Update to work with latest eval format
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@5719 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
corin
parent
2d81262f87
commit
139ae79d9e
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@ -20,14 +20,13 @@ cmdargs = gsa.getargs(
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bamlist = list(value=NA, doc="list of BAM files"),
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evalroot = list(value=NA, doc="VariantEval root"),
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tearout = list(value=NA, doc="Output path for tearsheet PDF")#,
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#plotout = list(value=NA, doc="Output path for PDF")
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plotout = list(value=NA, doc="Output path for PDF")
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),
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doc="Creates a tearsheet"
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);
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bamlist = scan(cmdargs$bamlist, "character");
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#print(paste("grep SQUID ", cmdargs$yaml, ' |grep "C..." -o', sep=""))
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squids <- system(paste("grep SQUID ", cmdargs$yaml, ' |grep "C..." -o', sep=""), intern=TRUE)
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indexed = c();
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nonindexed = c();
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@ -88,24 +87,22 @@ d2proj = d2[which(d2$"Project" %in% unique(dproj$Project) & d2$"Sample" %in% dpr
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tearsheet<-function(){
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tearsheetdrop <- "data/tearsheetdrop.jpg" #put the path to the tearsheet backdrop here
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tearsheetdrop <- "~Documents/Sting/R/gsalib/data/tearsheetdrop.jpg" #put the path to the tearsheet backdrop here
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pdf(file= cmdargs$tearout, width=22, height=17, pagecentre=TRUE, pointsize=24)
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#define layout
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postable<-matrix(c(1, 1, 1, 1, 1, 1, rep(c(2, 2, 2, 4, 4, 4), 5), rep(c(3, 3, 3, 4, 4, 4), 3), rep(c(3,3,3,5,5,5), 5), 6,6,6,7,7,7), nrow=15, ncol=6, byrow=TRUE)
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layout(postable, heights=c(1, rep(.18, 13), 2), respect=FALSE)
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#prep for title bar
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full<-strsplit(cmdargs$evalroot, "/")
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name<-strsplit(full[[1]][length(full[[1]])], ".",fixed=TRUE)[[1]][1]
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drop<-read.jpeg(system.file(tearsheetdrop, package="gsalib"))
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#plot title bar
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par(mar=c(0,0,0,0))
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plot(drop)
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text(155, 50, name, family="serif", adj=c(0,0), cex=3, col=gray(.25))
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text(155, 50, "NonAutism_Walsh", family="serif", adj=c(0,0), cex=3, col=gray(.25))
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# Project summary
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@ -130,12 +127,12 @@ tearsheet<-function(){
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callable_target = paste(na.omit(unique(dproj$"Target Territory")), collapse=", ");
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table1<-rbind(paste(used_samples," used samples/", unused_samples + used_samples," total samples", sep=""), sequencing_protocol, bait_design, callable_target)
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print(nrow(table1))
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rownames(table1)<-c("Samples","Sequencing Protocol", "Bait Design","Callable Target")
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par(mar=c(0,0,1,0))
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textplot(table1, col.rownames="darkblue", show.colnames=FALSE, cex=1.25, valign="top")
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title(main=sprintf("Project Summary (%s)\n", projects), family="sans", cex.main=1.25, line=-1)
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# Bases summary
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title(main=sprintf("Project Summary (%s)\n", projects), family="sans", cex.main=1.25, line=-1)
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# Bases summary
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reads_per_lane_mean = format(mean(dproj$"PF Reads (HS)", na.rm=TRUE), 8, 3,1, scientific=TRUE);
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reads_per_lane_sd = format(sd(dproj$"PF Reads (HS)", na.rm=TRUE), 8, 3,1, scientific=TRUE);
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@ -188,7 +185,6 @@ tearsheet<-function(){
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table2<-cbind(lanes, samps)
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colnames(table2)<-c("Per lane", "Per sample")
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print(nrow(table2))
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rownames(table2)<-c("Reads", "Used bases", "Average target coverage", "% loci covered to 10x", "% loci covered to 20x","% loci covered to 30x")
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par(mar=c(0,0,1,0))
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@ -246,31 +242,30 @@ tearsheet<-function(){
