Sorry if this question has already been addressed, I did not really found an answer on the site...
I have a dataset of microarray (agilent 1 color) with ~35 condition (corresponding to 3 time series, but with different time points, and where time cannot be consider as a continuous variable), 2 rep each. I have imported them with limma and normalized with vsn normalization.
I would like to identify gene that change in at some point in one of the time course only (corresponding to 7 conditions), like an anova or a likelihood ratio test, in order to perform clustering after.
From the vignette I understood that the F-test of limma would give me what I want, correct?
In the vignette example, to do it on 3 condition, a contrast is made with all pairwise comparisons ; do I really have to do that? if I just use lmfit() and eBayes():
> fit <- lmFit(arrayNorm, design)
> it <- eBayes(fit))
I got a F.p.Value in my fit object, does this correspond to the F-test for all the element in the design matrix?
If so, to perform the test on just a subset of my data, can I just give a design matrix with only the samples and factors that I am interested in?
I understood from few post online that Benjamini Hochberg correction is not optimal for F-test (am I wrong?), but I did not really got what should I use instead in this case. Could someone light me on that?