374 lines
19 KiB
R
374 lines
19 KiB
R
##put titles/rownames left
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##make titles blue
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##decrease margins below titles
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## put row names in black
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##put background rows in.
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##change layouts so that it looks better
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##get sample numbers in correctly
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.libPaths('/humgen/gsa-firehose2/pipeline/repositories/StingProduction/R/')
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suppressMessages(library(gplots));
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suppressMessages(library(ReadImages));
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suppressMessages(library(gsalib));
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suppressMessages(library(ROracle));
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cmdargs = gsa.getargs(
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list(
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yaml = list(value=NA, doc="pipeline YAML file"),
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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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),
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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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for (bam in bamlist) {
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bamheader = system(paste("samtools view -H", bam), intern=TRUE);
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if (length(bamheader) > 0) {
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rgs = bamheader[grep("^@RG", bamheader)];
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for (rg in rgs) {
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id = grep("PU:", unlist(strsplit(rg, "\t")), value=TRUE);
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id = sub("PU:", "", id);
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id = gsub("XX......", "XX", id)
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if (length(unlist(strsplit(id, "\\.")))==3){
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indexed<-c(indexed, id)
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}
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else{
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if(length(unlist(strsplit(id, "\\.")))==2){
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nonindexed<-c(nonindexed, id)
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}
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else{
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print(id + " is a strange PU and will result in odd searches")
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}
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}
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}
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} else {
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print(sprintf("Could not load '%s'\n", bam));
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}
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}
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drv = dbDriver("Oracle");
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con = dbConnect(drv, "REPORTING/REPORTING@ora01:1521/SEQPROD");
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rs = dbSendQuery(con, statement = paste("SELECT * FROM ILLUMINA_PICARD_METRICS"));
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d = fetch(rs, n=-1);
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dbHasCompleted(rs);
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dbClearResult(rs);
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rs2 = dbSendQuery(con, statement = paste("SELECT * FROM ILLUMINA_SAMPLE_STATUS_AGG"));
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d2 = fetch(rs2, n=-1);
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dbHasCompleted(rs2);
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dbClearResult(rs2);
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oraCloseDriver(drv);
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squid_fclanes = sprintf("%s.%s", d$"Flowcell", d$"Lane");
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squid_fclanes_indexed = sprintf("%s.%s.%s", d$"Flowcell", d$"Lane", d$"Barcode");
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dproj = d[which(squid_fclanes %in% nonindexed),];
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dproj = rbind(dproj, d[which(squid_fclanes_indexed %in% indexed),])
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dproj = dproj[which(dproj$"Project" %in% unique(squids)),]
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d2proj = d2[which(d2$"Project" %in% unique(dproj$Project) & d2$"Sample" %in% dproj$"External ID"),];
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tearsheet<-function(){
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tearsheetdrop <- "data/tearsheetdrop.jpg" #put the path to the tearsheet backdrop here
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pdf(file= cmdargs$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$evalroot, "/")
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name<-strsplit(full[[1]][length(full[[1]])], ".",fixed=TRUE)[[1]][1]
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drop<-read.jpeg(system.file(tearsheetdrop, package="gsalib"))
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#plot title bar
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par(mar=c(0,0,0,0))
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plot(drop)
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text(155, 50, name, family="serif", adj=c(0,0), cex=3, col=gray(.25))
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# Project summary
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projects = paste(unique(dproj$"Project"), collapse=", ");
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used_samples = length(bamlist);
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unused_samples = 0;
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sequencing_protocol = "Hybrid selection"; #can this be extracted?