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table3<-rbind(paste(instrument), used_lanes, sprintf("%s rejected by sequencing, %s by analysis\n", unused_lanes_by_sequencing, unused_lanes_by_analysis), sprintf("%0.1f +/- %0.1f lanes (median=%0.1f)\n", lanes_per_sample_mean, lanes_per_sample_sd, lanes_per_sample_median), sprintf("%s paired, %s widowed, %s single\n", lanes_paired, lanes_widowed, lanes_single), sprintf("%0.1f +/- %0.1f bases (median=%0.1f)\n", read_length_mean, read_length_sd, read_length_median), sprintf("\tSequencing dates: %s to %s\n", start_date, end_date))
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print(nrow(table3))
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print(table3)
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rownames(table3)<-c("Sequencer", "Used lanes", "Unused lanes","Used lanes/sample", "Lane parities", "Read lengths", "Sequencing dates")
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par(mar=c(0,0,1,0))
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textplot(table3, rmar=1, col.rownames="dark blue", show.colnames=FALSE, cex=1.25, valign="top")
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title(main="Sequencing Summary", family="sans", cex.main=1.25, line=0)
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eval = gsa.read.gatkreport("NonAutism_Walsh.cleaned.snps_and_indels.filtered.annotated.eval")
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# Variant summary
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eval.counts = read.csv(paste(cmdargs$evalroot, ".Count_Variants.csv", sep=""), header=TRUE, comment.char="#");
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eval.counts.called = subset(eval.counts, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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eval.counts.called.all = subset(eval.counts.called, novelty_name == "all")$nVariantLoci;
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eval.counts.called.known = subset(eval.counts.called, novelty_name == "known")$nVariantLoci;
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eval.counts.called.novel = subset(eval.counts.called, novelty_name == "novel")$nVariantLoci;
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##TODO: Fix this csv reader
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eval.counts = eval$CountVariants
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eval.counts.all = subset(eval.counts, Novelty == "all")$nVariantLoci;
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eval.counts.known = subset(eval.counts, Novelty == "known")$nVariantLoci;
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eval.counts.novel = subset(eval.counts, Novelty == "novel")$nVariantLoci;
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eval.titv = read.csv(paste(cmdargs$evalroot, ".Ti_slash_Tv_Variant_Evaluator.csv", sep=""), header=TRUE, comment.char="#");
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eval.titv.called = subset(eval.titv, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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eval.titv.called.all = subset(eval.titv.called, novelty_name == "all")$ti.tv_ratio;
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eval.titv.called.known = subset(eval.titv.called, novelty_name == "known")$ti.tv_ratio;
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eval.titv.called.novel = subset(eval.titv.called, novelty_name == "novel")$ti.tv_ratio;
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eval.titv = eval$TiTvVariantEvaluator
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eval.titv.all = subset(eval.titv, Novelty == "all")$tiTvRatio;
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eval.titv.known = subset(eval.titv, Novelty == "known")$tiTvRatio;
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eval.titv.novel = subset(eval.titv, Novelty == "novel")$tiTvRatio;
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table4 = matrix(c(eval.counts.all, eval.counts.known, eval.counts.novel, eval.titv.all, eval.titv.known, eval.titv.novel, "3.0 - 3.2", "3.2 - 3.4", "2.7 - 3.0"), nrow=3);
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table4 = matrix(c(eval.counts.called.all, eval.counts.called.known, eval.counts.called.novel, eval.titv.called.all, eval.titv.called.known, eval.titv.called.novel, "3.0 - 3.2", "3.2 - 3.4", "2.7 - 3.0"), nrow=3);
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print(nrow(table4))
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print(paste("columns should be three, actually are:", ncol(table4)))
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rownames(table4) = c("All", "Known", "Novel");
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colnames(table4) = c("Found", "Ti/Tv ratio", "Expected Ti/Tv ratio");
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@ -279,46 +274,45 @@ tearsheet<-function(){
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par(mar=c(0,0,0,0))
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textplot(table4, rmar=1, col.rownames="dark blue", cex=1.25, valign="top")
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title(main="Variant Summary", family="sans", cex.main=1.25, line=-2)
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#plots
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#
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# #plots
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# #fix this reader
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# eval.bysample = read.csv(paste(cmdargs$evalroot, ".SimpleMetricsBySample.csv", sep=""), header=TRUE, comment.char="#");
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# eval.bysample.called = subset(eval.bysample, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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# eval.bysample.all = subset(eval.bysample.called, novelty_name == "all");
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# eval.bysample.known = subset(eval.bysample.called, novelty_name == "known");
