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It is generally expected in communities like this that you read the manual first which covers this https://bioconductor.org/packages/release/bioc/vignettes/RUVSeq/inst/doc/RUVSeq.html#empirical-control-genes and then tell what beyond this poses a challenge.
I've tried to intersect the DEGs from edgeR with the compared groups (genes that didn’t meet the criteria to be included as DEGs, such as p < 0.05 and high FDR). However, I didn’t get satisfactory results, probably because I lost some genes in that comparison.
Next, I normalized the matrix and focused on genes with low variance (below 0.2). This approach gave me a list of about 1500 genes, but I need to verify this by checking the PCA plot after removing batch effects.
I’m asking if anyone with more experience could share advice, as I don’t have a strict method to validate results I get.