updated a whole bunch of column names to work like i want them to and added more informative figures for DOC
git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@4131 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
corin
parent
f384d4a5d6
commit
8931a63588
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@ -1,6 +1,8 @@
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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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#testcomment
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#TODO: make sure all font sizes readable
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#put everything into one decent looking pdf
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#set up database stuff for firehose and picard interface
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#
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stuffmaker<-function(args){
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@ -8,9 +10,9 @@ lanes<-args[1]
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samples<-args[2]
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sample_sets<-args[3]
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eval<-args[4]
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noveltitv<-args[5]
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knowntitv<-args[6]
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DOC<-args[7]
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titveval<-args[5]
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DOCi<-args[6]
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DOCs<-args[7]
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if(is.na(sample_sets)){
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print("Please specify sample set for file naming and press enter.")
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@ -23,9 +25,10 @@ if(is.na(sample_sets)){
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if(typeof(lanes)=="character"){
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read.delim(file=lanes, header= TRUE)->bylane;
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}else{
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colnames(bylane)<-c('Initiative','Project','GSSR.ID','External.ID','WR.ID','Flowcell','Lane','Lane.Type','Library','AL_TOTAL_READS','AL_PF_READS','AL_PCT_PF_READS','AL_PF_NOISE_READS','AL_PF_READS_ALIGNED','AL_PCT_PF_READS_ALIGNED','AL_PF_HQ_ALIGNED_READS','AL_PF_HQ_ALIGNED_BASES','AL_PF_HQ_ALIGNED_Q20_BASES','AL_PF_HQ_MEDIAN_MISMATCHES','AL_MEAN_READ_LENGTH','AL_READS_ALIGNED_IN_PAIRS','AL_PCT_READS_ALIGNED_IN_PAIRS','AL_BAD_CYCLES','AL_PCT_STRAND_BALANCE','DUP_UNPAIRED_READS_EXAMINED','DUP_READ_PAIRS_EXAMINED','DUP_UNMAPPED_READS','DUP_UNPAIRED_READ_DUPLICATES','DUP_READ_PAIR_DUPLICATES','DUP_PERCENT_DUPLICATION','DUP_ESTIMATED_LIBRARY_SIZE','HS_BAIT_SET','HS_GENOME_SIZE','HS_LIBRARY_SIZE','HS_BAIT_TERRITORY','HS_TARGET_TERRITORY','HS_BAIT_DESIGN_EFFICIENCY','HS_TOTAL_READS','HS_PF_READS','HS_PF_UNIQUE_READS','HS_PCT_PF_READS','HS_PCT_PF_UQ_READS','HS_PCT_PF_UQ_READS_ALIGNED','HS_PF_UQ_READS_ALIGNED','HS_PF_UQ_BASES_ALIGNED','HS_ON_BAIT_BASES','HS_NEAR_BAIT_BASES','HS_OFF_BAIT_BASES','HS_ON_TARGET_BASES','HS_PCT_SELECTED_BASES','HS_PCT_OFF_BAIT','HS_ON_BAIT_VS_SELECTED','HS_MEAN_BAIT_COVERAGE','HS_MEAN_TARGET_COVERAGE','HS_FOLD_ENRICHMENT','HS_ZERO_CVG_TARGETS_PCT','HS_FOLD_80_BASE_PENALTY','HS_PCT_TARGET_BASES_2X','HS_PCT_TARGET_BASES_10X','HS_PCT_TARGET_BASES_20X','HS_PCT_TARGET_BASES_30X','HS_PENALTY_10X','HS_PENALTY_20X','HS_PENALTY_30X','SNP_TOTAL_SNPS','SNP_PCT_DBSNP','SNP_NUM_IN_DBSNP','Lane.IC.Matches','Lane.IC.PCT.Mean.RD1.Err.Rate','Lane.IC.PCT.Mean.RD2.Err.Rate','FP_PANEL_NAME','FP_PANEL_SNPS','FP_CONFIDENT_CALLS','FP_CONFIDENT_MATCHING_SNPS','FP_CONFIDENT_CALLED_PCT','FP_CONFIDENT_MATCHING_SNPS_PCT','LPCNCRD_REFERENCE','LPCNCRD_NON_REFERENCE','LPCNCRD_PCT_CONCORDANCE')
