Question

What are the major differences in the computation of DE genes between deseq2 and limma?

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6.7 years ago

sbrown669 ▴ 20

Other than Limma being designed for microarray analysis, I'm not sure I have grasped the major differences in how they compute differential expression.

Also, how does this manifest in the gene calls? Is one more stringent?

limma deseq2 differential expression RNA-Seq R • 2.3k views

ADD COMMENT • link updated 6.7 years ago by Kevin Blighe 87k • written 6.7 years ago by sbrown669 ▴ 20

score 2 · Accepted Answer · 2017-09-07

This is not a comprehensive answer but, in a nutshell, I could say the following about each:

Limma (as it was/is used for microarrays):

After background correction, quantile normalisation, and log base 2 transformation, limma fits a linear model to your data and then performs statistical comparisons via linear regression

DESeq for RNA-seq

DESeq2 performs a 'geometric normalisation' and then comparisons are generally conducted using the Wald test. DESeq2 also allows you to transform your normalised counts using a regularised log transformation, which deals with transcripts of low counts, or a variance-stabilised transformation, which deals with the high variability of low/high counts that you get from RNA-seq data.