[BioC] Clustering like Eisen

[mcolosim at brandeis.edu]

Quoting Stephen Henderson <s.henderson at ucl.ac.uk>:

>  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
> 

Thanks for the help. I think this creates the same type of cluster dendrogram
as dCHIP:

my.metric<- 1-cor(exprs(Your.exprSet))
hc <- hcluster(as.dist(my.metric), method = "centroid")
plot(hc, hang=-1)

The two missing pieces where centroid and hang=-1.

Marc