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}else{
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lanes->bylane
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colnames(bylane)<-c(Initiative,Project,GSSR.ID,External.ID,WR.ID,Flowcell,Lane,Lane.Type,Library,AL_TOTAL_READS,AL_PF_READS,AL_PCT_PF_READS,AL_PF_NOISE_READS,AL_PF_READS_ALIGNED,AL_PCT_PF_READS_ALIGNED,AL_PF_HQ_ALIGNED_READS,AL_PF_HQ_ALIGNED_BASES,AL_PF_HQ_ALIGNED_Q20_BASES,AL_PF_HQ_MEDIAN_MISMATCHES,AL_MEAN_READ_LENGTH,AL_READS_ALIGNED_IN_PAIRS,AL_PCT_READS_ALIGNED_IN_PAIRS,AL_BAD_CYCLES,AL_PCT_STRAND_BALANCE,DUP_UNPAIRED_READS_EXAMINED,DUP_READ_PAIRS_EXAMINED,DUP_UNMAPPED_READS,DUP_UNPAIRED_READ_DUPLICATES,DUP_READ_PAIR_DUPLICATES,DUP_PERCENT_DUPLICATION,DUP_ESTIMATED_LIBRARY_SIZE,HS_BAIT_SET,HS_GENOME_SIZE,HS_LIBRARY_SIZE,HS_BAIT_TERRITORY,HS_TARGET_TERRITORY,HS_BAIT_DESIGN_EFFICIENCY,HS_TOTAL_READS,HS_PF_READS,HS_PF_UNIQUE_READS,HS_PCT_PF_READS,HS_PCT_PF_UQ_READS,HS_PCT_PF_UQ_READS_ALIGNED,HS_PF_UQ_READS_ALIGNED,HS_PF_UQ_BASES_ALIGNED,HS_ON_BAIT_BASES,HS_NEAR_BAIT_BASES,HS_OFF_BAIT_BASES,HS_ON_TARGET_BASES,HS_PCT_SELECTED_BASES,HS_PCT_OFF_BAIT,HS_ON_BAIT_VS_SELECTED,HS_MEAN_BAIT_COVERAGE,HS_MEAN_TARGET_COVERAGE,HS_FOLD_ENRICHMENT,HS_ZERO_CVG_TARGETS_PCT,HS_FOLD_80_BASE_PENALTY,HS_PCT_TARGET_BASES_2X,HS_PCT_TARGET_BASES_10X,HS_PCT_TARGET_BASES_20X,HS_PCT_TARGET_BASES_30X,HS_PENALTY_10X,HS_PENALTY_20X,HS_PENALTY_30X,SNP_TOTAL_SNPS,SNP_PCT_DBSNP,SNP_NUM_IN_DBSNP,Lane.IC.Matches,Lane.IC.PCT.Mean.RD1.Err.Rate,Lane.IC.PCT.Mean.RD2.Err.Rate,FP_PANEL_NAME,FP_PANEL_SNPS,FP_CONFIDENT_CALLS,FP_CONFIDENT_MATCHING_SNPS,FP_CONFIDENT_CALLED_PCT,FP_CONFIDENT_MATCHING_SNPS_PCT,LPCNCRD_REFERENCE,LPCNCRD_NON_REFERENCE,LPCNCRD_PCT_CONCORDANCE)
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colnames(bylane)<-c('Initiative','Project','GSSR.ID','External.ID','WR.ID','Flowcell','Lane','Lane.Type','Library','AL_TOTAL_READS','AL_PF_READS','AL_PCT_PF_READS','AL_PF_NOISE_READS','AL_PF_READS_ALIGNED','AL_PCT_PF_READS_ALIGNED','AL_PF_HQ_ALIGNED_READS','AL_PF_HQ_ALIGNED_BASES','AL_PF_HQ_ALIGNED_Q20_BASES','AL_PF_HQ_MEDIAN_MISMATCHES','AL_MEAN_READ_LENGTH','AL_READS_ALIGNED_IN_PAIRS','AL_PCT_READS_ALIGNED_IN_PAIRS','AL_BAD_CYCLES','AL_PCT_STRAND_BALANCE','DUP_UNPAIRED_READS_EXAMINED','DUP_READ_PAIRS_EXAMINED','DUP_UNMAPPED_READS','DUP_UNPAIRED_READ_DUPLICATES','DUP_READ_PAIR_DUPLICATES','DUP_PERCENT_DUPLICATION','DUP_ESTIMATED_LIBRARY_SIZE','HS_BAIT_SET','HS_GENOME_SIZE','HS_LIBRARY_SIZE','HS_BAIT_TERRITORY','HS_TARGET_TERRITORY','HS_BAIT_DESIGN_EFFICIENCY','HS_TOTAL_READS','HS_PF_READS','HS_PF_UNIQUE_READS','HS_PCT_PF_READS','HS_PCT_PF_UQ_READS','HS_PCT_PF_UQ_READS_ALIGNED','HS_PF_UQ_READS_ALIGNED','HS_PF_UQ_BASES_ALIGNED','HS_ON_BAIT_BASES','HS_NEAR_BAIT_BASES','HS_OFF_BAIT_BASES','HS_ON_TARGET_BASES','HS_PCT_SELECTED_BASES','HS_PCT_OFF_BAIT','HS_ON_BAIT_VS_SELECTED','HS_MEAN_BAIT_COVERAGE','HS_MEAN_TARGET_COVERAGE','HS_FOLD_ENRICHMENT','HS_ZERO_CVG_TARGETS_PCT','HS_FOLD_80_BASE_PENALTY','HS_PCT_TARGET_BASES_2X','HS_PCT_TARGET_BASES_10X','HS_PCT_TARGET_BASES_20X','HS_PCT_TARGET_BASES_30X','HS_PENALTY_10X','HS_PENALTY_20X','HS_PENALTY_30X','SNP_TOTAL_SNPS','SNP_PCT_DBSNP','SNP_NUM_IN_DBSNP','Lane.IC.Matches','Lane.IC.PCT.Mean.RD1.Err.Rate','Lane.IC.PCT.Mean.RD2.Err.Rate','FP_PANEL_NAME','FP_PANEL_SNPS','FP_CONFIDENT_CALLS','FP_CONFIDENT_MATCHING_SNPS','FP_CONFIDENT_CALLED_PCT','FP_CONFIDENT_MATCHING_SNPS_PCT','LPCNCRD_REFERENCE','LPCNCRD_NON_REFERENCE','LPCNCRD_PCT_CONCORDANCE')
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}
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if(typeof(samples)=="character"){
