Question: What are the major differences in the computation of DE genes between deseq2 and limma?
1
gravatar for sbrown669
3.5 years ago by
sbrown66920
sbrown66920 wrote:

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?

ADD COMMENTlink modified 3.5 years ago by Kevin Blighe71k • written 3.5 years ago by sbrown66920
2
gravatar for Kevin Blighe
3.5 years ago by
Kevin Blighe71k
Republic of Ireland
Kevin Blighe71k wrote:

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.

ADD COMMENTlink modified 2.4 years ago • written 3.5 years ago by Kevin Blighe71k
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