Linear regression vs DESeq2 models for DEG analysis

Question

[fiorino.gabriella]

I am very new to this field and looking to get some feedback. I have bulk short read RNA sequencing results from cell culture samples WT and mutant. N=5 per genotype. I want to calculate DEGs and wondering which is the preferred method in this scenario: LM (linear regression model) or DESeq2?

I ran code for LM following cqn normalization and RPKM filtering and controlled for batch effect and RIN, and received 0 DEGs. In contrast, I also ran DESeq2 on raw counts and controlled for batch and RIN, and obtained hundreds of DEGs. Why are the results between the two methods so different and how do you decide on which method to use?

From my reading, I believe that DESeq2 would be the best based on my sample size. Any help or guidance greatly appreciated! Thank you in advance!

Answer 1

[swbarnes2]

DE genes in bulk RNASeq is what DESeq2 was made for, so why wouldn't you use it? RPKM is not an appropriate normalization method here.

Most people do not include RIN as a variable. You really have a batch in sample prep with only 10 samples?