[R] clustering problem

[rex.dwyer at syngenta.com]

Don't you expect it to be a lot faster if you cluster 20 items instead of 25000?

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Maxim
Sent: Wednesday, March 02, 2011 4:08 PM
To: r-help at r-project.org
Subject: [R] clustering problem

Hi,

I have a gene expression experiment with 20 samples and 25000 genes each.
I'd like to perform clustering on these. It turned out to become much faster
when I transform the underlying matrix with t(matrix). Unfortunately then
I'm not anymore able to use cutree to access individual clusters. In general
I do something like this:

hc <- hclust(dist(USArrests), "ave")

library(RColorBrewer)
library(gplots)
clrno=3
cols<-rainbow(clrno, alpha = 1)
clstrs <- cutree(hc, k=clrno)
ccols <- cols[as.vector(clstrs)]
heatcol<-colorRampPalette(c(3,1,2), bias = 1.0)(32)
heatmap.2(as.matrix(USArrests), Rowv=as.dendrogram(hc),col=heatcol,
trace="none",RowSideColors=ccols)

Nice, I can access 3 main clusters with cutree. But what about a situation
when I perform hclust like

hc <- hclust(dist(t(USArrests)), "ave")

which I have to do in order to speed up the clustering process. This I can
plot with:

heatmap.2(as.matrix(USArrests), Colv=as.dendrogram(hc),col=heatcol,
trace="none")

But where do I find information about the clustering that was applied to the
rows?
cutree(hc, k=clrno) delivers the clustering on the columns, so what can I do
to access the levels for the rows?
I guess the solution is easy, but after ours of playing around I thought it
might be a good time to contact the mailing list!

Maxim

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