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# eval.bysample.novel = subset(eval.bysample.called, novelty_name == "novel");
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eval.bysample = read.csv(paste(cmdargs$evalroot, ".SimpleMetricsBySample.csv", sep=""), header=TRUE, comment.char="#");
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eval.bysample.called = subset(eval.bysample, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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eval.bysample.called.all = subset(eval.bysample.called, novelty_name == "all");
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eval.bysample.called.known = subset(eval.bysample.called, novelty_name == "known");
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eval.bysample.called.novel = subset(eval.bysample.called, novelty_name == "novel");
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eval.ac = read.csv(paste(cmdargs$evalroot, ".MetricsByAc.csv", sep=""), header=TRUE, comment.char="#");
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eval.ac.called = subset(eval.ac, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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eval.ac.called.all = subset(eval.ac.called, novelty_name == "all");
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eval.ac.called.known = subset(eval.ac.called, novelty_name == "known");
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eval.ac.called.novel = subset(eval.ac.called, novelty_name == "novel");
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eval.func = read.csv(paste(cmdargs$evalroot, ".Functional_Class_Counts_by_Sample.csv", sep=""), header=TRUE, comment.char="#");
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eval.func.called = subset(eval.func, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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eval.func.called.all = subset(eval.func.called, novelty_name == "all");
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eval.func.called.known = subset(eval.func.called, novelty_name == "known");
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eval.func.called.novel = subset(eval.func.called, novelty_name == "novel");
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eval.ac = eval$SimpleMetricsByAC.metrics
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eval.ac.all = subset(eval.ac, Novelty == "all");
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eval.ac.known = subset(eval.ac, Novelty == "known");
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eval.ac.novel = subset(eval.ac, Novelty == "novel");
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#
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# eval.func = read.csv(paste(cmdargs$evalroot, ".Functional_Class_Counts_by_Sample.csv", sep=""), header=TRUE, comment.char="#");
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# eval.func.called = subset(eval.func, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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# eval.func.all = subset(eval.func.called, novelty_name == "all");
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# eval.func.known = subset(eval.func.called, novelty_name == "known");
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# eval.func.novel = subset(eval.func.called, novelty_name == "novel");
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#boxplot(eval.bysample.called.all$CountVariants, eval.bysample.called.known$CountVariants, eval.bysample.called.novel$CountVariants, names=c("All", "Known", "Novel"), ylab="Variants per sample", main="", cex=1.3, cex.lab=1.3, cex.axis=1.3);
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#boxplot(eval.bysample.all$CountVariants, eval.bysample.known$CountVariants, eval.bysample.novel$CountVariants, names=c("All", "Known", "Novel"), ylab="Variants per sample", main="", cex=1.3, cex.lab=1.3, cex.axis=1.3);
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# par(mar=c(5, 4, 4, 2) + 0.1)
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# ind = order(eval.bysample.all$CountVariants);
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# plot(c(1:length(eval.bysample.all$CountVariants)), eval.bysample.all$CountVariants[ind], col="black", cex=1.1, cex.lab=1.1, cex.axis=1.1, main="Variants per Sample", xlab="Sample", ylab="Number of variants", bty="n", ylim=c(0, max(eval.bysample.all$CountVariants)));
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# points(c(1:length(eval.bysample.known$CountVariants)), eval.bysample.known$CountVariants[ind], col="blue", cex=1.3);
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# points(c(1:length(eval.bysample.novel$CountVariants)), eval.bysample.novel$CountVariants[ind], col="red", cex=1.3);
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# legend("right", max(eval.bysample.all$CountVariants)/2, c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
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par(mar=c(5, 4, 4, 2) + 0.1)
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ind = order(eval.bysample.called.all$CountVariants);
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plot(c(1:length(eval.bysample.called.all$CountVariants)), eval.bysample.called.all$CountVariants[ind], col="black", cex=1.1, cex.lab=1.1, cex.axis=1.1, main="Variants per Sample", xlab="Sample", ylab="Number of variants", bty="n", ylim=c(0, max(eval.bysample.called.all$CountVariants)));
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points(c(1:length(eval.bysample.called.known$CountVariants)), eval.bysample.called.known$CountVariants[ind], col="blue", cex=1.3);
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points(c(1:length(eval.bysample.called.novel$CountVariants)), eval.bysample.called.novel$CountVariants[ind], col="red", cex=1.3);
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legend("right", max(eval.bysample.called.all$CountVariants)/2, c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
