speeding up 1000s of coxph regression?

I have a gene expression matrix n (genes) X p (cases), where n = 8000 and p
= 100. I want to fit each gene as univariate in a coxph model, i.e., fitting
8000 models. I do something like this:

res <- apply(data, 1, coxph.func)

which takes about 4 min, not bad. But I need to do large numbers of
permutations of the data (permuting the columns), for example, 2000, which
would take 5 days. I would like to know if there is way to speed this up?

Calling coxph.fit directly would likely be faster.

Also, you probably don't need to do 2000 permutations on all 8000 genes: a
few hundred permutations is probably enough to decide that most of the
genes aren't interesting.

If you are going to be doing a lot of this sort of thing it might be worth
looking at the parallel processing facilities in the `snow' package.
There's a description of their use in another gene expression problem in
the new R Newsletter.


	-thomas

Thomas Lumley <tlumley at u.washington.edu> writes:

If you are going to be doing a lot of this sort of thing it might be worth
looking at the parallel processing facilities in the `snow' package.
There's a description of their use in another gene expression problem in
the new R Newsletter.

Actually, they used the RPVM package directly; however, Thomas is
still correct, it probably would be simple to recast using SNOW.

Some hints and details can be found in a tech report by Luke Tierney,
Michael Li, and myself in the UW Biostat tech report series (can't
recall which #, but it's on http://www.bepress.com/uwbiostat/).

best,
-tony

A.J. Rossini  /  rossini at u.washington.edu  /  rossini at scharp.org
Biomedical/Health Informatics and Biostatistics, University of Washington.
Biostatistics, HVTN/SCHARP, Fred Hutchinson Cancer Research Center.
FHCRC: 206-667-7025 (fax=4812)|Voicemail is pretty sketchy/use Email 

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