Question: Using SVA to correct for covariates in RNA-seq data
gravatar for sbrown669
22 months ago by
sbrown66920 wrote:

I am working with RNA-seq data, and I need to correct for covariates. To do this, I want to use the Bioconductor package SVA. I want to produce a 'corrected dataset' and then use this for further analyses - to perform pairwise Pearson correlations and generate heat maps to visualise co-expression. However, as I understand it, the documentation explains how to feed the covariates into a model for differential expression analysis, not how to produce and export a corrected dataset based on the SVA calculations.

I'd appreciate any advice on doing this, or in fact being informed that I'm going about this in the wrong way.

ADD COMMENTlink modified 22 months ago • written 22 months ago by sbrown66920
gravatar for Kevin Blighe
22 months ago by
Kevin Blighe69k
Republic of Ireland
Kevin Blighe69k wrote:


Yes, from what I understand, SVA will just determine the batch covariates that [may] exist in your data. You can then use removeBatchEffects if you want to directly remove these batch effects from your data.

Be cautious of these batch detection methods, though - one does not want to remove genuine biological effects of interest that exist in the samples.


Edit May 8, 2020:

ADD COMMENTlink modified 8 months ago • written 22 months ago by Kevin Blighe69k
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