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This updates the script to produce a more tearsheet-like output for sample set statistics. Formatting will be updated for aesthetic improvements. There are also several database options that currently pull out misleading information because of changes in sequencing methodology that will be updated to show correct information. Eventually, plot formatting will be updated as well and additional informative plots will be added. 

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@4988 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
corin 2011-01-13 15:59:06 +00:00
parent ffae7bf537
commit 6b5474a00a
1 changed files with 162 additions and 112 deletions
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292
R/DataProcessingReport/GetTearsheetStats.R
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@ -1,11 +1,27 @@
##put titles/rownames left
##make titles blue
##decrease margins below titles
## put row names in black
##put background rows in.
##change layouts so that it looks better
##get sample numbers in correctly
.libPaths('~/Sting/R/') #but make sure this is universal
#put use scripts here
suppressMessages(library(gplots));
suppressMessages(library(ReadImages));
suppressMessages(library(gsalib)); suppressMessages(library(gsalib));
suppressMessages(library(ROracle)); suppressMessages(library(ROracle));
cmdargs = getargs( cmdargs = gsa.getargs(
list( list(
bamlist = list(value=NA, doc="list of BAM files"), bamlist = list(value=NA, doc="list of BAM files"),
evalroot = list(value=NA, doc="VariantEval root"), evalroot = list(value=NA, doc="VariantEval root"),
statsout = list(value=NA, doc="Output path for stats"), tearxsout = list(value=NA, doc="Output path for tearsheet PDF"),
plotout = list(value=NA, doc="Output path for PDF") plotout = list(value=NA, doc="Output path for PDF")
), ),
doc="Creates a tearsheet" doc="Creates a tearsheet"
@ -54,156 +70,187 @@ dproj = d[which(squid_fclanes %in% fclanes),];
d2proj = d2[which(d2$"Project" %in% unique(dproj$"Project") & d2$"Sample" %in% dproj$"External ID"),]; d2proj = d2[which(d2$"Project" %in% unique(dproj$"Project") & d2$"Sample" %in% dproj$"External ID"),];
#} #}
## Tear sheet components tearsheetdrop <- "tearsheetdrop.jpg" #put the path to the tearsheet backdrop here
# Project summary tearsheet<-function(){
projects = paste(unique(dproj$"Project"), collapse=", ");
cat(sprintf("Project Summary (%s)\n", projects), file=cmdargs$statsout, append=FALSE);
used_samples = length(unique(dproj$"External ID")); pdf(file= cmdargs$statsout, width=22, height=15, pagecentre=TRUE, pointsize=24)
cat(sprintf("\tUsed samples: %s\n", used_samples), file=cmdargs$statsout, append=TRUE);
unused_samples = 0; #define layout
cat(sprintf("\tUnused samples: %s\n", unused_samples), file=cmdargs$statsout, append=TRUE); layout(matrix(c(1,1,2,4,3,5), ncol=2, nrow=3, byrow=TRUE), heights=c(1, 2.5,2.5), respect=FALSE)
sequencing_protocol = "Hybrid selection"; #prep for title bar
cat(sprintf("\tSequencing protocol: %s\n", sequencing_protocol), file=cmdargs$statsout, append=TRUE); full<-strsplit(cmdargs$evalroot, "/")
name<-strsplit(full[[1]][length(full[[1]])], ".",fixed=TRUE)[[1]][1]
