255 lines
8.3 KiB
R
255 lines
8.3 KiB
R
library(ellipse);
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library(hexbin);
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getAnnIndex <- function(d, ann) {
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index = -1;
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for (i in c(1:length(names(d)))) {
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if (names(d)[i] == ann) {
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index = i;
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}
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}
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index;
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}
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getClusterAnnIndex <- function(c, ann) {
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index = -1;
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for (i in c(1:length(c[[1]]$anns))) {
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if (c[[1]]$anns[i] == ann) {
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index = i;
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}
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}
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index;
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}
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plotAnn <- function(d.known, d.novel, d.loci, ann) {
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index = getAnnIndex(d.known, ann);
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k = hist(d.known[,index], breaks=100, plot=FALSE);
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n = hist(d.novel[,index], breaks=100, plot=FALSE);
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plot(k$mids, k$density, type="b", col="blue", ylim=c(0, max(k$density)), lwd=2, xlab=ann, ylab="Density", bty="n");
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points(n$mids, n$density, type="b", col="red", lwd=2);
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if (!is.na(d.loci)) {
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legend("topright", c("Known", "Novel", "Suspicious loci"), col=c("blue", "red", "yellow3"), pch=c(21, 21, 18));
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} else {
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legend("topright", c("Known", "Novel"), col=c("blue", "red"), pch=21);
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}
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if (!is.na(d.loci)) {
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for (i in c(1:nrow(d.loci))) {
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points(d.loci[i, index], 0, col="yellow3", pch=18, cex=2.0);
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}
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}
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}
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read.clusters <- function(filename) {
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con = file(filename, "r", blocking = FALSE)
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lines = readLines(con)
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close(con);
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anns = c();
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annIndex = 1;
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clusterIndex = 1;
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clusters = c();
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conversions = c();
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for (line in lines) {
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if (length(grep("ANNOTATION", line)) > 0) {
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linePieces = unlist(strsplit(line, ","));
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anns = c(anns, linePieces[2]);
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conversions[[annIndex]] = list(ann = linePieces[2], offset = as.numeric(linePieces[3]), multiplier = as.numeric(linePieces[4]));
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annIndex = annIndex + 1;
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} else if (length(grep("CLUSTER", line)) > 0) {
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linePieces = unlist(strsplit(line, ","));
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mixtureWeight = linePieces[2];
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mu = linePieces[3:(3+length(anns)-1)];
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cov = linePieces[(3+length(anns)):length(linePieces)];
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clusters[[clusterIndex]] = list(
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anns = anns,
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conversions = conversions,
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mixtureWeight = as.numeric(mixtureWeight),
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means = as.numeric(mu),
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cov = matrix(cov, nrow=length(anns), ncol=length(anns))
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);
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clusterIndex = clusterIndex + 1;
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}
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}
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clusters;
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}
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clusterLimits <- function( vals, defaultMin, defaultMax ) {
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x = c(max(defaultMin, min(vals, -2)), min(defaultMax, max(vals, 2)))
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print(x)
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x
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}
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getClusterColor <- function(clusterIndex, nClusters) {
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clusterColors(nClusters)[clusterIndex]
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}
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clusterColors <- function(nClusters) {
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rainbow(nClusters)
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}
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makeAxis <- function( num, vals, off1, mult1, xmin, xmax ) {
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#labels=as.integer(seq(from=min(vals), to=max(vals), by=(abs(min(vals)) + abs(max(vals)))/5))
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#at=seq(from=min((vals - off1)/mult1), to=max((vals - off1)/mult1), by=(abs(min((vals - off1)/mult1)) + abs(max((vals - off1)/mult1)))/5)
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#from = xmin * mult1 + off1
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#to = xmax * mult1 + off1
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#print(list(off1=off1, mult1=mult1, xmin=xmin, xmax=xmax))
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at = as.integer(seq(from=xmin, to=xmax, by=(abs(xmin) + abs(xmax))/5))
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labels = as.integer(at * mult1 + off1)
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#print(list(from=from, to=to, by=(abs(from) + abs(to))/5))
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#print(list(labels=labels, at=at))
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axis(num, labels=labels, at=at);
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# axis(num,
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# labels=as.integer(seq(from=min(vals), to=max(vals), by=(abs(min(vals)) + abs(max(vals)))/5)),
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# at=seq(from=min((vals - off1)/mult1), to=max((vals - off1)/mult1), by=(abs(min((vals - off1)/mult1)) + abs(max((vals - off1)/mult1)))/5)
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# );
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}
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plotClusters <- function(d.known, d.novel, d.loci, c, ann1, ann2, filename, maxVariants = -1) {
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index1 = getAnnIndex(d.known, ann1);
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index2 = getAnnIndex(d.known, ann2);
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cindex1 = getClusterAnnIndex(c, ann1);
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cindex2 = getClusterAnnIndex(c, ann2);
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mult1 = c[[1]]$conversions[[cindex1]]$multiplier;
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off1 = c[[1]]$conversions[[cindex1]]$offset;
