[BioC] p-value adjustment

[Matthew Hannah]

One thing to add on this point that might be of interest is that an
initial look at p.adjust
p.adjust(p, method = p.adjust.methods, n = length(p))
would suggest that including NAs would result in more conservative
correction as n > # of p-values, but it doesn't (at least for fdr) as
the NAs are ordered below the minimum p-value from your vector in the
calculation. So if you fdr correct with the NAs present then you get
more significant p-values than you should and the results are
meaningless - and there's no error message.

Obviously, the nice work by the developers will correct this.

>>Wenbin Liu wnbnl at yahoo.com
>>Wed Mar 23 23:51:28 CET 2005
>>
>>Dear list,
>>
>>I'm puzzled by the R function p.adjust with NA values
>>in the first argument. The adjustment gives different
>>result with and without the NAs.
>
>Please refer to the extensive recent discussion on this topic on the 
>R-devel mailing list.
>
>>  The question then is,
>>which approach should one take: with or without NAs in
>>the first argument?
>
>The limma package functions remove NAs before using p.adjust(), and I 
>believe this is virtually always correct in the microarray context.
>
>Gordon
>
>>Thanks a lot!
>>
>>Wenbin