**110**wrote:

I am doing differential expression on a fairly large RNA-Seq dataset, where multiple organs are obtained from the same mice. I would like to model the effects of individual mice using limma's random effects via `duplicateCorrelation`

. As library sizes are very consistent across all samples (and I might want to play around with `genas`

), I was thinking of using limma-trend.

I've seen some posts on using `voom`

and `voomWithArrayWeights`

together with `duplicateCorrelation`

, but nothing on limma-trend.

Given that a standard limma-trend pipeline goes as follows (starting from a count matrix `EM`

and design `mod`

):

dge <- DGEList(EM) dge <- calcNormFactors(dge) v <- cpm(dge, log=TRUE, prior.count=3) fit <- lmFit(v, design=mod) eb <- eBayes(fit, trend=TRUE, robust=TRUE) decideTests(eb) topTable(eb) ...

How would this be modified to accommodate `duplicateCorrelation`

and/or `arrayWeights`

?

**36k**• written 18 months ago by maltethodberg •

**110**