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read.delim(file=samples, header= TRUE)->bysample;
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@ -34,19 +37,19 @@ if(is.na(sample_sets)){
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}
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#Calc by lane metrics
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attach(bylane);
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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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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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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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@ -58,23 +61,29 @@ if(is.na(sample_sets)){
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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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ticks<-c(match(unique(Flowcell), Flowcell) )
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ticks<-c(match(unique(Flowcell), sort(Flowcell)) )
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ys=rep(c(min(SNP_TOTAL_SNPS, na.rm=TRUE)*0.96, max(SNP_TOTAL_SNPS, na.rm=TRUE)*1.04, max(SNP_TOTAL_SNPS, na.rm=TRUE)*1.04, min(SNP_TOTAL_SNPS, na.rm=TRUE)*0.96, min(SNP_TOTAL_SNPS, na.rm=TRUE)*0.96), ceiling(length(ticks)/2))
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layout(matrix(c(1,1 , 2), 1, 3, byrow=FALSE), respect=TRUE)
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par(mar=c(10, 6, 3, 8))
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plot(1:length(SNP_TOTAL_SNPS), main=paste(sample_sets, ": SNPs Called in Each Lane sorted by Flowcell", sep=""), SNP_TOTAL_SNPS, xlab="", ylab="SNPs Called in Lane", ylim = c(min(SNP_TOTAL_SNPS, na.rm=TRUE), max(SNP_TOTAL_SNPS, na.rm=TRUE)), xaxt="n", pch=NA, cex.main=2, cex.axis=1.25, cex.lab=1.5)
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plot(1:length(SNP_TOTAL_SNPS), main=paste(sample_sets, ": SNPs Called in Each Lane sorted by Flowcell", sep=""), SNP_TOTAL_SNPS[order(Flowcell)], xlab="", ylab="SNPs Called in Lane", ylim = c(min(SNP_TOTAL_SNPS, na.rm=TRUE), max(SNP_TOTAL_SNPS, na.rm=TRUE)), xaxt="n", pch=NA, cex.main=2, cex.axis=1.25, cex.lab=1.5)
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axis(side=1, at=(ticks+1), labels=unique(Flowcell), tick=FALSE, hadj=1, cex.axis=1.25, las=2)
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axis(side=1, at=c(1:length(Flowcell))), labels=Lane[order(Flowcell)], tick=FALSE, hadj=1, cex.axis=1.25)
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axis(side=1, at=(ticks), labels=sort(unique(Flowcell)), tick=FALSE, vadj=2, hadj=1, cex.axis=1.25, las=2)
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shader<-ticks[c(rep(c(1,1,2,2,1), ceiling(length(ticks)/2))+sort(rep(seq(0, length(ticks),by=2), 5)))]-0.5
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if((length(ticks)%%2 > 0)){
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shader[(length(shader)-2):(length(shader)-1)]<-length(Flowcell)+0.5
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}
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shader<-na.omit(shader)
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polygon(shader, ys, border="black", lty=0, col="gray")
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points(1:length(SNP_TOTAL_SNPS), SNP_TOTAL_SNPS, col="blue", pch=19)
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boxplot(SNP_TOTAL_SNPS, main="SNPs Called in Lane", ylab="SNPs Called")
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cols<-rep("blue", length(SNP_TOTAL_SNPS))
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cols[which(SNP_TOTAL_SNPS %in% boxplot.stats(SNP_TOTAL_SNPS)$out)]<-"red"