title=paste(name, ": TEAR SHEET", sep="")
drop<-read.jpeg(system.file(tearsheetdrop, package="gsalib"))
bait_design = paste(unique(dproj$"Bait Set"), collapse=", "); #plot title bar
cat(sprintf("\tBait design: %s\n", bait_design), file=cmdargs$statsout, append=TRUE); par(mar=c(0,0,0,0))
plot(drop)
callable_target = paste(unique(dproj$"Target Territory"), collapse=", "); text(110, 45, title, family="serif", adj=c(0,0), cex=3, col=gray(.25))
cat(sprintf("\tCallable target: %s\n", callable_target), file=cmdargs$statsout, append=TRUE);
# Bases summary per lane
cat("\nBases summary (excluding unused lanes/samples)\n", file=cmdargs$statsout, append=TRUE);
cat("\tBases summary per lane\n", file=cmdargs$statsout, append=TRUE);
reads_per_lane_mean = mean(dproj$"PF Reads (HS)");
reads_per_lane_sd = sd(dproj$"PF Reads (HS)");
cat(sprintf("\t\tReads: %s +/- %s\n", reads_per_lane_mean, reads_per_lane_sd), file=cmdargs$statsout, append=TRUE);
used_bases_per_lane_mean = mean(dproj$"PF HQ Aligned Q20 Bases");
used_bases_per_lane_sd = sd(dproj$"PF HQ Aligned Q20 Bases");
cat(sprintf("\t\tUsed bases: %s +/- %s\n", used_bases_per_lane_mean, used_bases_per_lane_sd), file=cmdargs$statsout, append=TRUE);
target_coverage_mean = mean(na.omit(dproj$"Mean Target Coverage"));
target_coverage_sd = sd(na.omit(dproj$"Mean Target Coverage"));
cat(sprintf("\t\tTarget coverage: %0.2fx +/- %0.2fx\n", target_coverage_mean, target_coverage_sd), file=cmdargs$statsout, append=TRUE);
pct_loci_gt_10x_mean = mean(na.omit(dproj$"Target Bases 10x %"));
pct_loci_gt_10x_sd = sd(na.omit(dproj$"Target Bases 10x %"));
cat(sprintf("\t\t%% loci > 10x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_10x_mean, pct_loci_gt_10x_sd), file=cmdargs$statsout, append=TRUE);
pct_loci_gt_20x_mean = mean(na.omit(dproj$"Target Bases 20x %"));
pct_loci_gt_20x_sd = sd(na.omit(dproj$"Target Bases 20x %"));
cat(sprintf("\t\t%% loci > 20x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_20x_mean, pct_loci_gt_20x_sd), file=cmdargs$statsout, append=TRUE);
pct_loci_gt_30x_mean = mean(na.omit(dproj$"Target Bases 30x %"));
pct_loci_gt_30x_sd = sd(na.omit(dproj$"Target Bases 30x %"));
cat(sprintf("\t\t%% loci > 30x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_30x_mean, pct_loci_gt_30x_sd), file=cmdargs$statsout, append=TRUE);
cat("\tBases summary per sample\n", file=cmdargs$statsout, append=TRUE); # Project summary
projects = paste(unique(dproj$"Project"), collapse=", ");
reads_per_sample_mean = mean(d2proj$"PF Reads"); used_samples = length(unique(dproj$"External ID"));
reads_per_sample_sd = sd(d2proj$"PF Reads");
cat(sprintf("\t\tReads: %s +/- %s\n", reads_per_sample_mean, reads_per_sample_sd), file=cmdargs$statsout, append=TRUE);
used_bases_per_sample_mean = mean(d2proj$"PF HQ Aligned Q20 Bases"); unused_samples = 0;
used_bases_per_sample_sd = sd(d2proj$"PF HQ Aligned Q20 Bases");
cat(sprintf("\t\tUsed bases: %s +/- %s\n", used_bases_per_sample_mean, used_bases_per_sample_sd), file=cmdargs$statsout, append=TRUE);
target_coverage_mean = mean(na.omit(d2proj$"Mean Target Coverage")); sequencing_protocol = "Hybrid selection"; #can this be extracted?