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bait_design = paste(dimnames(table(dproj$"Bait Set"))[[1]][order(table(dproj$"Bait Set"), decreasing=TRUE)], collapse=", ");
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if(nchar(bait_design)>50){
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bait_design<-strsplit(bait_design, ", ")[[1]][1]
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}
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if(nchar(bait_design)>50){
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bait_design<-strsplit(bait_design, ".Homo")[[1]][1]
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}
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callable_target = paste(na.omit(unique(dproj$"Target Territory")), collapse=", ");
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table1<-rbind(paste(used_samples," used samples/", unused_samples + used_samples," total samples", sep=""), sequencing_protocol, bait_design, callable_target)
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print(nrow(table1))
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rownames(table1)<-c("Samples","Sequencing Protocol", "Bait Design","Callable Target")
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par(mar=c(0,0,1,0))
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textplot(table1, col.rownames="darkblue", show.colnames=FALSE, cex=1.25, valign="top")
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title(main=sprintf("Project Summary (%s)\n", projects), family="sans", cex.main=1.25, line=-1)
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# Bases summary
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reads_per_lane_mean = format(mean(dproj$"PF Reads (HS)", na.rm=TRUE), 8, 3,1, scientific=TRUE);
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reads_per_lane_sd = format(sd(dproj$"PF Reads (HS)", na.rm=TRUE), 8, 3,1, scientific=TRUE);
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lanes<-sprintf("%s +/- %s\n", reads_per_lane_mean, reads_per_lane_sd)
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used_bases_per_lane_mean = format(mean(dproj$"PF HQ Aligned Q20 Bases", na.rm=TRUE),8, 3,1, scientific=TRUE);
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used_bases_per_lane_sd = format(sd(dproj$"PF HQ Aligned Q20 Bases", na.rm=TRUE), 8, 3,1, scientific=TRUE);
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lanes<-c(lanes, sprintf("%s +/- %s\n", used_bases_per_lane_mean, used_bases_per_lane_sd));
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target_coverage_mean = mean(na.omit(dproj$"Mean Target Coverage"));
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target_coverage_sd = sd(na.omit(dproj$"Mean Target Coverage"));
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lanes<-c(lanes, sprintf("%0.2fx +/- %0.2fx\n", target_coverage_mean, target_coverage_sd));
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pct_loci_gt_10x_mean = mean(na.omit(dproj$"Target Bases 10x %"));
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pct_loci_gt_10x_sd = sd(na.omit(dproj$"Target Bases 10x %"));
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lanes<-c(lanes, sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_10x_mean, pct_loci_gt_10x_sd));
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pct_loci_gt_20x_mean = mean(na.omit(dproj$"Target Bases 20x %"));
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pct_loci_gt_20x_sd = sd(na.omit(dproj$"Target Bases 20x %"));
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lanes<-c(lanes,sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_20x_mean, pct_loci_gt_20x_sd));
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pct_loci_gt_30x_mean = mean(na.omit(dproj$"Target Bases 30x %"));
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pct_loci_gt_30x_sd = sd(na.omit(dproj$"Target Bases 30x %"));
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lanes<-c(lanes,sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_30x_mean, pct_loci_gt_30x_sd));
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reads_per_sample_mean = format(mean(d2proj$"PF Reads", na.rm=TRUE), 8, 3,1, scientific=TRUE);
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reads_per_sample_sd = format(sd(d2proj$"PF Reads",na.rm=TRUE), 8, 3,1, scientific=TRUE);
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samps<-sprintf("%s +/- %s\n", reads_per_sample_mean, reads_per_sample_sd);
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used_bases_per_sample_mean = format(mean(d2proj$"PF HQ Aligned Q20 Bases", na.rm=TRUE),8, 3,1, scientific=TRUE);
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used_bases_per_sample_sd = format(sd(d2proj$"PF HQ Aligned Q20 Bases", na.rm=TRUE), 8, 3,1, scientific=TRUE);
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samps<-c(samps, sprintf("%s +/- %s\n", used_bases_per_sample_mean, used_bases_per_sample_sd));
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target_coverage_mean = mean(na.omit(d2proj$"Mean Target Coverage"));
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target_coverage_sd = sd(na.omit(d2proj$"Mean Target Coverage"));
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samps<-c(samps, sprintf("%0.2fx +/- %0.2fx\n", target_coverage_mean, target_coverage_sd));
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pct_loci_gt_10x_mean = mean(na.omit(d2proj$"Target Bases 10x %"));
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pct_loci_gt_10x_sd = sd(na.omit(d2proj$"Target Bases 10x %"));
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samps<-c(samps, sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_10x_mean, pct_loci_gt_10x_sd));
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pct_loci_gt_20x_mean = mean(na.omit(d2proj$"Target Bases 20x %"));
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pct_loci_gt_20x_sd = sd(na.omit(d2proj$"Target Bases 20x %"));
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samps<-c(samps, sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_20x_mean, pct_loci_gt_20x_sd));
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pct_loci_gt_30x_mean = mean(na.omit(d2proj$"Target Bases 30x %"));
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pct_loci_gt_30x_sd = sd(na.omit(d2proj$"Target Bases 30x %"));
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samps<-c(samps, sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_30x_mean, pct_loci_gt_30x_sd));
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table2<-cbind(lanes, samps)
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colnames(table2)<-c("Per lane", "Per sample")
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print(nrow(table2))
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rownames(table2)<-c("Reads", "Used bases", "Average target coverage", "% loci covered to 10x", "% loci covered to 20x","% loci covered to 30x")
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par(mar=c(0,0,1,0))
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textplot(table2, rmar=1, col.rownames="dark blue", cex=1.25, valign="top")
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title(main="Bases Summary", family="sans", cex.main=1.25, line=0)
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# Sequencing summary
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instrument <- c();
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if(length(grep("AAXX", dproj$Flowcell))>0){
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instrument <- c(instrument, "Illumina GA2")
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}
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if(length(grep("ABXX", dproj$Flowcell))>0){
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instrument <- c(instrument, "Illumina HiSeq")
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}
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if(length(instrument)>1){
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instrument<-paste(instrument[1], instrument[2], sep=" and ")
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}
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used_lanes = nrow(dproj);
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unused_lanes_by_sequencing = 0; #can we get this?