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points(1:length(SNP_TOTAL_SNPS), SNP_TOTAL_SNPS, col=cols, pch=19)
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if(length(boxplot.stats(SNP_TOTAL_SNPS)$out)>0){
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legend("bottomright", legend=c("Normal SNP Call Counts", "Outlier SNP Call Counts"), pch=19, col=c("Blue", "red"), bg="White")
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}
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boxplot(SNP_TOTAL_SNPS, main="SNPs Called in Lane", ylab="SNPs Called", cex.axis=1.25 )
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if(length(boxplot.stats(SNP_TOTAL_SNPS)$out)==0){
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@ -85,53 +94,7 @@ if(is.na(sample_sets)){
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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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ys=rep(c((min(SNP_TOTAL_SNPS, na.rm=TRUE)+1)*0.96, max(SNP_TOTAL_SNPS, na.rm=TRUE)*1.04, max(SNP_TOTAL_SNPS, na.rm=TRUE)*1.04, (min(SNP_TOTAL_SNPS, na.rm=TRUE)+1)*0.96, (min(SNP_TOTAL_SNPS, na.rm=TRUE)+1)*0.96), ceiling(length(ticks)/2))
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layout(matrix(c(1,1 , 2), 1, 3, byrow=FALSE), respect=TRUE)
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par(mar=c(10, 6, 3, 8))
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plot(1:length(SNP_TOTAL_SNPS), main=paste(sample_sets, ": SNPs Called in Each Lane sorted by Flowcell", sep=""), SNP_TOTAL_SNPS, xlab="", log="y", ylab="SNPs Called in Lane (Log Scale)", ylim = c(min(SNP_TOTAL_SNPS, na.rm=TRUE)+1, max(SNP_TOTAL_SNPS, na.rm=TRUE)), xaxt="n", pch=NA, cex.main=2, cex.axis=1.25, cex.lab=1.5)
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par(ylog=TRUE)
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axis(side=1, at=(ticks+1), labels=unique(Flowcell), tick=FALSE, hadj=1, cex.axis=1.25, las=2)
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if((length(ticks)%%2 > 0)){
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shader[(length(shader)-2):(length(shader)-1)]<-length(Flowcell)+0.5
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}
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polygon(shader, ys, border="black", lty=0, col="gray")
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points(1:length(SNP_TOTAL_SNPS), SNP_TOTAL_SNPS, col="blue", pch=19)
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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)$out), "lanes"), side=1, line=4)
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}
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dev.off()
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#makes a plot of snp calls ordered by lane
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pdf(file=paste(sample_sets, "_SNPS_lane.pdf", sep=""), width=0.2*length(SNP_TOTAL_SNPS), height=0.1*length(SNP_TOTAL_SNPS))
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layout(matrix(c(1,1 , 2), 1, 3, byrow=FALSE), respect=TRUE)
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plot(1:length(SNP_TOTAL_SNPS), SNP_TOTAL_SNPS[order(Lane)], main=paste(sample_sets, ": SNPs Called in Each Lane sorted by Lane"), xlab="", ylab="SNPs Called in Lane", xaxt="n", pch=16, col="blue")
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par(ylog=TRUE)
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axis(side=1, at=(1:length(SNP_TOTAL_SNPS)), labels=names[order(Lane)], cex.axis=0.75, las=2)
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boxplot(SNP_TOTAL_SNPS, main="SNPs Called in Lane", ylab="SNPs Called")
