In a DESeq2: Appropriate way to deal with knockouts in experiment design (RIPSeq) I asked about how DESeq2 could be used to take the effects of sequencing from knockout (KO) tissue. This is possible by using interactions. If I do this, the results column returns a "basemean". Looking at some other posts, I understand this is calculated by taking the mean of the normalized count data.
I would like to run clustering or classification algorithms on my counts AFTER the effect of the KO sequences have been taken into account. Would it be appropriate to use the data from basemean to do this? In other words, assuming I have built my model to take into account the interaction of the KO, is basemean giving me normalized counts adjusted for the effect of the KO? Looking at how it is calculated, I'm not so sure but I might be misunderstanding something...
The second issue is that according to DESeq2 baseMean counts, basemean returned by DESeq2 does not take transcript length into account so it's probably not appropriate for my downstream applications. If I use the
counts() function though, I don't see how I can get back counts after taking into account the KO tissue. It just gives me back all the counts for all samples.
So is there a way to obtain normalized counts from DESeq2 that I can use in downstream applications that have taken into account the effects from my KO sequences?