Hi,

I m currently trying to run some clustering on some expression arrays and I
was wondering about the best way of doing it, I have 81 samples on
hgu133plus2 (55000), I have filtered this down to approximately 10000 (X, Y,
low variabilty, control probes), and wanted to try hierarchical clustering
on these both by arrays and genes. I was planning on using hopach as this
seems an easy and obvious choice. How long would such a lot of comparisons
take? I make it something like  ( 81 * 10000 ) ^ 2 comparisons, I have a
machine with 24gb of memory. Has anybody ever done something like this
before? and what was the amount of time it took to actually do it? Given it
might take a while are there any suggestions for how I might decrease the
running time for such a program? I am already creating the distance matrix
prior to clustering.

Why is it better to use cosangle for gene clustering and euclidean distance
for arrays? Is there a good reason for this and why would you use one
distance over another.

Many thanks in advance,

Nathan

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