**60**wrote:

I have a design of 6 conditions with three replicates each, with expression levels for ~51000 transcription products. I'm using Limma's voom transformation to make my data approximately Gaussian, which by ocular pat-down seems to be the case:

The mean-variance estimation is visible here - kinda overdispersed in the mid range but it doesn't look critically bad to me:

I fit the linear model thusly:

> labels [1] A A A C C C G G G L L L T T T U U U Levels: A C G L T U design <- model.matrix(~labels) fit2 <- eBayes( lmFit(voomCounts, design) )

However, this yields the following p-value distribution from the pairwise t-tests (looks like a misspecified model)

The fit2$F.p.vals has only 10-15 genes *above *0.01, the rest are absolutely tiny; and when I plot some of the genes called for differential expression they look very unremarkable. What am I doing wrong?

Thanks so much :)

**2.1k**• written 5.7 years ago by Amusement Pork •

**60**