A simple R script to visualize OncoPrints out of an alteration matrix

basicOncoPrint.R

# This function sorts the matrix for better visualization of mutual exclusivity across genes
memoSort <- function(M) {
	geneOrder <- sort(rowSums(M), decreasing=TRUE, index.return=TRUE)$ix;
	scoreCol <- function(x) {
		score <- 0;
		for(i in 1:length(x)) {
			if(x[i]) {
				score <- score + 2^(length(x)-i);
			}
		}
		return(score);
	}
	scores <- apply(M[geneOrder, ], 2, scoreCol);
	sampleOrder <- sort(scores, decreasing=TRUE, index.return=TRUE)$ix;
	return(M[geneOrder, sampleOrder]);
}

# This is the plotting function
oncoPrint <- function(M, sort=TRUE) {
	if(sort) {
		alts <- memoSort(M);		
	} else {
		alts <- M;
	}

	ngenes <- nrow(alts);
	nsamples <- ncol(alts);
	coverage <- sum(rowSums(alts) > 0);

	### OncoPrint
	numOfOncos <- ngenes*nsamples;
	oncoCords <- matrix( rep(0, numOfOncos * 5), nrow=numOfOncos );
	colnames(oncoCords) <- c("xleft", "ybottom", "xright", "ytop", "altered");

	xpadding <- .01;
	ypadding <- .01;
	cnt <- 1;
	for(i in 1:ngenes) {
		for(j in 1:nsamples) {
			xleft <- j-1 + xpadding;
			ybottom <- ((ngenes-i+1) -1) + ypadding;
			xright <- j - xpadding;
			ytop <- (ngenes-i+1) -ypadding;
			altered <- alts[i, j];
			
			oncoCords[cnt, ] <- c(xleft, ybottom, xright, ytop, altered);
			cnt <- cnt+1;
		}
	}

	colors <- rep("lightgray", cnt);
	colors[ which(oncoCords[, "altered"] == 1) ] <- "black";
	plot(c(0, nsamples), c(0, ngenes), type="n", main=sprintf("Gene set altered in %.2f%%: %d of %d cases", coverage/nsamples*100, coverage, nsamples), xlab="Samples", ylab="", yaxt="n");
	rect(oncoCords[, "xleft"], oncoCords[, "ybottom"],oncoCords[, "xright"], oncoCords[, "ytop"], col=colors, border="white");
	axis(2, at=(ngenes:1)-.5, labels=rownames(alts), las=2);
}

# M is the alteration matrix with T => Altered, F => Not-altered
# Rows are genes, columns are samples
M <- matrix(rep(FALSE, 100*3), ncol=100, nrow=3);
rownames(M) <- c("Gene1", "Gene2", "Gene2");
colnames(M) <- paste("Sample", 1:100, sep="");
M[1, 1:25] <- TRUE;
M[2, 26:50] <- TRUE;
M[3, 51:75] <- TRUE;

# We don't care about the order, so samples can be ordered in any way before plotting
M <- M[, sample(1:100, 100)];
# End of custom alteration matrix

# Create the plot
oncoPrint(M);

# You can also disable the sorting
oncoPrint(M, sort=FALSE);