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mult2 = c[[1]]$conversions[[cindex2]]$multiplier;
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off2 = c[[1]]$conversions[[cindex2]]$offset;
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xvalsForLims = clusterLimits(d.known[,index1], -4, 4)
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yvalsForLims = clusterLimits(d.known[,index2], -4, 4)
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xlims = c(min(xvalsForLims), 1.2*max(xvalsForLims));
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ylims = c(min(yvalsForLims), max(yvalsForLims));
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# par(mar=c(5, 6, 2, 5));
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plot(0, 0, type="n", xaxt="n", yaxt="n", xlim=xlims, ylim=ylims, xlab=ann1, ylab=ann2, bty="n");
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mv.known = if (maxVariants == -1 | maxVariants >= nrow(d.known)) { seq(1, nrow(d.known)) } else { as.integer(runif(maxVariants, 1, nrow(d.known)+1))}
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mv.novel = if (maxVariants == -1 | maxVariants >= nrow(d.novel)) { 1:nrow(d.novel) } else { as.integer(runif(maxVariants, 1, nrow(d.novel)+1)) }
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print(dim(mv.known))
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print(maxVariants)
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points(((d.known[,index1] - off1)/mult1)[mv.known], ((d.known[,index2] - off2)/mult2)[mv.known], pch=19, cex=0.3, col="#0000FF33");
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points(((d.novel[,index1] - off1)/mult1)[mv.novel], ((d.novel[,index2] - off2)/mult2)[mv.novel], pch=19, cex=0.3, col="#FF000033");
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nClusters = length(c)
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for (clusterIndex in c(1:nClusters)) {
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mu = c(c[[clusterIndex]]$means[cindex1], c[[clusterIndex]]$means[cindex2]);
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cov = matrix(as.numeric(
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matrix(
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c(
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c[[clusterIndex]]$cov[cindex1,cindex1],
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c[[clusterIndex]]$cov[cindex2,cindex1],
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c[[clusterIndex]]$cov[cindex1,cindex2],
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c[[clusterIndex]]$cov[cindex2,cindex2]
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),
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nrow=2, ncol=2
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)
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), nrow=2, ncol=2
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);
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weight = c[[clusterIndex]]$mixtureWeight;
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color = getClusterColor(clusterIndex, nClusters);
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lineweight = ifelse(weight > 0.50, 4, 3);
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points(mu[1], mu[2], pch=21, col=color, cex=0.5);
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points(ellipse(t(cov), centre=mu), type="l", lwd=lineweight, col=color);
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}
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makeAxis(1, d.novel[,index1], off1, mult1, xvalsForLims[1], xvalsForLims[2])
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makeAxis(2, d.novel[,index2], off2, mult2, yvalsForLims[1], yvalsForLims[2])
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# add points legend on the lower left
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if (!is.na(d.loci)) {
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legend("bottomleft", c("Known", "Novel", "Suspicious loci"), col=c("blue", "red", "yellow3"), pch=19);
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} else {
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legend("bottomleft", c("Known", "Novel"), col=c("blue", "red"), pch=19);
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}
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# add upper right legend with cluster id and weights
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weights = round(sapply(c, function(x) x$mixtureWeight),2)
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clusterNames = paste("C", paste(1:nClusters), sep="")
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clusterLegendNames = paste(clusterNames, weights, sep="-W=")
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legend("topright", clusterLegendNames, fill=clusterColors(nClusters))
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if (!is.na(d.loci)) {
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points((d.loci[,index1] - off1)/mult1, (d.loci[,index2] - off2)/mult2, pch=19, cex=0.8, col="yellow3");
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}
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}
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args = commandArgs(TRUE);
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plotRoot = args[1];
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if (is.na(plotRoot)) { plotRoot = "test"; }
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clusterFile = args[2];
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if (is.na(clusterFile)) { clusterFile = "/Volumes/gsa-hpprojects/dev/depristo/oneOffProjects/1000GenomesProcessingPaper/wgs.v8/GA2.WEx.cleaned.ug.snpfiltered.indelfiltered.optimized"; }
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vcfTable = args[3];
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if (is.na(vcfTable)) { vcfTable = "/Volumes/gsa-hpprojects/dev/depristo/oneOffProjects/1000GenomesProcessingPaper/wgs.v8/GA2.WEx.cleaned.ug.snpfiltered.indelfiltered.optimized.table"; }
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lociFile = args[4];
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if (is.na(lociFile) | lociFile == "NA" ) { lociFile = NA; }
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maxVariants = args[5];
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if (is.na(maxVariants)) { maxVariants = 5000; }
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maxVariants = as.integer(maxVariants)
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greedy = args[6]
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if (is.na(greedy)) { greedy = -1; }
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greedy = as.integer(greedy)
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l = c();
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if (!is.na(lociFile)) {
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t = read.table(lociFile, header=TRUE);
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l = t$POS;
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}
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print("Greedy reading")
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d = read.table(vcfTable, header=TRUE, nrows = greedy);
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c = read.clusters(clusterFile);
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d.known = d[which(d$DB == 1 | d$ID != "."),];
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d.novel = d[which(d$DB == 0 | d$ID == "."),];
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d.loci = NA;
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if (length(l) > 0) {
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d.loci = d[which(d$POS %in% l),];
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}
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pdf(paste(plotRoot, ".clusterReport.pdf", sep=""));
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for (ann1 in c[[1]]$anns) {
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print(ann1)
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plotAnn(d.known, d.novel, d.loci, ann1);
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for (ann2 in c[[1]]$anns) {
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if (ann1 != ann2) {
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print(paste("-- v ", ann2))
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plotClusters(d.known, d.novel, d.loci, c, ann1, ann2, maxVariants=maxVariants);
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}
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}
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}
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dev.off();
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