Question: WGCNA-consensus network analysis using RNA-seq data
gravatar for pixie@bioinfo
19 months ago by
pixie@bioinfo1.4k wrote:

Hello, I have raw read counts from various stress conditions (4 types) from the same RNA-seq platform and tissue. I had previously determined the DEGs from the individual stresses separately and also extracted their normalized expression matrix for each of the stress conditions .

If I wish to perform a consensus module analysis using the WGCNA frame-work, do I need to perform 'vst' normalization in DeSeq2 across all the conditions ? Any leads to go about it will be very useful. Thanks

rna-seq • 1.3k views
ADD COMMENTlink modified 19 months ago by andrew.j.skelton735.9k • written 19 months ago by pixie@bioinfo1.4k
gravatar for andrew.j.skelton73
19 months ago by
andrew.j.skelton735.9k wrote:

For use in WGCNA, your data should be log2-like, so vst or rlog transformations are appropriate. Alternatively, you could use Limma Voom too for RNAseq data.

ADD COMMENTlink written 19 months ago by andrew.j.skelton735.9k

Thanks, my only concern was that by merging all the samples of all the conditions in one matrix, am I diluting the any information. But after going through their tutorial, they have possibly done that (i.e. normalized across male and female data)

ADD REPLYlink written 19 months ago by pixie@bioinfo1.4k

If these are from different experiments, then that's a whole different box of questions. Normalising cross experiment is far from trivial, and can only be done in some cases. The big caveat for WGCNA is that you need a decent number (>20) of samples to get interpretable output.

If these are cross experiment, then I'd recommend that you do WGCNA per condition agnostic of one another, then compare / contrast after you've generated modules.

If you're worried about covariates, or there's a strong effect that you want to account for, you could always take the residuals from a model fit (check out the removeBatchEffects function in Limma). Word of warning though, you're then going down the road of a lot of statistical caveats, make sure you truly understand the consequences of each step.

ADD REPLYlink written 19 months ago by andrew.j.skelton735.9k

Thanks so much for your time and detailed explanation. I will give a proper thought to this before I jump into network analysis.

ADD REPLYlink written 19 months ago by pixie@bioinfo1.4k
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