Dear list, 

 

I have an experiment with several contrasts associated to different
experimental conditions for each gene, so I want to select genes in a first
step by their moderated-F statistic in order to reduce the number of genes
for multiple contrasts across genes. 

 

For this purpose, I am using decideTests with nestedF, as it is said in
limma user guide (chapter 10, p 51). However, when I use decideTests with
method=”nestedF”, I get quite a few genes that have an adjusted p value in
their separate t-tests contrast greater than 0.9. These genes have a really
small M value as to accept that they are really differentially expressed
between conditions. Does this make sense or I am doing something wrong?.- 

 

The adjusted pvalue that I am talking about is the one that I see after
applying the topTable function for the corresponding single contrast between
two experimental conditions. In this case, the adj.P.Val is the same as the
ones in decideTests with method =”separate”, since we are applying the same
tests. 

 

 

My code is: 

 

decideTests(fit2,method="nestedF",adjust.method=”fdr”,p.value=0,05)

topTable(fit2,coef=i,number=100 adjust=”fdr”) 

 

 

Would be the function p.adjust(fit2$F.p.value,method="BH") (as Gordon
proposed in a recent email about this topic) more appropriate in order to do
what I what to do? 

 

 

Any comment will be of great help to me. 

Thanks a lot.- 

 

p.- 

 


	[[alternative HTML version deleted]]