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if(length(boxplot.stats(SNP_TOTAL_SNPS)$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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@ -139,12 +102,13 @@ if(is.na(sample_sets)){
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colors<-c(rep("Blue", length(FP_CONFIDENT_CALLS)))
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colors[badsnps]<-"Red"
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ticks<-c(match(unique(Flowcell), Flowcell) )
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ys=rep(c(min(SNP_TOTAL_SNPS, na.rm=TRUE)*0.96, max(SNP_TOTAL_SNPS, na.rm=TRUE)*1.04, max(SNP_TOTAL_SNPS, na.rm=TRUE)*1.04, min(SNP_TOTAL_SNPS, na.rm=TRUE)*0.96, min(SNP_TOTAL_SNPS, na.rm=TRUE)*0.96), ceiling(length(ticks)/2))
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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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ys=rep(c(0, 24*1.04, 24*1.04, 0, 0), ceiling(length(ticks)/2))
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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(10, 6, 8, 3))
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plot(1:length(FP_CONFIDENT_MATCHING_SNPS), FP_CONFIDENT_MATCHING_SNPS, pch=NA, ylim=c(0,24), ylab="Fingerprint calls", xlab="", xaxt="n", col=colors, main="Fingerprint Calling and Matching Sorted by lane", cex.main=3, cex.lab=2)
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axis(side=1, at=(ticks+1), labels=unique(Flowcell), tick=FALSE, hadj=1, cex.axis=1.25, las=2)
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shader<-ticks[c(rep(c(1,1,2,2,1), ceiling(length(ticks)/2))+sort(rep(seq(0, length(ticks),by=2), 5)))]-0.5
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shader<-na.omit(shader)
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if((length(ticks)%%2 > 0)){
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shader[(length(shader)-2):(length(shader)-1)]<-length(Flowcell)+0.5
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}
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@ -220,6 +184,7 @@ if(is.na(sample_sets)){
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}else{
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eval->errpercycle
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}
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pdf(paste(sample_sets, "_errorrate_per_cycle.pdf", sep=""), width=6, height=5)
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@ -245,63 +210,97 @@ if(is.na(sample_sets)){
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}
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#Makes TI/TV known v novel graph
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if(is.na(noveltitv)==FALSE && is.na(knowntitv) == FALSE){
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pdf(paste(sample_set, "_TiTv.pdf", sep=""), width=6, height=5)
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if(typeof(noveltitv)=="character"){
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read.table(file=noveltitv, header=FALSE)->novels
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}else{
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noveltitv->novels
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}
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if(typeof(knowntitv)=="character"){
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read.table(file=knowntitv, header=FALSE)->knowns
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}else{
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knowntitv->knowns
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}
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if(is.na(titveval)==FALSE){
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##TODO: need ot make sure this is nice and prettified.
