Is there an easy way to add a per-gene covariate like GC content when I fit the GLMs in DESeq? I'm only able to find per-sample covariates in the vignettes.
A broader question is: how can I determine if I need to consider GC content when I do a differential expression analysis? I've plotted fold change vs. GC content and pvalues vs. GC, and there's no noticeable trend. I figured I would run with GC content as a covariate and see if things change drastically...
-------------------
The DESeq GLM formulas I'm using now have two per-sample confounders:
form0 <- count ~ dem.group + sex
form1 <- count ~ condition + dem.group + sex
I'm using DESeq_1.10.1.
A broader question is: how can I determine if I need to consider GC content when I do a differential expression analysis? I've plotted fold change vs. GC content and pvalues vs. GC, and there's no noticeable trend. I figured I would run with GC content as a covariate and see if things change drastically...
-------------------
The DESeq GLM formulas I'm using now have two per-sample confounders:
form0 <- count ~ dem.group + sex
form1 <- count ~ condition + dem.group + sex
I'm using DESeq_1.10.1.
Comment