I would like to adjust my whole-blood RNA-Seq count data matrix for cell type composition (obtained from hematological analysis & flow cytometry) before doing a coexpression network analysis with
So far, I did the following:
# I use DESeq2's vst to remove mean-variance relationship in the data dds <- DESeq2::DESeqDataSetFromMatrix(counts, colData, design = ~ group) dds <- DESeq2::vst(dds, blind = TRUE) vst <- assay(dds) # adjust for confounding variables vst_adjusted <- limma::removeBatchEffect( x = vst, covariates = c(cellA, cellB, cellC) # numeric vectors containing scaled cell proportion )
However, according to this link from other forum I can apparently insert the covariates into the design matrix when making the
DESeqDataSet and then set
blind = FALSE during the variance-stabilizing transformation.
There are also those who recommend using
sva by inserting my covariates to the
Which one is the best way for my goal?
Thank you for your kind response.
Best regards, Mikhael