[BioC] Clustering like Eisen

[michael watson (IAH-C)]

Hi Marc

It's all in the docs :-)  The functions you will be interested in are:

dist()
cor()
as.dist()
hclust()
as.dendrogram()
cutree()
plot(..., horiz=TRUE)

If you are using cor() as your distance measure, you will need to
convert it from a similarity to a distance by taking 1-cor().

Also heatmap will draw you a nice dendrogram of rows without clustering
the columns if you set the 3rd argument to NA.

Mick

-----Original Message-----
From: Stephen Henderson [mailto:s.henderson at ucl.ac.uk] 
Sent: 18 August 2004 09:06
To: 'mcolosim at brandeis.edu '; 'bioconductor at stat.math.ethz.ch '
Subject: RE: [BioC] Clustering like Eisen


 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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