ecluster.fn <- function(data, distance.method, linkage.method, key.txt=NULL,
                        clab=NULL, rlab=NULL, clab.col=NULL, rlab.col=NULL, gamma=0.75){

  array.hclust <- hclust( dist(t(data),distance.method), linkage.method) # cluster the arrays/columns
  genes.hclust <- hclust( dist(data, distance.method), linkage.method)   # cluster the genes/rows
  genes.dend <- as.dendrogram(genes.hclust)

  data.re <- data[ rev(genes.hclust$order), array.hclust$order ] # reorder the data
  def.par <- par()
  nf <- layout(matrix(c(4,3,2,1),2,2,byrow=TRUE), c(1,3), c(1,3), TRUE); layout.show(nf)

  if(is.null(rlab))     rlab <- dimnames(data.re)[[1]] else rlab <- rlab[rev(genes.hclust$order)]
  if(is.null(clab))     clab <- dimnames(data.re)[[2]] else clab <- clab[array.hclust$order]
  if(is.null(rlab.col)) rlab.col <- 1 else rlab.col <- rlab.col[rev(genes.hclust$order)]
  if(is.null(clab.col)) clab.col <- 1 else clab.col <- clab.col[array.hclust$order]
 
  par(mar=c(1,1,1,1))
  plot.mat(data.re, nrgcol=100, rlabels=rlab, clabels=clab, rcols=rlab.col, ccols= clab.col, gamma=gamma)

  par(mar=c(0,3,0.25,2))
  plot(genes.dend, horiz=T, ann=F, axes=F)

  par(mar=c(1,0.25,1,0.25))
  plclust(array.hclust, axes=F, ann=F, label=F, hang=-1)

  par(mar=c(0,0.25,1,0.25))
  plot.new()
  text(0.25, 0, paste(dim(data.re)[1], "genes used."), cex=0.8, adj=c(0,0)) # Number of genes used
  if(is.null(key.txt)==F){
    for(i in 1:length(key.txt)){
      text(0.25, 0.125*i, key.txt[i], col=i, cex=0.8, adj=c(0,0))}}
}