Hi All, This message can be ignored- I found my mistake. Apologies, Noelle --- "noel0925 at sbcglobal.net" <noel0925 at="" sbcglobal.net=""> wrote: > 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 > >