(aonly$n, intersection$n, bonly$n);
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barplot(mat, col=col, xlab="Allele count", ylab="counts", main=title, names.arg=intersection$AC, border="gray");
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}
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legend("topright", c(eval$CallsetOnlyNames[1], "Intersection", eval$CallsetOnlyNames[2]), fill=col);
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}
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plot.alleleCountSpectrum <- function(eval, novelty_name="all", col=c("#FF6342", "#5C92A4", "#55BBFF")) {
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aonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[1]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),];
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intersection = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & eval$MetricsByAc$jexl_expression == "Intersection" & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),];
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bonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[2]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),];
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title = paste("Allele count spectrum (", novelty_name, " variants)", sep="");
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suppressWarnings(plot(0, 0, type="n", xlim=c(1, length(intersection$n)), ylim=c(1, max(aonly$n + intersection$n, bonly$n + intersection$n)), xlab="Allele count", ylab="Number of variants", main=title, log="xy", bty="n"));
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suppressWarnings(points(intersection$AC, aonly$n + intersection$n, type="l", lwd=2, col=col[1]));
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suppressWarnings(points(intersection$AC, intersection$n, type="l", lwd=2, col=col[2]));
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suppressWarnings(points(intersection$AC, bonly$n + intersection$n, type="l", lwd=2, col=col[3]));
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legend("topright", c(eval$CallsetOnlyNames[1], "Intersection", eval$CallsetOnlyNames[2]), fill=col);
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}
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plot.titvSpectrum <- function(eval, novelty_name="all", col=c("#FF6342", "#5C92A4", "#55BBFF")) {
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aonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[1]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),];
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intersection = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & eval$MetricsByAc$jexl_expression == "Intersection" & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),];
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bonly = eval$MetricsByAc[which(eval$MetricsByAc$evaluation_name == "eval" & eval$MetricsByAc$comparison_name == "dbsnp" & as.character(eval$MetricsByAc$jexl_expression) == as.character(eval$CallsetOnlyNames[2]) & eval$MetricsByAc$filter_name == "called" & eval$MetricsByAc$novelty_name == novelty_name),];
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title = paste("Ti/Tv spectrum (", novelty_name, " variants)", sep="");
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titv.aonly = (aonly$nTi + intersection$nTi)/(aonly$nTv + intersection$nTv);
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titv.aonly.finite = titv.aonly[which(is.finite(titv.aonly))];
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titv.intersection.finite = intersection$Ti.Tv[which(is.finite(intersection$Ti.Tv))];
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titv.bonly = (bonly$nTi + intersection$nTi)/(bonly$nTv + intersection$nTv);
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titv.bonly.finite = titv.bonly[which(is.finite(titv.bonly))];
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titv.min = min(titv.aonly.finite, titv.intersection.finite, titv.bonly.finite);
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titv.max = max(titv.aonly.finite, titv.intersection.finite, titv.bonly.finite);
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plot(0, 0, type="n", xlim=c(1, length(intersection$n)), ylim=c(titv.min, titv.max), xlab="Allele count", ylab="Transition/transversion (Ti/Tv) ratio", main=title, bty="n");
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points(intersection$AC, (aonly$nTi + intersection$nTi)/(aonly$nTv + intersection$nTv), type="l", lwd=2, col=col[1]);
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points(intersection$AC, intersection$Ti.Tv, type="l", lwd=2, col=col[2]);
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points(intersection$AC, (bonly$nTi + intersection$nTi)/(bonly$nTv + intersection$nTv), type="l", lwd=2, col=col[3]);
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abline(h=2.3, lty=2);
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mtext("2.3", side=4, at=2.3, cex=0.9);
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abline(h=3.3, lty=2);
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mtext("3.3", side=4, at=3.3, cex=0.9);
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legend("topleft", c(eval$CallsetOnlyNames[1], "Intersection", eval$CallsetOnlyNames[2]), fill=col);
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}
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plot.variantsPerSample <- function(eval) {
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metrics.all = eval$MetricsBySample[which(eval$MetricsBySample$evaluation_name == "eval" & eval$MetricsBySample$comparison_name == "dbsnp" & as.character(eval$MetricsBySample$jexl_expression) == "none" & eval$MetricsBySample$filter_name == "called" & eval$MetricsBySample$novelty_name == "all"),];
|
||||
metrics.known = eval$MetricsBySample[which(eval$MetricsBySample$evaluation_name == "eval" & eval$MetricsBySample$comparison_name == "dbsnp" & as.character(eval$MetricsBySample$jexl_expression) == "none" & eval$MetricsBySample$filter_name == "called" & eval$MetricsBySample$novelty_name == "known"),];
|
||||
metrics.novel = eval$MetricsBySample[which(eval$MetricsBySample$evaluation_name == "eval" & eval$MetricsBySample$comparison_name == "dbsnp" & as.character(eval$MetricsBySample$jexl_expression) == "none" & eval$MetricsBySample$filter_name == "called" & eval$MetricsBySample$novelty_name == "novel"),];
|
||||
|
||||
title = "Calls per sample";
|
||||
indices = order(metrics$nVariants, decreasing=TRUE);
|
||||
|
||||
plot(0, 0, type="n", xaxt="n", xlim=c(1, length(metrics$sample)), ylim=c(0, max(metrics$nVariants)), xlab="", ylab="Number of variants", main=title, bty="n");
|
||||
points(c(1:length(metrics.all$sample)), (metrics.all$nVariants)[indices], pch=21, col="black");
|
||||
points(c(1:length(metrics.known$sample)), (metrics.known$nVariants)[indices], pch=21, col="blue");
|
||||
points(c(1:length(metrics.novel$sample)), (metrics.novel$nVariants)[indices], pch=21, col="red");
|
||||
|
||||
legend("topright", c("All", "Known", "Novel"), pch=21, col=c("black", "blue", "red"));
|
||||
|
||||
axis(1, at=c(1:length(metrics$sample)), labels=(metrics$sample)[indices], las=2, cex.axis=0.4);
|
||||
}
|
||||
Loading…
Reference in New Issue