[BioC] multiple testing with 54000 genes

[Dipl.-Ing. Johannes Rainer]

exactly! the smalles p value i get is exactly 0.00024 (to be more 
precise 0.0002442 :) ). i thought 12 samples should be enough to 
calculate p values using permutation. what do you mean with parametric 
null? something like t-test? i'm sorry for my questions, but i am not a 
statistician (not yet...)

thanks!


Quoting "James W. MacDonald" <jmacdon at med.umich.edu>:

> Dipl.-Ing. Johannes Rainer wrote:
>> hi,
>>
>> i wanted to ask if someone has experience in multiple testing with a 
>> large number of genes.
>>
>> i have in total 24 Affymetrix chips (hgu133plus2), 12 patients, for 
>> every patient an 0 hours and 6 hours after treatment sample. i 
>> calculated p values using permutation (mt.maxT function with 
>> test="pairt") and corrected for multiple testing using the Benjamini 
>> Hochberg method. the problem is, that with that large number of 
>> tests (54675 genes and therefore 54675 tests) after adjusting the p 
>> values no gene shows a "significant" difference.
>>
>> i will now reduce the number of genes to test to get to some results.
>> has anyone experienced similar problems?
>
> You probably don't have enough samples to use a permuted null 
> distribution. I believe the smallest p-value you can get with a 
> permuted null is going to be ~0.00024, which may not be small enough 
> to survive a multiplicity correction with that many genes. I would 
> imagine you would get better results if you used a parametric null 
> (e.g., using the limma package).
>
>
> Best,
>
> Jim
>
>
>>
>> thanks, jo
>>
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>
> -- 
> James W. MacDonald
> Affymetrix and cDNA Microarray Core
> University of Michigan Cancer Center
> 1500 E. Medical Center Drive
> 7410 CCGC
> Ann Arbor MI 48109
> 734-647-5623
>