[BioC] Clustering like Eisen

[Stephen Henderson]

 try

hc<-hclust(dist(t(exprs(Your.exprset))))

plot(hc, hang=-1, labels=A.suitable.vector)

this will give you a complete clustering of samples with the euclidean
distance. This is fine for rma data that is already in log2 units.

You can change the clustering "method" of hclust check ?hclust, or you can
use a different distance method something like 1-cor, but you will have to
then convert it to a distance metric using 

my.metric<- 1-cor(exprs(Your.exprSet))
hc<-hclust(as.dist(my.metric))

or something like that








-----Original Message-----
From: mcolosim at brandeis.edu
To: bioconductor at stat.math.ethz.ch
Sent: 17/08/04 23:34
Subject: [BioC] Clustering like Eisen


Is there a way to create a dendrogram of arrays(samples) like the Eisen
et al
method used in dChip? I don't want a heatmap, just the clustering of
arrays.


I tried kmeans but that doesn't produce something I can plot as a
dendrogram.

Thanks

Marc

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