target_coverage_sd = sd(na.omit(d2proj$"Mean Target Coverage"));
cat(sprintf("\t\tTarget coverage: %0.2fx +/- %0.2fx\n", target_coverage_mean, target_coverage_sd), file=cmdargs$statsout, append=TRUE);
pct_loci_gt_10x_mean = mean(na.omit(d2proj$"Target Bases 10x %")); bait_design = paste(unique(dproj$"Bait Set"), collapse=", ");
pct_loci_gt_10x_sd = sd(na.omit(d2proj$"Target Bases 10x %"));
cat(sprintf("\t\t%% loci > 10x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_10x_mean, pct_loci_gt_10x_sd), file=cmdargs$statsout, append=TRUE);
pct_loci_gt_20x_mean = mean(na.omit(d2proj$"Target Bases 20x %")); callable_target = paste(unique(dproj$"Target Territory"), collapse=", ");
pct_loci_gt_20x_sd = sd(na.omit(d2proj$"Target Bases 20x %"));
cat(sprintf("\t\t%% loci > 20x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_20x_mean, pct_loci_gt_20x_sd), file=cmdargs$statsout, append=TRUE);
pct_loci_gt_30x_mean = mean(na.omit(d2proj$"Target Bases 30x %")); table1<-rbind(paste(used_samples," used samples/", unused_samples + used_samples," total samples", sep=""), sequencing_protocol, bait_design, callable_target)
pct_loci_gt_30x_sd = sd(na.omit(d2proj$"Target Bases 30x %")); print(nrow(table1))
cat(sprintf("\t\t%% loci > 30x covered: %0.2f%% +/- %0.2f%%\n", pct_loci_gt_30x_mean, pct_loci_gt_30x_sd), file=cmdargs$statsout, append=TRUE); rownames(table1)<-c("Samples","Sequencing Protocol", "Bait Design","Callable Target")
par(mar=c(4,4,4,4))
textplot(table1, col.rownames="darkblue", show.colnames=FALSE, cex=1.25)
title(main=sprintf("Project Summary (%s)\n", projects), family="sans", cex.main=1.75)
# Sequencing summary # Bases summary
reads_per_lane_mean = signif(mean(dproj$"PF Reads (HS)"), 3);
reads_per_lane_sd = signif(sd(dproj$"PF Reads (HS)"), 3);
lanes<-sprintf("%s +/- %s\n", reads_per_lane_mean, reads_per_lane_sd)
used_bases_per_lane_mean = signif(mean(dproj$"PF HQ Aligned Q20 Bases"), 3);
used_bases_per_lane_sd = signif(sd(dproj$"PF HQ Aligned Q20 Bases"), 3);
lanes<-c(lanes, sprintf("%s +/- %s\n", used_bases_per_lane_mean, used_bases_per_lane_sd));
target_coverage_mean = mean(na.omit(dproj$"Mean Target Coverage"));
target_coverage_sd = sd(na.omit(dproj$"Mean Target Coverage"));
lanes<-c(lanes, sprintf("%0.2fx +/- %0.2fx\n", target_coverage_mean, target_coverage_sd));
pct_loci_gt_10x_mean = mean(na.omit(dproj$"Target Bases 10x %"));
pct_loci_gt_10x_sd = sd(na.omit(dproj$"Target Bases 10x %"));
lanes<-c(lanes, sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_10x_mean, pct_loci_gt_10x_sd));
pct_loci_gt_20x_mean = mean(na.omit(dproj$"Target Bases 20x %"));
pct_loci_gt_20x_sd = sd(na.omit(dproj$"Target Bases 20x %"));
lanes<-c(lanes,sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_20x_mean, pct_loci_gt_20x_sd));
