This file automaticially generates data and graphics for tearsheets and data processing reports
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@3551 348d0f76-0448-11de-a6fe-93d51630548a
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#Before executing this file, save squid files as csv, then as tab deliminated files with only the column values as the header, change the format of all cells to numbers. Assign the path to these files to "samples" and "lanes" respectively.
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tearsheetcalc<-function(lanes, samples, sample_sets, eval1, eval2, eval3, eval4, eval5, eval6){
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read.delim(file=lanes, header= TRUE)->bylane;
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read.delim(file=samples, header= TRUE)->bysample;
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attach(bylane);
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callable.target<-HS_TARGET_TERRITORY[1];
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singlelanes<-length(which(Lane.Type=="Single"));
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pairedlanes<-length(which(Lane.Type=="Paired"));
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mean.read.lane<-signif(mean(AL_TOTAL_READS, na.rm=TRUE));
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sd.read.lane<-signif(sd(AL_TOTAL_READS, na.rm=TRUE));
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mean.ub.lane<-signif(mean(HS_ON_TARGET_BASES, na.rm=TRUE));
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sd.ub.lane<-signif(sd(HS_ON_TARGET_BASES, na.rm=TRUE));
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mean.cov.lane<-round(mean(HS_MEAN_TARGET_COVERAGE, na.rm=TRUE));
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sd.cov.lane<-round(sd(HS_MEAN_TARGET_COVERAGE, na.rm=TRUE));
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mean.10x.lane<-round(mean(HS_PCT_TARGET_BASES_10X, na.rm=TRUE));
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mean.20x.lane<-round(mean(HS_PCT_TARGET_BASES_20X, na.rm=TRUE));
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mean.30x.lane<-round(mean(HS_PCT_TARGET_BASES_30X, na.rm=TRUE));
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sd.10x.lane<-round(sd(HS_PCT_TARGET_BASES_10X, na.rm=TRUE));
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sd.20x.lane<-round(sd(HS_PCT_TARGET_BASES_20X, na.rm=TRUE));
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sd.30x.lane<-round(sd(HS_PCT_TARGET_BASES_30X, na.rm=TRUE));
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names<-paste(Project, " ", External.ID, "-", Lane, sep="")
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#makes a plot of the number of SNPS called per lane
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library(graphics)
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pdf(file=paste(sample_sets, "_SNPS.pdf", sep=""), width=0.2*length(SNP_TOTAL_SNPS), height=0.1*length(SNP_TOTAL_SNPS))
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layout(matrix(c(1,1 , 2), 1, 3, byrow=FALSE), respect=TRUE)
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plot(1:length(SNP_TOTAL_SNPS), main="SNPs Called in Each Lane", SNP_TOTAL_SNPS, xlab="", ylab="SNPs Called in Lane", xaxt="n", pch=16, col="blue")
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axis(side=1, at=(1:length(SNP_TOTAL_SNPS)), labels=names, cex.axis=0.75, las=2)
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boxplot(SNP_TOTAL_SNPS, main="SNPs Called in Lane", ylab="SNPs Called")
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if(length(boxplot.stats(SNP_TOTAL_SNPS)$out)==0){
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mtext("No outliers", side=1, line=4)
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}else{
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mtext(paste("Outlier SNP call counts in ", length(boxplot.stats(SNP_TOTAL_SNPS)$out), "lanes"), side=1, line=4)
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}
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dev.off()
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#makes SNP plot in log scale
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pdf(file=paste(sample_sets, "_SNPS_log.pdf", sep=""), width=0.2*length(SNP_TOTAL_SNPS), height=0.1*length(SNP_TOTAL_SNPS))
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layout(matrix(c(1,1 , 2), 1, 3, byrow=FALSE), respect=TRUE)
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plot(1:length(SNP_TOTAL_SNPS), log(SNP_TOTAL_SNPS), main="SNPs Called in Each Lane", xlab="", ylab="Log(SNPs Called in Lane)", xaxt="n", pch=16, col="blue")
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par(ylog=TRUE)
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axis(side=1, at=(1:length(SNP_TOTAL_SNPS)), labels=names, cex.axis=0.75, las=2)
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boxplot(SNP_TOTAL_SNPS, main="SNPs Called in Lane", ylab="SNPs Called")
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if(length(boxplot.stats(SNP_TOTAL_SNPS)$out)==0){
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mtext("No outliers", side=1, line=4)
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}else{
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mtext(paste("Outlier SNP call counts in ", length(boxplot.stats(SNP_TOTAL_SNPS)$out), "lanes"), side=1, line=4)
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}
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dev.off()
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#makes a plot of snp calls ordered by lane
