Dear all,
I received RNAseq gene expression data that show batch effects for several technical confounders. I am performing a differential expression analysis using DESeq2, and have tried to add these effects as parameters to my design (~batch1+batch2+condition), but some of them are also linear combinations of the others, resulting in a model matrix that is not full rank.
Someone suggests the usage of tools such as Combat or SVA, but I am aware that transformed values are no longer integer count and I wonder whether the usage of these values will affects the DESeq2 outcomes.
What is the correct way to remove batch effects in this case?
Thank you very much!
1 answer
We have an example of using sva in the workflow: http://www.bioconductor.org/help/workflows/rnaseqGene/#batch
However, it sounds like you already know the batch. I don't understand how one would have multiple batch terms. What do these represent? What does your sample table look like (colData)?
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written
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Michael Love
It appears to me that GC content is a gene-level confounding variable, while date of sequencing and primer index are sample-level confounders. Therefore it is not clear to me how you would account for GC content at the sample-level (like the DESeq2 usage ~batch1+batch2+condition would indicate). However, DESeq2 allows you to control for gene-level confounders when estimating the size factors. Please see DESeq2 documentation for details on how to do that.
Sorry if I answered too quickly without thinking. The main problem is that I have two conditions (cases and controls) and (in about ~80% of the samples) in the same day either only cases or only controls have been sequenced (and primer index makes things worse). I know that this is a really poor design, but that's the way it is. This yields to an model matrix that is not full rank when I introduce the batches in the design.
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