I am analyzing some RNA-seq data in which we have pairs of samples from the same individuals, before and after treatment. For example, we might have 4 samples:
- Individual 1, bfore treatment
- Individual 1, after treatment
- Individual 2, before treatment
- Individual 2, after treatment
Unfortunately, as far as I can tell most standard RNA-seq tools will treat my data as simply a set of 2 pre-treatment samples and another set of 2 post-treatment samples, with no regard for the fact that they are matched pairs of samples from the same individuals. That is, the statistical test being performed is essentially testing for differences between two (or more) groups of unlabeled samples. In contrast, I want to test for consistent changes in response to treatment across individuals. Is there an analysis program or package for RNA-Seq data that supports matched pairs of samples like this?
Note that for now I am not interested in alternative splicing, but rather just testing at the gene level for differential expression.
As an example of the limitation I am looking to overcome, consider this quote from the conclusion of the baySeq paper which confirms what I have said above:
... at present these methods remain limited to comparisons involving multiple groups, and are not able to account for, for example, paired samples.
It seems that at least DESeq, edgeR, and cuffdiff share the same limitation.
So far I have simply performed DESeq in 1v1 mode for each individual, then collected the differential gene sets and looked for common entries in a post-process step. It's not statistically rigorous, and I'm going to look into matted's suggestion of edgeR. He said edgeR's documentation explains paired sample design!