pct_loci_gt_30x_mean = mean(na.omit(dproj$"Target Bases 30x %"));
pct_loci_gt_30x_sd = sd(na.omit(dproj$"Target Bases 30x %"));
lanes<-c(lanes,sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_30x_mean, pct_loci_gt_30x_sd));
reads_per_sample_mean = signif(mean(d2proj$"PF Reads"), 3);
reads_per_sample_sd = signif(sd(d2proj$"PF Reads"), 3);
samps<-sprintf("%s +/- %s\n", reads_per_sample_mean, reads_per_sample_sd);
used_bases_per_sample_mean = signif(mean(d2proj$"PF HQ Aligned Q20 Bases"), 3);
used_bases_per_sample_sd = signif(sd(d2proj$"PF HQ Aligned Q20 Bases"), 3);
samps<-c(samps, sprintf("%s +/- %s\n", used_bases_per_sample_mean, used_bases_per_sample_sd));
target_coverage_mean = mean(na.omit(d2proj$"Mean Target Coverage"));
target_coverage_sd = sd(na.omit(d2proj$"Mean Target Coverage"));
samps<-c(samps, sprintf("%0.2fx +/- %0.2fx\n", target_coverage_mean, target_coverage_sd));
pct_loci_gt_10x_mean = mean(na.omit(d2proj$"Target Bases 10x %"));
pct_loci_gt_10x_sd = sd(na.omit(d2proj$"Target Bases 10x %"));
samps<-c(samps, sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_10x_mean, pct_loci_gt_10x_sd));
pct_loci_gt_20x_mean = mean(na.omit(d2proj$"Target Bases 20x %"));
pct_loci_gt_20x_sd = sd(na.omit(d2proj$"Target Bases 20x %"));
samps<-c(samps, sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_20x_mean, pct_loci_gt_20x_sd));
pct_loci_gt_30x_mean = mean(na.omit(d2proj$"Target Bases 30x %"));
pct_loci_gt_30x_sd = sd(na.omit(d2proj$"Target Bases 30x %"));
samps<-c(samps, sprintf("%0.2f%% +/- %0.2f%%\n", pct_loci_gt_30x_mean, pct_loci_gt_30x_sd));
table2<-cbind(lanes, samps)
colnames(table2)<-c("Per lane", "Per sample")
print(nrow(table2))
rownames(table2)<-c("Reads", "Used bases", "Average target coverage", "% loci covered to 10x", "% loci covered to 20x","% loci covered to 30x")
par(mar=c(4,4,4,4))
textplot(table2, rmar=1, col.rownames="dark blue", cex=1.25)
title(main="Bases Summary", family="sans", cex.main=1.75)
cat("\nSequencing summary\n", file=cmdargs$statsout, append=TRUE); cat("\nSequencing summary\n", file=cmdargs$statsout, append=TRUE);
instrument = "Illumina GA2";
cat(sprintf("\tSequencer: %s\n", instrument), file=cmdargs$statsout, append=TRUE);
used_lanes = nrow(dproj); # Sequencing summary
cat(sprintf("\tUsed lanes: %s\n", used_lanes), file=cmdargs$statsout, append=TRUE);
unused_lanes_by_sequencing = 0; instrument = "Illumina GA2";#Can we get this?
unused_lanes_by_analysis = 0; used_lanes = nrow(dproj);
cat(sprintf("\tUnused lanes: %s rejected by sequencing, %s by analysis\n", unused_lanes_by_sequencing, unused_lanes_by_analysis), file=cmdargs$statsout, append=TRUE); unused_lanes_by_sequencing = 0; #can we get this?
