Hi Sunny,

Sorry for the confusion. 
In fact, tp is actually one coefficent. i.e. tp 
<-topTable(fit,coef=1,n=Inf,adj="fdr").

Regards,
Xiaokuan




________________________________
From: Sunny Srivastava <research.baba@gmail.com>

Cc: bioconductor <bioconductor@stat.math.ethz.ch>
Sent: Mon, September 27, 2010 1:46:21 PM
Subject: Re: [BioC] applying p.adjust() in limma

Hello Wei, 
If I understand your question correctly, most probably

tp$adj.P.Val is the adjusted p value for the moderated F statistic (would be 
different from the moderated t if the number of treatments is more than 2)

When you calculate the p-value separately for the three comparisons and use
p.adjust for all of them, this may not be equal to the p values you obtained 
from F statistic(s).

Thanks,
S.






Dear List,
>
>Sorry to bring up this adjusted p value question about limma. I searched mail
>list but didn't find an answer.
>
>I know limma use "p.adjust()" for multiple test correction. And if there is 
only
>one coefficent, the adjust p values results of limma's top table is the same as
>I directly apply p.adjust to the P.Value vector.
>
>My question is that if we have several coefficent, for example, 3 comparisons,
>how does limma use the p.adjust()?
>I tried to feed p.adjust() as: p.adjust(tp$P.Value,
>method="fdr",n=length(tp$P.Value) * 3), but the results are different from the
>tp$adj.P.Val.
>
>Could some let me know how limma apply p.adjust() to handle this situation
>(several comparisons/coefficents)?
>
>Thank you.
>
>Regards,
>Xiaokuan
>
>
>
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