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

[Matthew Lyon]

cool thanx.

Thank You,

Matthew Lyon        UC  Riverside                    lab (951) 827-4736
Ph.D. Student        B O T A N Y                new c.p. (951) 941-5554
Citrus Genomics                   apt (951) 328-9930
http: // int - citrusgenomics . org /           messengers: ptrifoliata
mattlyon at mattlyon.com ptrifoliata at hotmail.com  mlyon003 at student.ucr.edu




>From: Sean Davis <sdavis2 at mail.nih.gov>
>To: Matthew Lyon <ptrifoliata at hotmail.com>
>CC: bioconductor at stat.math.ethz.ch
>Subject: Re: [BioC] straight t vs. bonferroni vs. all the new stuff.
>Date: Thu, 19 Oct 2006 14:17:50 -0400
>
>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