Very often, when things look to good to be true, it is because they  
aren't.

This thread is all just entertainment for me, but I was under the  
impression that
limma produced corrected P values (from reading the manual):

"Limma provides functions topTable() and decideTests() which summarize  
the results of the
linear model, perform hypothesis tests and adjust the p-values for  
multiple testing. Results
include (log) fold changes, standard errors, t-statistics and p- 
values. The basic statistic used
for signi cance analysis is the moderated t-statistic, which is  
computed for each probe and
for each contrast."

And this being the case, it is quite reasonable to expect a t test on  
a single point to yield a
much more attractive P value than any mechanism that corrects for  
multiple hypothesis testing.

T


On Oct 22, 2009, at 10:51 PM, Gordon K Smyth wrote:

>> [BioC] Very low P-values in limma
>> Thomas Hampton Thomas.H.Hampton at Dartmouth.edu
>> Wed Oct 21 22:04:57 CEST 2009
>>
>> I too find it very odd that a multiple hypothesis correction
>> apparently reduces P values.
>
> It would be odd if it did, but it doesn't.
>
>> One possibility is that
>> the duplicate spot weighting in limma is somehow doing a really
>> superior job compared to a simple t test
>> that might view duplicates on different arrays as the same thing as
>> duplicates on the same array.
>
> It doesn't.  See the documentation.
>
> Best wishes
> Gordon
>
>> Tom
>
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