Re: [R] permutated p values vs. normal p values

From: Berton Gunter <>
Date: Wed 17 Aug 2005 - 09:07:13 EST

A **guess** ... subject to correction by others.

If you had large systematic error in your experiment, nothing will turn out "significant" (which is what you saw).

If you permute the data so that the systematic error becomes "random", you'll get a random number of significant p-values, which is what you saw.

If the samples came from animals (or people),-- possibly performed over time by differnet people at diffeent labs (sites)-- large systematic error that would overwhelm small sample size is not unusual.Lack of explicit and careful randomization/cage effects in animal experiments/ equipment and calibration issues are some possible sources for such error.

OTOH, what I just said might be pure nonsense, so caveat emptor.

"The business of the statistician is to catalyze the scientific learning process." - George E. P. Box    

> -----Original Message-----
> From:
> [] On Behalf Of Steve Adams
> Sent: Tuesday, August 16, 2005 3:14 PM
> To:
> Subject: [R] permutated p values vs. normal p values
> Hi, I am performing Cox proportional hazards
> regression on a microarray dataset with 15000 genes.
> The p values generated from the Cox regression (based
> on normal distribution of large sample theory) showed
> only 2 genes have a p value less than 0.05. However,
> when I did a permutation on the dataset to obtained
> permutated p values, and it turned out about 750 genes
> had a permutated p value less than 0.05 (that just
> happens to be equal to the number of significant genes
> you would expect by chance alone). With that big
> difference in the number of significant genes, which
> one should I trusted? and what's reason why such a big
> difference exists? My dataset is not large in sample
> size (17 samples), might this be the reason?
> Thanks
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> mailing list PLEASE do read the posting guide! Received on Wed Aug 17 09:14:22 2005

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