I use RUVSeq and I find it extremely helpful. I have a question concerning
the number of covariables to be used under RUVr. I've realized that
increasing the number of covariables makes the groups I want to see on the
PCA more visible and distinct from each other. It follows the the number of
DE genes also increases with k.
In one of my projects I have 72 samples and I run RUVr with up to k=50. The
number of DE genes on each of my comparisons increases exponentially up to a
plateau when k is high. Likewise, the common dispersion decreases with increasing k. It looks so good both in terms of PCA and DE genes that I wonder if using such high k values might have induced false interpretations or high number of false positives.
I came to ask myself such questions also because on the RUVSeq manual, the
given example is k=1 and I wondered why this is the case if increasing k
improves the results.
I would be grateful if you could provide me with any feedback on this.