Anna Pluzhnikov <[EMAIL PROTECTED]> writes:
> Hi,
> I need to run a Fisher's exact test on thousands of 2x2 contingency tables,
> and
> repeat this process several thousand times (this is a part of the permutation
> test for a genome-wide association study).
>
> How can I run this process most efficiently? Is there any way to optimize R
> code?
>
> I have my data in a 2x2xN array (N ~ 5 K; eventually N will be ~ 500 K), and
> use
> apply inside the loop:
> > for (iter in 1:1000) {
> apply(data,3,fisherPval)
> }
> fisherPval <- function(x) {
> fisher.test(x)$p.value
> }
> Right now, it takes about 30 sec per iteration on an Intel Xeon 3.06GHz
> processor.
>
> Thanks in advance.
The appropriate application of phyper() should save you quite a bit,
especially if you're pragmatic and just use the two one-sided tests
rather than the two-sided one which is a bit harder to compute.
(Notice that phyper() is vectorized over all its arguments).
As in:
> M <- array(rpois(2*2*5000,lambda=20),c(2,2,500000))
> x <- M[1,1,]
> m <- M[1,1,]+M[2,1,]
> n <- M[1,2,]+M[2,2,]
> k <- M[1,1,]+M[1,2,]
> system.time(pleft<-phyper(x,m,n,k))
[1] 2.16 0.01 2.16 0.00 0.00
> sum(pleft < 0.05)
[1] 16400
> sum(pleft < 0.05)/500000
[1] 0.0328
--
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c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
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