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pdf(file=paste(sample_sets, "_SNPS_lane.pdf", sep=""), width=0.2*length(SNP_TOTAL_SNPS), height=0.1*length(SNP_TOTAL_SNPS))
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layout(matrix(c(1,1 , 2), 1, 3, byrow=FALSE), respect=TRUE)
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plot(1:length(SNP_TOTAL_SNPS), SNP_TOTAL_SNPS[order(Lane)], main="SNPs Called in Each Lane", xlab="", ylab="Log(SNPs Called in Lane)", xaxt="n", pch=16, col="blue")
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par(ylog=TRUE)
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axis(side=1, at=(1:length(SNP_TOTAL_SNPS)), labels=names[order(Lane)], cex.axis=0.75, las=2)
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boxplot(SNP_TOTAL_SNPS, main="SNPs Called in Lane", ylab="SNPs Called")
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if(length(boxplot.stats(SNP_TOTAL_SNPS)$out)==0){
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mtext("No outliers", side=1, line=4)
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}else{
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mtext(paste("Outlier SNP call counts in ", length(boxplot.stats(SNP_TOTAL_SNPS)$out), "lanes"), side=1, line=4)
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}
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dev.off()
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#makes a plot of fingerprint calls and labels them good or bad
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badsnps<-union(which(FP_CONFIDENT_MATCHING_SNPS<15), which(FP_CONFIDENT_MATCHING_SNPS<15))
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colors<-c(rep("Blue", length(FP_CONFIDENT_CALLS)))
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colors[badsnps]<-"Red"
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pdf(file=paste(sample_sets, "_Fingerprints.pdf", sep=""), width=.2*length(FP_CONFIDENT_CALLS), height=.1*length(FP_CONFIDENT_CALLS))
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par(mar=c(6, 4, 5, 4))
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plot(1:length(FP_CONFIDENT_MATCHING_SNPS), FP_CONFIDENT_MATCHING_SNPS, pch=16, ylim=c(0,24), ylab="Fingerprint calls", xlab="", xaxt="n", col=colors, main="Fingerprint Calling and Matching")
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points(1:length(FP_CONFIDENT_MATCHING_SNPS), FP_CONFIDENT_CALLS, col=colors)
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axis(side=1, at=(1:length(FP_CONFIDENT_CALLS)), labels=names, cex.axis=0.75, las=2)
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if(length(badsnps)>0){
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legend("bottomright", legend=c("Confident calls at fingerprint sites by lane", "Confident matching calls at fingerprint sites by lane", "Confident calls in bad lanes", "Confident matching calls in bad lanes"), pch=c(1, 16, 1, 16), col=c("Blue", "Blue", "Red", "Red"))
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mtext("Some problematic fingerprint sites", side=3)
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}else{
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legend("bottomright", legend=c("Confident calls at fingerprint sites by lane", "Confident matching calls at fingerprint sites by lane"), pch=c(1, 16), col="Blue")}
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dev.off()
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detach(bylane);
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attach(bysample);
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mean.lanes.samp<-signif(mean(X..Lanes.included.in.aggregation, na.rm = TRUE));
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sd.lanes.samp<-signif(sd(X..Lanes.included.in.aggregation, na.rm=TRUE));
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mean.mrl.samp<-signif(mean(Mean.Read.Length, na.rm=TRUE));
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sd.mrl.samp<-signif(sd(Mean.Read.Length, na.rm=TRUE));
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mean.read.samp<-signif(mean(Total.Reads, na.rm=TRUE));
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sd.read.samp<-signif(sd(Total.Reads, na.rm=TRUE));
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mean.ub.samp<-signif(mean(On.Target.Bases..HS., na.rm=TRUE));
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sd.ub.samp<-signif(sd(On.Target.Bases..HS., na.rm=TRUE));
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mean.cov.samp<-round(mean(Mean.Target.Coverage..HS., na.rm=TRUE));
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sd.cov.samp<-round(sd(Mean.Target.Coverage..HS., na.rm=TRUE));
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mean.10x.samp<-round(mean(PCT.Target.Bases.10x..HS., na.rm=TRUE));
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mean.20x.samp<-round(mean(PCT.Target.Bases.20x..HS., na.rm=TRUE));
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mean.30x.samp<-round(mean(PCT.Target.Bases.30x..HS., na.rm=TRUE));
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sd.10x.samp<-round(sd(PCT.Target.Bases.10x..HS., na.rm=TRUE));
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sd.20x.samp<-round(sd(PCT.Target.Bases.20x..HS., na.rm=TRUE));
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sd.30x.samp<-round(sd(PCT.Target.Bases.30x..HS., na.rm=TRUE));
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detach(bysample);
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#print all of this stuff out in R.
