Getting TMM from DESeq2

Question

[zizigolu]

I have raw read count of two groups of patients I want to normalize this matrix in a way getting TMM or geometric mean values by DESeq2 how can I do that?

Answer 1

[Kevin Blighe]

Hello, this should return what you need:

counts(dds, normalised = TRUE)

Please see: http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#do-normalized-counts-correct-for-variables-in-the-design

Kevin

[rpolicastro]

I just wanted to point out for the original poster's knowledge that only edgeR uses TMM normalization.

[Kevin Blighe]

Indeed: A: How do I explain the difference between edgeR, LIMMA, DESeq etc. to experimental

[zizigolu]

You mean DESeq2 gives geometric mean by Kevin's formula?

[Kevin Blighe]

Perhaps take a look at the output of ?DESeq2::counts

normalized: logical indicating whether or not to divide the counts by the size factors or normalization factors before returning (normalization factors always preempt size factors)

[zizigolu]

Thank you so much

In GSEA manual says

Normalizing RNA-seq quantification to support comparisons of a feature's expression levels across samples is important for GSEA. Normalization methods (such as, TMM, geometric mean) which operate on raw counts data should be applied prior to running GSEA. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA

So I have two groups (n=9 versus n=24)

I put my raw counts matrix in this formula

dge <- DGEList(M)
dge <- calcNormFactors(dge)
logCPM <- cpm(dge, log=TRUE)

Does logCPM gives proper input for GSEA?

I asked this in Bioconductor and Gordon Smith believes this is fine

https://support.bioconductor.org/p/133174/ Honestly I am not certain

[Kevin Blighe]

Indeed, yes, logCPM is okay for GSEA. There is no problem here.

[Ram]

Should it be normalized or does normalised also work?

[Kevin Blighe]

Good point. Probably just normalized