[BioC] straight t vs. bonferroni vs. all the new stuff.

[Sean Davis]

Matthew Lyon wrote:
> Esteemed List:
>
> i need an alpha value for a t-test with about n=450,000 and a
> 1) df of 2
> 2) df of 4
>
> this is microarray data. i've been told bonferroni is too conservative for 
> microarrays, hence interesting approaches like multtest, the q-value 
> permuted one, etc...
>
> can anyone who deals in this area extensively (say, expression data) give me 
> a ballpark value for t- or alpha- that's typically giving good 'oh man this 
> is significantly different!' results ? i've got my own hunches but would 
> like some blinded numbers tossed at me too.
>   
Look at the p.adjust() function if you already have p-values computed by 
a t-test as a place to start.  Bonferroni should probably never be used, 
as I think the Holm correction has the same assumptions but is less 
conservative (you get something for nothing...).  Some of the more 
stats-minded folks might be able to ellaborate on that particular point, 
but Holm is probably also too conservative.

Sean