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Beautiful labels, better choice of dimension ranges. Supports fast loading of just first N records for testing. 

git-svn-id: file:///humgen/gsa-scr1/gsa-engineering/svn_contents/trunk@3964 348d0f76-0448-11de-a6fe-93d51630548a
This commit is contained in:
depristo 2010-08-06 23:17:32 +00:00
parent d3bebe0f2c
commit b63d64bbbc
1 changed files with 39 additions and 11 deletions
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50
R/VariantRecalibratorReport/VariantRecalibratorReport.R
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@ -20,6 +20,10 @@ maxVariants = args[5];
if (is.na(maxVariants)) { maxVariants = -1; } if (is.na(maxVariants)) { maxVariants = -1; }
maxVariants = as.integer(maxVariants) maxVariants = as.integer(maxVariants)
greedy = args[6]
if (is.na(greedy)) { greedy = -1; }
greedy = as.integer(greedy)
getAnnIndex <- function(d, ann) { getAnnIndex <- function(d, ann) {
index = -1; index = -1;
for (i in c(1:length(names(d)))) { for (i in c(1:length(names(d)))) {
@ -111,6 +115,32 @@ read.clusters <- function(filename) {
clusters; clusters;
} }
clusterLimits <- function( vals, defaultMin, defaultMax ) {
x = c(max(defaultMin, min(vals, -2)), min(defaultMax, max(vals, 2)))
print(x)
x
}
makeAxis <- function( num, vals, off1, mult1, xmin, xmax ) {
#labels=as.integer(seq(from=min(vals), to=max(vals), by=(abs(min(vals)) + abs(max(vals)))/5))
#at=seq(from=min((vals - off1)/mult1), to=max((vals - off1)/mult1), by=(abs(min((vals - off1)/mult1)) + abs(max((vals - off1)/mult1)))/5)
#from = xmin * mult1 + off1
#to = xmax * mult1 + off1
#print(list(off1=off1, mult1=mult1, xmin=xmin, xmax=xmax))
at = as.integer(seq(from=xmin, to=xmax, by=(abs(xmin) + abs(xmax))/5))
labels = as.integer(at * mult1 + off1)
#print(list(from=from, to=to, by=(abs(from) + abs(to))/5))
#print(list(labels=labels, at=at))
axis(num, labels=labels, at=at);
# axis(num,
# labels=as.integer(seq(from=min(vals), to=max(vals), by=(abs(min(vals)) + abs(max(vals)))/5)),
# at=seq(from=min((vals - off1)/mult1), to=max((vals - off1)/mult1), by=(abs(min((vals - off1)/mult1)) + abs(max((vals - off1)/mult1)))/5)
# );
}
plotClusters <- function(d.known, d.novel, d.loci, c, ann1, ann2, filename, maxVariants = -1) { plotClusters <- function(d.known, d.novel, d.loci, c, ann1, ann2, filename, maxVariants = -1) {
index1 = getAnnIndex(d.known, ann1); index1 = getAnnIndex(d.known, ann1);
index2 = getAnnIndex(d.known, ann2); index2 = getAnnIndex(d.known, ann2);
@ -125,8 +155,11 @@ plotClusters <- function(d.known, d.novel, d.loci, c, ann1, ann2, filename, maxV
off2 = c[[1]]$conversions[[cindex2]]$offset; off2 = c[[1]]$conversions[[cindex2]]$offset;
# todo -- should only include min/max actually observed if bounds are bigger than actual distributions # todo -- should only include min/max actually observed if bounds are bigger than actual distributions
xvalsForLims = c(-5, 5) xvalsForLims = clusterLimits(d.known[,index1], -4, 4)
yvalsForLims = c(-5, 5) yvalsForLims = clusterLimits(d.known[,index2], -4, 4)
#d.known = d.known[(d.known[,index1]-off1)/mult1 >= xvalsForLims[1] & (d.known[,index1]-off1)/mult1 <= xvalsForLims[2] & (d.known[,index2]-off2)/mult2 >= yvalsForLims[1] & (d.known[,index2]-off2)/mult2 <= yvalsForLims[2],]
#xvalsForLims = (d.known[,index1] - off1)/mult1 #xvalsForLims = (d.known[,index1] - off1)/mult1
#yvalsForLims = (d.known[,index2] - off2)/mult2 #yvalsForLims = (d.known[,index2] - off2)/mult2
xlims = c(min(xvalsForLims), 1.2*max(xvalsForLims)); xlims = c(min(xvalsForLims), 1.2*max(xvalsForLims));
@ -177,14 +210,8 @@ plotClusters <- function(d.known, d.novel, d.loci, c, ann1, ann2, filename, maxV
points(ellipse(t(cov), centre=mu), type="l", lwd=lineweight, col=color); points(ellipse(t(cov), centre=mu), type="l", lwd=lineweight, col=color);
} }
axis(1, makeAxis(1, d.novel[,index1], off1, mult1, xvalsForLims[1], xvalsForLims[2])
labels=as.integer(seq(from=min(d.novel[,index1]), to=max(d.novel[,index1]), by=(abs(min(d.novel[,index1])) + abs(max(d.novel[,index1])))/5)), makeAxis(2, d.novel[,index2], off2, mult2, yvalsForLims[1], yvalsForLims[2])
at=seq(from=min((d.novel[,index1] - off1)/mult1), to=max((d.novel[,index1] - off1)/mult1), by=(abs(min((d.novel[,index1] - off1)/mult1)) + abs(max((d.novel[,index1] - off1)/mult1)))/5)
);
axis(2,
labels=as.integer(seq(from=min(d.novel[,index2]), to=max(d.novel[,index2]), by=(abs(min(d.novel[,index2])) + abs(max(d.novel[,index2])))/5)),
at=seq(from=min((d.novel[,index2] - off2)/mult2), to=max((d.novel[,index2] - off2)/mult2), by=(abs(min((d.novel[,index2] - off2)/mult2)) + abs(max((d.novel[,index2] - off2)/mult2)))/5)
);
if (!is.na(d.loci)) { if (!is.na(d.loci)) {
legend("bottomleft", c("Known", "Novel", "Suspicious loci"), col=c("blue", "red", "yellow3"), pch=19); legend("bottomleft", c("Known", "Novel", "Suspicious loci"), col=c("blue", "red", "yellow3"), pch=19);
@ -224,7 +251,8 @@ if (!is.na(lociFile)) {
l = t$POS; l = t$POS;
} }
d = read.table(vcfTable, header=TRUE); print("Greedy reading")
d = read.table(vcfTable, header=TRUE, nrows = greedy);
c = read.clusters(clusterFile); c = read.clusters(clusterFile);
d.known = d[which(d$DB == 1),]; d.known = d[which(d$DB == 1),];