I am thankful for How Do I Draw A Heatmap In R With Both A Color Key And Multiple Color Side Bars? in R where you can add row and column information together with the color mapping information. I use it all the time to make pictures of hierarchically clustered expression profiles measured by expression arrays.
Usually I scale the input matrix on rows (probesets) to Z-scores in order to make weigh equally in the clustering. The colors of the heatmap are mapped linearly to the Z-scores. \
The problem is, when you have a few extremes in your data set, they tend to make your heatmap a little faint. In the attached heatmap you can see that the Z-scores range from somewhere around -10 to 10, however, about 99% is in the range of -4 to 4. I would like to change the way colors are mapped to the Z-scores in such a way that the green-to-red goes from -4 to 4 and the colors of the Z-scores outside this range just do not become any more green or red then they are already, any ideas?
# import gplots for greenred colors
library(gplots)
# generate random matrix
x<-replicate(100,rnorm(100,0,1))
# draw heatmap
heatmap.3(x,density.info="histogram",col=greenred)
# introduce outliers
x[1,1] <- -10
x[2,2] <- 10
# draw heatmap with outliers
heatmap.3(x,density.info="histogram",col=greenred)
3 answers
You can use the breaks argument to do this, from the heatmap.2 documentation:
breaks (optional) Either a numeric vector indicating the splitting points for binning x into colors, or a integer number of break points to be used, in which case the break points will be spaced equally between min(x) and max(x)
To use to achieve your desired effect:
#breaks for the core of the distribution
breaks=seq(-4, 4, by=0.2) #41 values
#now add outliers
breaks=append(breaks, 10)
breaks=append(breaks, -10, 0)
#create colour panel with length(breaks)-1 colours
mycol <- colorpanel(n=length(breaks)-1,low="green",mid="black",high="red")
#now plot heatmap - I'm using heatmap.2, but am assuming this will work with Obi's heatmap.3
heatmap.2(x,density.info="histogram",col=mycol, trace='none', breaks=breaks)
The major drawback is that the outliers are a lot less obvious, but clearly the heatmap overall is a lot less 'washed out'.
Results:
Before
After
you could simply change your matrix by changing all value over 4 to 4 and all value under -4 to -4
Or write a little function
plotHeatmap <- function(M,up=4,down=-4){
M[M>up]<- up
M[M<(-down)]<- down
print(heatmap.3(M,density.info="histogram",col=greenred))
}
You can also apply a logarithmic scaling onto the values, this will greatly compress the range.
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