unused_lanes_by_analysis = 0;
lanes_per_sample_mean = mean(table(dproj$"External ID"));
lanes_per_sample_sd = sd(table(dproj$"External ID"));
lanes_per_sample_median = median(table(dproj$"External ID"));
cat(sprintf("\tLanes/sample: %0.1f +/- %0.1f lanes (median=%0.1f)\n", lanes_per_sample_mean, lanes_per_sample_sd, lanes_per_sample_median), file=cmdargs$statsout, append=TRUE);
lanes_paired = nrow(subset(dproj, dproj$"Lane Type" == "Paired")); lanes_per_sample_mean = mean(table(dproj$"External ID"));
lanes_widowed = nrow(subset(dproj, dproj$"Lane Type" == "Widowed")); lanes_per_sample_sd = sd(table(dproj$"External ID"));
lanes_single = nrow(subset(dproj, dproj$"Lane Type" == "Single")); lanes_per_sample_median = median(table(dproj$"External ID"));
cat(sprintf("\tLane parities: %s paired, %s widowed, %s single\n", lanes_paired, lanes_widowed, lanes_single), file=cmdargs$statsout, append=TRUE); lanes_paired = nrow(subset(dproj, dproj$"Lane Type" == "Paired"));
lanes_widowed = nrow(subset(dproj, dproj$"Lane Type" == "Widowed"));
lanes_single = nrow(subset(dproj, dproj$"Lane Type" == "Single"));
read_length_mean = mean(dproj$"Mean Read Length (P)"); read_length_mean = mean(dproj$"Mean Read Length (P)");
read_length_sd = sd(dproj$"Mean Read Length (P)"); read_length_sd = sd(dproj$"Mean Read Length (P)");
read_length_median = median(dproj$"Mean Read Length (P)"); read_length_median = median(dproj$"Mean Read Length (P)");
cat(sprintf("\tRead length: %0.1f +/- %0.1f bases (median=%0.1f)\n", read_length_mean, read_length_sd, read_length_median), file=cmdargs$statsout, append=TRUE);
date = dproj$"Run Date"; date = dproj$"Run Date";
date = sub("JAN", "01", date); date = sub("JAN", "01", date);
date = sub("FEB", "02", date); date = sub("FEB", "02", date);
date = sub("MAR", "03", date); date = sub("MAR", "03", date);
date = sub("APR", "04", date); date = sub("APR", "04", date);
date = sub("MAY", "05", date); date = sub("MAY", "05", date);
date = sub("JUN", "06", date); date = sub("JUN", "06", date);
date = sub("JUL", "07", date); date = sub("JUL", "07", date);
date = sub("AUG", "08", date); date = sub("AUG", "08", date);
date = sub("SEP", "09", date); date = sub("SEP", "09", date);
date = sub("OCT", "10", date); date = sub("OCT", "10", date);
date = sub("NOV", "11", date); date = sub("NOV", "11", date);
date = sub("DEC", "12", date); date = sub("DEC", "12", date);
date = date[order(as.Date(date, format="%d-%m-%Y"))]; date = date[order(as.Date(date, format="%d-%m-%Y"))];
start_date = date[1]; start_date = date[1];
end_date = date[length(date)]; end_date = date[length(date)];
cat(sprintf("\tSequencing dates: %s to %s\n", start_date, end_date), file=cmdargs$statsout, append=TRUE);
table3<-rbind(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))
print(nrow(table3))
rownames(table3)<-c("Sequencer", "Used lanes", "Unused lanes","Used lanes/sample", "Lane pariteies", "Read legnths", "Sequencing dates")
par(mar=c(4,4,4,4))
textplot(table3, rmar=1, col.rownames="dark blue", show.colnames=FALSE, cex=1.25)
title(main="Sequencing Summary", family="sans", cex.main=1.75)
# Variant summary # Variant summary
cat("\nVariant summary\n", file=cmdargs$statsout, append=TRUE);
eval.counts = read.csv(paste(cmdargs$evalroot, ".Count_Variants.csv", sep=""), header=TRUE, comment.char="#"); eval.counts = read.csv(paste(cmdargs$evalroot, ".Count_Variants.csv", sep=""), header=TRUE, comment.char="#");
eval.counts.called = subset(eval.counts, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called"); eval.counts.called = subset(eval.counts, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
eval.counts.called.all = subset(eval.counts.called, novelty_name == "all")$nVariantLoci; eval.counts.called.all = subset(eval.counts.called, novelty_name == "all")$nVariantLoci;