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par(mar=c(5, 4, 4, 2) + 0.1)
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plot(eval.ac.called.all$AC, eval.ac.called.all$n, col="black", type="l", lwd=2, cex=1.1, cex.lab=1.1, cex.axis=1.1, xlab="Allele count", ylab="Number of variants", main="Variants by Allele Count", log="xy", bty="n");
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points(eval.ac.called.known$AC, eval.ac.called.known$n, col="blue", type="l", lwd=2);
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points(eval.ac.called.novel$AC, eval.ac.called.novel$n, col="red", type="l", lwd=2);
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plot(eval.ac.all$AC, eval.ac.all$n, col="black", type="l", lwd=2, cex=1.1, cex.lab=1.1, cex.axis=1.1, xlab="Allele count", ylab="Number of variants", main="Variants by Allele Count", log="xy", bty="n");
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points(eval.ac.known$AC, eval.ac.known$n, col="blue", type="l", lwd=2);
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points(eval.ac.novel$AC, eval.ac.novel$n, col="red", type="l", lwd=2);
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legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), lwd=2);
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#plot(eval.func.called.all$Synonymous[ind] / (eval.func.called.all$Missense + eval.func.called.all$Nonsense)[ind], ylim=c(0, 2), cex=1.3, cex.lab=1.3, cex.axis=1.3, bty="n", xlab="Sample", ylab="Ratio of synonymous to non-synonymous variants", col="black");
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#points(eval.func.called.known$Synonymous[ind] / (eval.func.called.known$Missense + eval.func.called.known$Nonsense)[ind], cex=1.3, col="blue");
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#points(eval.func.called.novel$Synonymous[ind] / (eval.func.called.novel$Missense + eval.func.called.novel$Nonsense)[ind], cex=1.3, col="red");
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#plot(eval.func.all$Synonymous[ind] / (eval.func.all$Missense + eval.func.all$Nonsense)[ind], ylim=c(0, 2), cex=1.3, cex.lab=1.3, cex.axis=1.3, bty="n", xlab="Sample", ylab="Ratio of synonymous to non-synonymous variants", col="black");
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#points(eval.func.known$Synonymous[ind] / (eval.func.known$Missense + eval.func.known$Nonsense)[ind], cex=1.3, col="blue");
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#points(eval.func.novel$Synonymous[ind] / (eval.func.novel$Missense + eval.func.novel$Nonsense)[ind], cex=1.3, col="red");
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#legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
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@ -330,44 +324,43 @@ tearsheet()
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# Plots
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plots<-function(){
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eval.bysample = read.csv(paste(cmdargs$evalroot, ".SimpleMetricsBySample.csv", sep=""), header=TRUE, comment.char="#");
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eval.bysample.called = subset(eval.bysample, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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eval.bysample.called.all = subset(eval.bysample.called, novelty_name == "all");
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eval.bysample.called.known = subset(eval.bysample.called, novelty_name == "known");
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eval.bysample.called.novel = subset(eval.bysample.called, novelty_name == "novel");
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# eval.bysample = read.csv(paste(cmdargs$evalroot, ".SimpleMetricsBySample.csv", sep=""), header=TRUE, comment.char="#");
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# eval.bysample.called = subset(eval.bysample, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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# eval.bysample.all = subset(eval.bysample.called, novelty_name == "all");
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# eval.bysample.known = subset(eval.bysample.called, novelty_name == "known");
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# eval.bysample.novel = subset(eval.bysample.called, novelty_name == "novel");
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eval.ac = read.csv(paste(cmdargs$evalroot, ".MetricsByAc.csv", sep=""), header=TRUE, comment.char="#");
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eval.ac.called = subset(eval.ac, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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eval.ac.called.all = subset(eval.ac.called, novelty_name == "all");
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eval.ac.called.known = subset(eval.ac.called, novelty_name == "known");
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eval.ac.called.novel = subset(eval.ac.called, novelty_name == "novel");
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eval.func = read.csv(paste(cmdargs$evalroot, ".Functional_Class_Counts_by_Sample.csv", sep=""), header=TRUE, comment.char="#");
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eval.func.called = subset(eval.func, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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eval.func.called.all = subset(eval.func.called, novelty_name == "all");
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eval.func.called.known = subset(eval.func.called, novelty_name == "known");
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eval.func.called.novel = subset(eval.func.called, novelty_name == "novel");
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eval.ac = eval$SimpleMetricsByAC.metrics
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eval.ac.all = subset(eval.ac.called, Novelty == "all");
