Hello,
I am running a RNA Seq Analysis on a number of samples from a certain livestock organism. I ran a very typical pipeline using featurecounts for the counts portion of the analysis, RPKM for normalization and DEseq2 and EdgeR both for the differential gene analysis portion. However, because these are samples from different tissues and not from different conditions(i.e. drug vs control) the differential gene analysis results are very overblown(i.e. way too many genes look differentially expressed). For example, I have 4 replicates from the brain, 4 replicates from muscle, and another 4 from kidneys. Are there any tools or methods our that there that take this kind of thing into account in order to get better results? Alternatively, are there statistical methods or cutoffs that I should change or be made aware of in my analysis? I know this is a very vague question, but this is my first time running an analysis like this and I am still learning a lot. Any help would be greatly appreciated.
Hello,
What is the biological question, you want to answer with such an experiment? Of course you can compare brain-samples with kidney-samples. But these two organs have totally different functions. Since there are different metabolic and signalling pathways active, any comparison will lead to "way too many" differentially expressed genes.
For instance, this publication shows a large scale transcriptome analysis for different organs.