This is a test version of Biostars. For the public version, visit https://www.biostars.org.
Combined p-values for DE genes from RNA-seq meta-analysis

Hello, I have raw p-values from edgeR for two comparisons of interest: control vs genotype1 and control vs genotype2. I'd like to see if there is an aggregate effect of genotype on DE.

The Internet has a number of answers on how to do a Fisher's combined test, and some resources on how to do this in R (such as the metaRNASeq package). But edgeR (as well as DESeq2) give two-sided p-values, while the R functions for combined tests requires a one-sided p-value. Does anyone know how to proceed? I'm also not sure if this is a valid way to get an aggregate result, since these two tests have the same control samples and are not independent. Does anyone have thoughts on whether it would be better to run the analysis in edgeR again, treating genotype1 and genotype2 as one group?

rna-seq meta analysis fisher
• 3,626 views • link
updated by chris86 chris86 • written by ks • ks

You haven't explained which studies are separate? Are control vs genotype1 and control vs genotype2 two completely separate experiments with new controls and new genotype samples?

• 1 views • link
written by chris86 chris86

No, they are from the same experiment -- the samples sequenced were control, genotype1, and genotype2.

• 1 views • link
written by ks • ks

1 answer

You can use limma for RNA-seq analysis now it has been updated. You can see in Aarons answer on this page he describes how to do contrasts for a A+B versus C comparison which is what you seem to want to do. I think this is what you are looking for - at a guess.

con.2 <- makeContrasts(A - (B+C)/2, levels=design)

https://support.bioconductor.org/p/69764/

• 1 views • link
written by chris86 chris86

Log in to answer this question.

More posts like this