Regressing out unwanted sources of variation in single cell RNA-seq data I have a dataset of single cell count data and I want to regress out the variation caused by the number of UMI's and the percentage of mitochondrial genes.

I know that count data is discrete data, and commonly follows a negative binomial distribution. To regress out these two confounders, should I use a linear model or GLM (poisson/negative binomial)? And how can I determine the optimal choice?
Cleaning data before doing analysis can be more important than the method of data analysis. For UMIs, there's an obvious cleaning step that can be done: it would be better to filter out duplicates (prior to generating counts) than to try to incorporate that information in the count analysis.

I'm not familiar with single cell analysis, but I am aware that DESeq2 can be adapted to work with single cells, and incorporating additional variation is just a matter of adding an additional covariate into the DESeq2 formula model.