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titv<-read.csv(file=titveval, skip=1)
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attach(titv)
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plot(novels[,2], col="red", ylim=c(0, 3.5), main="Ti/Tv for Novel and Known SNP calls", ylab="Ti/Tv", xlab="", xaxt="n")
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points(knowns[,2], col="blue")
|
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pdf(file=paste(sample_sets, "_TI-TV.pdf", sep=""), width=0.2*length(unique(sample)), height=0.175*length(unique(sample)))
|
||||
par(mar=c(11, 4, 4, 2))
|
||||
plot(seq(1:length(unique(sample))), Ti.Tv[which(novelty_name=="novel" & filter_name=="called")], xaxt="n", ylim=c(1, 4), main="Ti/Tv for Novel and Known SNP calls", ylab="Ti/Tv", xlab="", col="red", pch=1)
|
||||
|
||||
axis(side=1, at=(1:length(novels[,2])), labels=novels[,1], cex.axis=1, las=2)
|
||||
points(seq(1:length(unique(sample))), Ti.Tv[which(novelty_name=="known" & filter_name=="called")], pch=1, col="blue")
|
||||
|
||||
legend("bottomright", legend=c("Known Variants", "Novel Variants"), col=c("blue", "red"), pch=1, xjust=0.5)
|
||||
mtext("Lower Ti/Tv ratios indicated more false positive SNP calls.", side=1)
|
||||
axis(side=1, at=(1:length(unique(sample))), labels=unique(sample), tick=FALSE, hadj=1, cex.axis=1, las=2)
|
||||
|
||||
abline(a=mean(Ti.Tv[which(novelty_name=="all" & filter_name=="called")]),b=0)
|
||||
|
||||
legend("bottomright", legend=c("Known Variants", "Novel Variants", "Mean Ti/Tv for all variants"), col=c("blue", "red", "black"), pch=c(1,1,NA_integer_), lty=c(0, 0, 1), xjust=0.5)
|
||||
mtext(line=9,"Lower Ti/Tv ratios indicate potentially increased false positive SNP rates.", side=1)
|
||||
|
||||
dev.off()
|
||||
}else{
|
||||
print("Transition/transversion ratio file paths not provided")
|
||||
|
||||
print("TiTV filepath not provided")
|
||||
}
|
||||
|
||||
#Make DOC graph
|
||||
if(is.na(DOC)==FALSE){
|
||||
pdf(paste(sample_set, "_DOC.pdf", sep=""), width=6, height=5)
|
||||
if(typeof(DOC)=="character"){
|
||||
as.matrix(as.vector(read.delim(DOC, header=TRUE)[,2:502]))->DOCdata
|
||||
DOCdata<-matrix(DOCdata*100/sum(DOCdata[1,]), nrow=501, ncol=29, byrow=TRUE)
|
||||
colnames(DOCdata)<-read.delim(DOC, header=TRUE)[,1]
|
||||
}else{
|
||||
DOC->DOCdata
|
||||
if(is.na(DOCi)==FALSE){
|
||||
pdf(paste(sample_set, "_DOCi.pdf", sep=""), width=6, height=5)
|
||||
if(typeof(DOCi)=="character"){
|
||||
as.data.frame(read.delim(DOCi))->DOC
|
||||
}else{
|
||||
DOCi->DOCdata
|
||||
}
|
||||
|
||||
colnames(DOC)->cols
|
||||
apply(DOC[,grep("mean", cols)], 1, median)->medianofmeans
|
||||
apply(DOC[,grep("mean", cols)], 1, quantile, probs=3/4)->q3s
|
||||
apply(DOC[,grep("mean", cols)], 1, quantile, probs=1/4)->q1s
|
||||
|
||||
par(ylog=FALSE, mar=c(5, 4, 4, 2))
|
||||
plot(c(1:3122),sort(medianofmeans, decreasing=TRUE), type="l", lwd="1",log="y",ylab="Coverage", xlab="Targets sorted by median average coverage across sample",xaxt="n", main="Coverage Across All Targets")