eval.counts.called.known = subset(eval.counts.called, novelty_name == "known")$nVariantLoci; eval.counts.called.known = subset(eval.counts.called, novelty_name == "known")$nVariantLoci;
eval.counts.called.novel = subset(eval.counts.called, novelty_name == "novel")$nVariantLoci; eval.counts.called.novel = subset(eval.counts.called, novelty_name == "novel")$nVariantLoci;
eval.titv = read.csv(paste(cmdargs$evalroot, ".Ti_slash_Tv_Variant_Evaluator.csv", sep=""), header=TRUE, comment.char="#"); eval.titv = read.csv(paste(cmdargs$evalroot, ".Ti_slash_Tv_Variant_Evaluator.csv", sep=""), header=TRUE, comment.char="#");
eval.titv.called = subset(eval.titv, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called"); eval.titv.called = subset(eval.titv, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
eval.titv.called.all = subset(eval.titv.called, novelty_name == "all")$ti.tv_ratio; eval.titv.called.all = subset(eval.titv.called, novelty_name == "all")$ti.tv_ratio;
eval.titv.called.known = subset(eval.titv.called, novelty_name == "known")$ti.tv_ratio; eval.titv.called.known = subset(eval.titv.called, novelty_name == "known")$ti.tv_ratio;
eval.titv.called.novel = subset(eval.titv.called, novelty_name == "novel")$ti.tv_ratio; eval.titv.called.novel = subset(eval.titv.called, novelty_name == "novel")$ti.tv_ratio;
vars = matrix(c(eval.counts.called.all, eval.counts.called.known, eval.counts.called.novel, eval.titv.called.all, eval.titv.called.known, eval.titv.called.novel, "3.0 - 3.2", "3.2 - 3.4", "2.7 - 3.0"), nrow=3); table4 = matrix(c(eval.counts.called.all, eval.counts.called.known, eval.counts.called.novel, eval.titv.called.all, eval.titv.called.known, eval.titv.called.novel, "3.0 - 3.2", "3.2 - 3.4", "2.7 - 3.0"), nrow=3);
rownames(vars) = c("All", "Known", "Novel"); print(nrow(table4))
colnames(vars) = c("Found", "Ti/Tv ratio", "Expected Ti/Tv ratio");
cat(sprintf("\t\tFound\tTi/Tv ratio\tExpected Ti/Tv ratio\n"), file=cmdargs$statsout, append=TRUE); rownames(table4) = c("All", "Known", "Novel");
cat(sprintf("\tAll\t%s\t%s\t\t%s\n", eval.counts.called.all, eval.titv.called.all, "3.0 - 3.2"), file=cmdargs$statsout, append=TRUE); colnames(table4) = c("Found", "Ti/Tv ratio", "Expected Ti/Tv ratio");
cat(sprintf("\tKnown\t%s\t%s\t\t%s\n", eval.counts.called.known, eval.titv.called.known, "3.2 - 3.4"), file=cmdargs$statsout, append=TRUE);
cat(sprintf("\tNovel\t%s\t%s\t\t%s\n", eval.counts.called.novel, eval.titv.called.novel, "2.7 - 3.0"), file=cmdargs$statsout, append=TRUE);
textplot(table4, rmar=1, col.rownames="dark blue", cex=1.25)
title(main="Variant Summary", family="sans", cex.main=1.75)
dev.off()
}
tearsheet()
# Plots # Plots
plots<-function(){
eval.bysample = read.csv(paste(cmdargs$evalroot, ".SimpleMetricsBySample.csv", sep=""), header=TRUE, comment.char="#"); eval.bysample = read.csv(paste(cmdargs$evalroot, ".SimpleMetricsBySample.csv", sep=""), header=TRUE, comment.char="#");
eval.bysample.called = subset(eval.bysample, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called"); eval.bysample.called = subset(eval.bysample, evaluation_name == "eval" & comparison_name == "dbsnp" & jexl_expression == "none" & filter_name == "called");
eval.bysample.called.all = subset(eval.bysample.called, novelty_name == "all"); eval.bysample.called.all = subset(eval.bysample.called, novelty_name == "all");
@ -243,3 +290,6 @@ points(eval.func.called.novel$Synonymous[ind] / (eval.func.called.novel$Missense
legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21); legend("topright", c("All", "Known", "Novel"), col=c("black", "blue", "red"), pt.cex=1.3, pch=21);
dev.off(); dev.off();
}
plots()