Identify The Effect Of Covariates With Count Data

Question

[Floris Brenk]

Hi all,

I have about 100 count data libraries from different individuals. Now I want to identify the effect of each covariate that is has on the expression of all genes and preferably as output an adjusted p-value. Covariates are age, rin, pmi, tissuebank, batch. Does anyone knows an easy way to do this?

Thanks

Answer 1

[Devon Ryan]

Any of the methods incorporating a GLM will work (i.e., DESeq2, edgeR, etc.). Alternatively, just use voom() to get things into limma and use it. There are a huge number of examples of using limma for this.

[Floris Brenk]

Thanks for your reply. I was reading the DESeq2 manual, but cant find the word covariate anywhere (http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.pdf) Do you have some example page of this?

[Devon Ryan]

Covariates are just additional components in a model, there's nothing special about them. They're treated the same as everything, just see section 1.5 for a start (their examples are all factors, but continuous variables work in the same manner).