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print(paste("Callable Target: ", callable.target, " bases", sep=""), quote = FALSE);
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print(paste("Used Lanes per Sample: ", mean.lanes.samp, " +/- ", sd.lanes.samp, sep=""), quote=FALSE);
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print(paste("Parities: ", singlelanes, " single lanes, ", pairedlanes, " paired lanes", sep=""), quote=FALSE);
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print(paste("Read Legnths: ", mean.mrl.samp, " +/- ", sd.mrl.samp, sep=""), quote = FALSE);
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print(paste("Reads per lane: ", mean.read.lane, " +/- ", sd.read.lane, sep=""), quote = FALSE);
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print(paste("Reads per sample: ", mean.read.samp, " +/- ", sd.read.samp, sep=""), quote = FALSE);
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print(paste("Used bases per lane: ", mean.read.lane, " +/- ", sd.read.lane, sep=""), quote = FALSE);
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print(paste("Used bases per sample: ", mean.read.samp, " +/- ", sd.read.samp, sep=""), quote = FALSE)
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print(paste("Average target coverage per lane: ", mean.cov.lane, " +/- ", sd.cov.lane, sep=""), quote = FALSE);
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print(paste("Average target coverage per sample: ", mean.cov.samp, " +/- ", sd.cov.samp, sep=""), quote = FALSE);
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print(paste("% loci covered to 10x per lane: ", mean.10x.lane, "% +/- ", sd.10x.lane, "%", sep=""), quote = FALSE)
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print(paste("% loci covered to 10x per sample: ", mean.10x.samp, " +/- ", sd.10x.samp, "%", sep=""), quote = FALSE)
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print(paste("% loci covered to 20x per lane: ", mean.20x.lane, "% +/- ", sd.20x.lane, "%", sep=""), quote = FALSE)
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print(paste("% loci covered to 20x per sample: ", mean.20x.samp, "% +/- ", sd.20x.samp, "%", sep=""), quote = FALSE)
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print(paste("% loci covered to 30x per lane: ", mean.30x.lane, "% +/- ", sd.30x.lane, "%", sep=""), quote = FALSE)
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print(paste("% loci covered to 30x per sample: ", mean.30x.samp, "% +/- ", sd.30x.samp, "%", sep=""), quote = FALSE)
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#still need to figure out how to get various information from eval file into R, but whatever
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read.csv(eval1, header=TRUE)->errpercycle
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errpercycle<-matrix(c(rep("tester", 24*90 ), rep(1:8, 3*90), rep(1:90,24), runif(90*24, min=0, max=0.7)), nrow=90*24, ncol=4); colnames(errpercycle)<-c("sample_set", "plate", "lane", "cycle", "errorrate") #delete this after testing
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as.data.frame(errpercycle)->errpercycle
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attach(errpercycle)
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pdf(paste(sample_set, "error_rate_per_cycle.pdf", sep=""))
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names<-paste(sample)
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}
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lane<-"mennonite_by_lane.txt"
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#insert file path here before running
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sample<-"mennonite_by_sample.txt"
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#insertfilepath here before running
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sample_set<-"Ashuldin_mennonite"
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tearsheetcalc(lane,sample,sample_set)
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