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unused_lanes_by_analysis = 0;
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lanes_per_sample_mean = mean(table(dproj$"External ID"), na.rm=TRUE);
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lanes_per_sample_sd = sd(table(dproj$"External ID"), na.rm=TRUE);
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lanes_per_sample_median = median(table(dproj$"External ID"));
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lanes_paired = nrow(subset(dproj, dproj$"Lane Type" == "Paired"));
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lanes_widowed = nrow(subset(dproj, dproj$"Lane Type" == "Widowed"));
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lanes_single = nrow(subset(dproj, dproj$"Lane Type" == "Single"));
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read_length_mean = mean(dproj$"Mean Read Length (P)");
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read_length_sd = sd(dproj$"Mean Read Length (P)");
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read_length_median = median(dproj$"Mean Read Length (P)");
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date = dproj$"Run Date";
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# date = sub("JAN", "01", date);
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# date = sub("FEB", "02", date);
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# date = sub("MAR", "03", date);
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# date = sub("APR", "04", date);
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# date = sub("MAY", "05", date);
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# date = sub("JUN", "06", date);
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# date = sub("JUL", "07", date);
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# date = sub("AUG", "08", date);
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# date = sub("SEP", "09", date);
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# date = sub("OCT", "10", date);
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# date = sub("NOV", "11", date);
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# date = sub("DEC", "12", date);
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date = date[order(as.Date(date, format="%d-%m-%Y"))];
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start_date = date[1];
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end_date = date[length(date)];
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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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# 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")$nVariantLoci;
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eval.titv = read.csv(paste(cmdargs$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")$ti.tv_ratio;
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table4 = matrix(c(eval.counts.called.all, eval.counts.called.known, eval.counts.called.novel, eval.titv.called.all, eval.titv.called.known, eval.titv.called.novel, "3.0 - 3.2", "3.2 - 3.4", "2.7 - 3.0"), nrow=3);
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print(nrow(table4))
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print(paste("columns should be three, actually are:", ncol(table4)))
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rownames(table4) = c("All", "Known", "Novel");
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colnames(table4) = c("Found", "Ti/Tv ratio", "Expected Ti/Tv ratio");
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par(mar=c(0,0,0,0))
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textplot(table4, rmar=1, col.rownames="dark blue", cex=1.25, valign="top")
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title(main="Variant Summary", family="sans", cex.main=1.25, line=-2)
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#plots
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eval.bysample = read.csv(paste(cmdargs$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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#boxplot(eval.bysample.called.all$CountVariants, eval.bysample.called.known$CountVariants, eval.bysample.called.novel$CountVariants, names=c("All", "Known", "Novel"), ylab="Variants per sample", main="", cex=1.3, cex.lab=1.3, cex.axis=1.3);
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par(mar=c(5, 4, 4, 2) + 0.1)
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ind = order(eval.bysample.called.all$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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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$Nonsense)[ind], cex=1.3, col="red");
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#legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
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dev.off()
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}
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tearsheet()
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# Plots
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plots<-function(){
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eval.bysample = read.csv(paste(cmdargs$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.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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pdf(cmdargs$plotout);
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boxplot(eval.bysample.called.all$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$CountVariants)/2, c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
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plot(eval.ac.called.all$AC, eval.ac.called.all$n, col="black", type="l", lwd=2, cex=1.3, cex.lab=1.3, cex.axis=1.3, xlab="Allele count", ylab="Number of variants", main="", log="xy", bty="n");
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points(eval.ac.called.known$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$n, col="red", type="l", lwd=2);
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legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), lwd=2);
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plot(eval.func.called.all$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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legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
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dev.off();
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}
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