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eval.ac.known = subset(eval.ac.called, Novelty == "known");
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eval.ac.novel = subset(eval.ac.called, Novelty == "novel");
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#
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# eval.func = read.csv(paste(cmdargs$evalroot, ".Functional_Class_Counts_by_Sample.csv", sep=""), header=TRUE, comment.char="#");
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# eval.func.called = subset(eval.func, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
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# eval.func.all = subset(eval.func.called, novelty_name == "all");
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# eval.func.known = subset(eval.func.called, novelty_name == "known");
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# eval.func.novel = subset(eval.func.called, novelty_name == "novel");
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pdf(cmdargs$plotout);
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pdf(file= cmdargs$plotout, width=22, height=17, pagecentre=TRUE, pointsize=24)
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#
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# boxplot(eval.bysample.all$CountVariants, eval.bysample.known$CountVariants, eval.bysample.novel$CountVariants, names=c("All", "Known", "Novel"), ylab="Variants per sample", main="", cex=1.3, cex.lab=1.3, cex.axis=1.3);
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#
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# ind = order(eval.bysample.all$CountVariants);
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# plot(c(1:length(eval.bysample.all$CountVariants)), eval.bysample.all$CountVariants[ind], col="black", cex=1.3, cex.lab=1.3, cex.axis=1.3, xlab="Sample", ylab="Number of variants", bty="n", ylim=c(0, max(eval.bysample.all$CountVariants)));
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# points(c(1:length(eval.bysample.known$CountVariants)), eval.bysample.known$CountVariants[ind], col="blue", cex=1.3);
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# points(c(1:length(eval.bysample.novel$CountVariants)), eval.bysample.novel$CountVariants[ind], col="red", cex=1.3);
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# legend(0, max(eval.bysample.all$CountVariants)/2, c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
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boxplot(eval.bysample.called.all$CountVariants, eval.bysample.called.known$CountVariants, eval.bysample.called.novel$CountVariants, names=c("All", "Known", "Novel"), ylab="Variants per sample", main="", cex=1.3, cex.lab=1.3, cex.axis=1.3);
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ind = order(eval.bysample.called.all$CountVariants);
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plot(c(1:length(eval.bysample.called.all$CountVariants)), eval.bysample.called.all$CountVariants[ind], col="black", cex=1.3, cex.lab=1.3, cex.axis=1.3, xlab="Sample", ylab="Number of variants", bty="n", ylim=c(0, max(eval.bysample.called.all$CountVariants)));
|
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points(c(1:length(eval.bysample.called.known$CountVariants)), eval.bysample.called.known$CountVariants[ind], col="blue", cex=1.3);
|
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points(c(1:length(eval.bysample.called.novel$CountVariants)), eval.bysample.called.novel$CountVariants[ind], col="red", cex=1.3);
|
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legend(0, max(eval.bysample.called.all$CountVariants)/2, c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
|
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|
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plot(eval.ac.called.all$AC, eval.ac.called.all$n, col="black", type="l", lwd=2, cex=1.3, cex.lab=1.3, cex.axis=1.3, xlab="Allele count", ylab="Number of variants", main="", log="xy", bty="n");
|
||||
points(eval.ac.called.known$AC, eval.ac.called.known$n, col="blue", type="l", lwd=2);
|
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points(eval.ac.called.novel$AC, eval.ac.called.novel$n, col="red", type="l", lwd=2);
|
||||
plot(eval.ac.all$AC, eval.ac.all$n, col="black", type="l", lwd=2, cex=1.3, cex.lab=1.3, cex.axis=1.3, xlab="Allele count", ylab="Number of variants", main="", log="xy", bty="n");
|
||||
points(eval.ac.known$AC, eval.ac.known$n, col="blue", type="l", lwd=2);
|
||||
points(eval.ac.novel$AC, eval.ac.novel$n, col="red", type="l", lwd=2);
|
||||
legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), lwd=2);
|
||||
|
||||
plot(eval.func.called.all$Synonymous[ind] / (eval.func.called.all$Missense + eval.func.called.all$Nonsense)[ind], ylim=c(0, 2), cex=1.3, cex.lab=1.3, cex.axis=1.3, bty="n", xlab="Sample", ylab="Ratio of synonymous to non-synonymous variants", col="black");
|
||||
points(eval.func.called.known$Synonymous[ind] / (eval.func.called.known$Missense + eval.func.called.known$Nonsense)[ind], cex=1.3, col="blue");
|
||||
points(eval.func.called.novel$Synonymous[ind] / (eval.func.called.novel$Missense + eval.func.called.novel$Nonsense)[ind], cex=1.3, col="red");
|
||||
legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
|
||||
#
|
||||
# plot(eval.func.all$Synonymous[ind] / (eval.func.all$Missense + eval.func.all$Nonsense)[ind], ylim=c(0, 2), cex=1.3, cex.lab=1.3, cex.axis=1.3, bty="n", xlab="Sample", ylab="Ratio of synonymous to non-synonymous variants", col="black");
|
||||
# points(eval.func.known$Synonymous[ind] / (eval.func.known$Missense + eval.func.known$Nonsense)[ind], cex=1.3, col="blue");
|
||||
# points(eval.func.novel$Synonymous[ind] / (eval.func.novel$Missense + eval.func.novel$Nonsense)[ind], cex=1.3, col="red");
|
||||
# legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
|
||||
|
||||
dev.off();
|
||||
}
|
||||
|
||||
|
|
|
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Loading…
Reference in New Issue