Hi All,

I am trying to grasp the different options for decideTests in Limma. I have read
many of the postings, but still am confused. Some of these postings left me with
the following questions.

For the decideTests function in Limma, the method, "separate" is considered less stringent than the others. Does this mean least "stringent" in terms of identifying DE genes?  
So is this method also considered to be the most conservative- eg it identifies
fewer gene and also fewer false positives? Or is that a separate issue? I plan
to use BH's FDR correction at say the 0.05 level so I realize then that within
my list of DE genes, the expected number of FPs is less than 5%. So, this is
only indirectly related to the method- separate, hierarchical, global, or nestedF?

This posting:
https://stat.ethz.ch/pipermail/bioconductor/2005-July/009542.html
also states that "for example, the nestedF method is most
powerful for picking up genes which change in multiple conditions, but is
possibly least powerful for picking up genes which are different in only one
condition". So then if you expect that some genes will change in many conditions
(contrasts of interest) but that certain genes will be altered only in a certain
condition, which approach do you choose?
Is the "separate" approach the safest bet?


Also as regards a separate but related posting:
https://stat.ethz.ch/pipermail/bioconductor/2006-February/011970.html
about obtaining individual p-values corresponding to the nestedF method.
Here the post reads:
"No, there is no way to get individual p-values corresponding to the nestedF
method.  You can however just used the overall F-test p-values, fit$F.p.value,
which will give you p-values at the probe level rather than the contrast level.
Use p.adjust(fit$F.p.value) to get adjusted p-values."

Isn't it possible to obtain the individual p-values corresponding
to the nestedF method using the following:

NestedF<-decideTests(fit2,method="nestedF",adjust.method="BH",p.value=0.05)
write.fit(fit2, results=NestedF, "NestedF_FDRadjusted_P0.05.txt", adjust="fdr", sep="\t")

Here I obtain a table with p.values for each of my contrasts and a separate one
for the overall F-test. These values differ from that obtained from say topTable
which is eqivalent to performing decideTests with method="separate" 
when the same multiple testing correction is used, namely "BH" with p=0.05.

So, if these are not the individual p-values from the nestedF method, then what
do they represent?

Thanks in advance, 
Noelle


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