Batch adjustment for cohort based RNA seq data
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@8cebf978
Last seen 11 days ago
India

I have cohort RNA sequencing data and clinical traits. Is the given code below fine to normalize and correct for Batch effect. For further analysis can we subject the processed matrix to WGCNA directly?

data <- cpm(data, log=TRUE)

design.Covariates <- model.matrix(~Batch)

logCPM.adjusted <- removeBatchEffect(data, group=NULL, covariates=design.Covariates)

RNASeqPower removeBatchEffect cpm Batcheffect • 180 views
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@james-w-macdonald-5106
Last seen 1 day ago
United States

You are asking for analysis advice on a support site meant for technical advice for using Bioconductor packages (WGCNA is not part of Bioconductor). Ideally you would ask this sort of question on biostars.org instead.

What you have done will remove any batch effects that can be captured as the mean of a batch. You could also consider using RUVseq or ComBat in sva, which can adjust for differences in variability between batches as well. You might also consider using cqn, since WGCNA is making inter-gene comparisons. It's your analysis though, so you have to decide what you think is best.

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Apologies for asking the analysis question here and thank you for the response.

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