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)