Hello all,

I have a large RNA-seq dataset that I am trying to run WGCNA on. The data set has many variables including (including 3 brain regions, 3 ages, sex, 2 genotypes.) I have been advised to use Deseq2 normalization for these samples prior to doing WGCNA.

I am struggling with two aspects. 1) I assume I am not actually doing Deseq2() differential expression on this data, rather just using the normalization method of varianceStabilizingTransformation on the raw counts

2) I am struggling with the "design" aspect of these functions as I think that all three aspects (region, age, sex and genotype) should all play a role in the design. Does the design even matter if I am not using this for differential expression? Is even used for normalization or just for the differential expression?

countData= read.csv("Raw.Counts.csv, sep = ",", rownames=Genes)
colData=read.csv("Sample.info.numerical.csv", sep = ",", rownames=Sample_ID)
dds<- DESeqDataSetFromMatrix(countData, colData, design=???)
dds <- estimateSizeFactors(dds)
vsd <- varianceStabilizingTransformation(dds, blind=T)
normalized_counts <- counts(dds, normalized=TRUE)

There have been a number of posts on DESeq2 + WGCNA, you can google the support site by adding "site:support.bioconductor.org" to normal google search.

I think its more common to call the EstimateSizeFactors step 'normalization', so that might be what you want. Which is what you are doing, since the vsd object you make with varianceStabilisingTranforamtion isn't being used at all after being created. EstimateSizeFactor absolutely does not use the design at all; you can run it with a design of ~1 if you want.