|
||||
|
||||
abline(h=15, lty="dotted")
|
||||
|
||||
lines(c(1:3122),q3s[order(medianofmeans, decreasing=TRUE)])
|
||||
|
||||
lines(c(1:3122),q1s[order(medianofmeans, decreasing=TRUE)])
|
||||
|
||||
legend("bottomleft", "15x coverage", box.lty=0, lty="dotted")
|
||||
|
||||
dev.off()
|
||||
|
||||
pdf(paste(sample_set, "_DOCiy.pdf", sep=""), width=6, height=5)
|
||||
yuck<-DOC[which(medianofmeans<15),grep("mean", cols)]
|
||||
yuck<-yuck+0.1
|
||||
par(mar=c(16, 4, 4, 2))
|
||||
boxplot(t(yuck[order(medianofmeans[which(medianofmeans<15)], decreasing=TRUE),]),log="y", yaxt="n", xaxt="n", ylab="Average coverage accross all samples", main="Targets with low coverage accross samples")
|
||||
|
||||
axis(2, at=axTicks(2)+c(0, rep(0.1, length(axTicks(2))-1)), labels=c(0.0, axTicks(2)[2:length(axTicks(2))]))
|
||||
mtext("Target", side=1, line=14)
|
||||
axis(1, at=c(1:length(which(medianofmeans<15))), labels=DOC[which(medianofmeans<15),1][order(medianofmeans[which(medianofmeans<15)])], las=2)
|
||||
|
||||
oddies<-which(apply(DOCdata, 2, max)>10) #can be assigned any particular heuristic
|
||||
ncolors<-rainbow(ncol(DOCdata), s=0.5, v=0.5)
|
||||
ncolors[oddies]<-rainbow(length(oddies))
|
||||
nweights<-rep(1, ncol(DOCdata))
|
||||
nweights[oddies]<-2
|
||||
matplot(DOCdata, type="l", main="Depth of Coverage by Sample", ylab="Percent bases covered to a given depth", xlab="log(Depth)", log="x", col=ncolors, lty="solid", lwd=nweights)
|
||||
|
||||
if(length(oddies)>0){
|
||||
legend("topright", title="Unusual Cases", legend=colnames(DOCdata)[oddies], lty="solid", lwd=2, col=ncolors[oddies], xjust=0.5)
|
||||
dev.off()
|
||||
|
||||
|
||||
}else{
|
||||
legend("topright", legend="No unusual cases.", bty="n")
|
||||
}
|
||||
|
||||
dev.off()
|
||||
|
||||
}else{
|
||||
print("Depth of Coverage filepath not provided")
|
||||
print("Depth of Coverage--intervals filepath not provided")
|
||||
}
|
||||
|
||||
|
||||
if(is.na(DOCs)==FALSE){
|
||||
pdf(paste(sample_set, "_DOCs.pdf", sep=""), width=6, height=5)
|
||||
if(typeof(DOCs)=="character"){
|
||||
as.data.frame(read.delim(DOCs))->DOC
|
||||
}else{
|
||||
DOCs->DOCdata
|
||||
}
|
||||
par(mar=c(10, 4, 4, 2))
|
||||
boxplot(t(DOC2[,2:ncol(DOC2)]+0.1), log="y", main="Depth of Coverage by Sample", xaxt="n", yaxt="n", ylab="Coverage")
|
||||
|
||||
axis(1, at=c(1:nrow(DOC2)), labels=DOC2[,1], las=2)
|
||||
|
||||
axis(2, at=axTicks(2)+c(0, rep(0.1, length(axTicks(2))-1)), labels=floor(c(0.0, axTicks(2)[2:length(axTicks(2))])))
|
||||
|
||||
labels=floor(c(0.0, axTicks(2)[2:length(axTicks(2))]))
|
||||
|
||||
mtext("Samples", side=1, line=9)
|
||||
|
||||
dev.off()
|
||||
|
||||
}else{
|
||||
print("Depth of Coverage--samples filepath not provided")
|
||||
}
|
||||
|
||||
}
|
||||
if(length(commandArgs(TRUE))>0){
|
||||
stuffmaker(commandArgs(TRUE))
|
||||
|
|
|
|||
Loading…
Reference in New Issue