I have RNA Seq data from mouse and human skin ( 2 replicates each) and want to compare the expression of the orthologous genes to find any which are differentially expressed. I have quantile normalized the gene expression matrix across all 4 samples (2 mouse + 2 human). I eventually want to calculate the log fold change in expression of all orthologous genes between the 2 species. However before I do this, I should control for gene length, right? Will this be enough to give me an idea of the differentially expressed genes or should I be employing more sophisticated methods? Any comments would be helpful. Thanks a lot.
It depends on what type of data you have, really. There are methods developed solely for quantifying relative expression based on count data, such as using edgeR or limma-voom.
You don't need to correct for gene length to estimate fold-changes of relative expression, what you need to do is normalise by library size first (and in the process obtain log2 ((counts + 0.5)/1e+06) and then, following quantile normalisation, you can just calculate mouse - human or human - mouse to give you an estimate of fold change.
I still would recommend using something a bit more sophisticated like limma-voom for this task, though, because that also will enable you to get stuff like false-discovery rates for your fold changes.