R/matrixPlot.R

In ABarray: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data.

#- $Id: matrixPlot.R,v 1.1.1.1 2006/06/06 22:06:37 sunya Exp $

#- Produce heatmap with a given matrix

"matrixPlot" <-
function (x, nrgcols = 50, rlabels = TRUE, clabels = TRUE, 
    rcols = 1, ccols = 1, k = 10, title = "", ...) 
{
	# Figure out the layout, last column should be at least 0.90 in
  figWidth <- par("fin")[1]
  colLayout <- floor(figWidth / 0.9)
  op <- par(no.readonly = TRUE) # the whole list of settable par's.
  layout(matrix(c(rep(1, colLayout-1), 2), 2, colLayout, byrow=TRUE))
  
  color <- rgcolorsfunc(nrgcols)
  n <- nrow(x)
  p <- ncol(x)

  rlabels <- rownames(x)
  clabels <- colnames(x)
	 #- Check the largest character length for the label and adjust for margins
  titleHeight = 4
  rMax <- max(nchar(rlabels)) + 1
  cMax <- max(nchar(clabels)) + titleHeight + 2
  if (cMax > 18) {
    cMax = 18
  }
  if (rMax > 15) {
    rMax = 15
  }
  
  par(mar = c(2, rMax, cMax, 1))
  
  image(1:p, 1:n, t(x[n:1, ]), col = color, 
        axes = FALSE, xlab = "", ylab = "", ...)
  if (length(ccols) == 1) {
    axis(3, at = 1:p, labels = clabels, las = 2, cex.axis = 1, 
         col.axis = ccols)
  }
  if (length(ccols) == p) {
    cols <- unique(ccols)
    for (i in 1:length(cols)) {
      which <- (1:p)[ccols == cols[i]]
      axis(3, at = which, labels = clabels[which], las = 2, 
           cex.axis = 0.6, col.axis = cols[i])
    }
  }
  if (length(rcols) == 1) {
    axis(2, at = n:1, labels = rlabels, las = 2, cex.axis = 1, 
         col.axis = rcols)
  }
  if (length(rcols) == n) {
    cols <- unique(rcols)
    for (i in 1:length(cols)) {
      which <- (1:n)[rcols == cols[i]]
      axis(2, at = (n:1)[which], labels = rlabels[which], 
           las = 2, cex.axis = 0.6, col.axis = cols[i])
    }
  }
  mtext(title, side = 3, line = cMax - titleHeight, cex = 2)
  box()
  
  scaleColorBar(x, col = color, k = k, cLen = cMax)

	 #- return to default margins
  par(mar = c(5, 4, 4, 2) + 0.1)
  par(op)  #- reset par to oldpar
}

##########################################################
#- $Log: matrixPlot.R,v $
#- Revision 1.1.1.1  2006/06/06 22:06:37  sunya
#- ABarray project converted from ab1700 project
#-
#- Revision 1.2  2006/03/14 19:48:31  sunya
#- Changed icp (internal control probe) QC plots.
#- Added function for icp -> icpPlot
#- ANOVA analysis now performs probe filtering, but no FDR is calculated.
#- hclusterPlot now calculate correlation coefficient for probes, previously
#- it used Euclidian distance. The distance between arrays is still Euclidean.
#-