Hi,

I am analyzing a NanoString dataset where the metadata look as below:

The factor of interest is the "group" and both groups are found in each mouse ("mouseID") (paired samples). I used DESeq2 for the analysis and the PCA on the normalized data looks like this:

It seems like there is a batch coming from the animals in PC1, although PC2 mildly separates the groups. I have set my design as: ~mouseID+group to account for differences between animals in my downstream DGE analysis. I wanted to ask whether you think I should perform a stronger batch correction (e.g. limma). Also, I read a recent article on NanoString analysis where RUVg() was used for normalization. Below I attach how the samples look before and upon normalization with DESeq2 (counts()). Do you reckon I should use RUVseq offset to normalize the samples?

Thanks in advance!

Agree with swbarnes2. It just seems like the condition effect is very small. Also I'd double check the metadata. Sometimes samples are swapped which can lead to strange looking PCA plots.