I have a question regarding the best way to adjust for unwanted variation while using DESeq2.
Case 1: I have a dataset that came from one lab (so there are no known batch effects), and I wish to adjust for unwanted variation. I’m running svaseq on my count matrix, getting 17 SVs and adding them to my model.
dds=DESeqDataSetFromMatrix(countData = counts, colData = phenoData, design = ~ SV_1 + … + SV_17 + covariate_of_interest)
Is that an appropriate thing to do? Is ok to add this many SVs? Is there a better way to adjust for unwanted variation?
What if there are ~30 SVs, can you just add them into the model?
Case 2: I’m combining datasets generated by multiple labs, so now there are known batch effects. Should I include the known batch effects in my model in addition to the SVs estimated by svaseq?
The instructions for using svaseq state that the input should be a “transformed data matrix”. Does that mean I can run svaseq on a count matrix, or does it have to transformed in some way?
When you are fitting an interaction model and you also have SVs, can you set up your model like so:
dds=DESeqDataSetFromMatrix(countData = counts, colData = phenoData, design = ~ SV_1 + … + SV_17 + genotype + condition+ genotype:condition)