text: user973: 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?
  
  user73: 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.
  
  user2388: Seurat has as part of its protocol a step where you filter based on UMI counts and percent mitochondrial
  http://satijalab.org/seurat/pbmc3k_tutorial.html
  
original_text: 973: <p>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.</p>
  
  <p>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?</p>
   73: <p>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.</p>
  
  <p>I'm not familiar with single cell analysis, but I am aware that DESeq2 can be <a href="https://t.co/KsUpczFn21" rel="nofollow noreferrer">adapted</a> to work with single cells, and incorporating additional variation is just a matter of adding an <a href="https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#multi-factor-designs" rel="nofollow noreferrer">additional covariate</a> into the DESeq2 formula model.</p>
   2388: <p>Seurat has as part of its protocol a step where you filter based on UMI counts and percent mitochondrial</p>
  
  <p><a href="http://satijalab.org/seurat/pbmc3k_tutorial.html" rel="nofollow noreferrer">http://satijalab.org/seurat/pbmc3k_tutorial.html</a></p>
  