topn_pheatmap.R

topn_pheatmap = function (xx, topn, topsd, pdffile, annoRow=NULL, annoCol=NULL, isScaled = F, breaks, ...){## top SD genes
	xx.sd = apply(xx, 1, sd)
	xx.sd = xx.sd[order(xx.sd, decreasing=T)]

	if(missing(topn) & missing(topsd)){ print("topn or topsd not assigned"); return}
	top = 0
	if(!missing(topn)){ top = names(xx.sd[1:topn])}
	if(!missing(topsd)){ top = names(xx.sd[xx.sd > topsd])}

	cat("total rows selected: ", length(top), "\n")

	tmp = xx[row.names(xx) %in% top, ]
	cn = colnames(tmp)
	matrix_tmp = t(apply(tmp, 1, scale))
	matrix_tmp[matrix_tmp > 2] = 2
	matrix_tmp[matrix_tmp < -2] = -2
	colnames(matrix_tmp) = cn
	dim(matrix_tmp)

	if(missing(breaks)){
		breaks = c( seq(from = -2, to = 2, length.out = 30))
	}

	tf = apply(!is.na(as.matrix(matrix_tmp)), 1, all)
	matrix_tmp = matrix_tmp[tf, ]

	pdf(pdffile)
	hp = pheatmap(matrix_tmp, color=greenred(length(breaks)+1), scale='none', clustering_method = 'complete', 
		      cluster_rows = TRUE, cluster_cols = TRUE, show_colnames = T, fontsize_row = 9, show_rownames = F,
		      annotation_col = annoCol, annotation_row = annoRow)
	dev.off()
}