Answers.Body: <p>I think it would be a good idea to at least look at the distribution of mitochondrial gene fraction in your cells. If they mostly have low fractions (e.g. less than 15%), then I wouldn't expect the clustering to be influenced too much by them.</p>
  <p>With SCTransform regressing on the percent.mt, I think it's okay to do post-transform filtering, but it'd be a good idea to exclude any cells that have high mitochondrial fraction prior to doing clustering (unless, for whatever reason, those are the cells you're interested in).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-11T01:34:52.363Z
Answers.LastActivityDate: 2021-08-11T01:34:52.363Z
Answers.UserDisplayName: gringer
Body: <p>I have been doing single cell analysis based on seurat and I realized after each clustering and reclustering when I run SCTransform, in my code there is vars.to.regress = percent.mt and I was running this everytime. I removed MT- contaminated cells before.</p>
  <p>My clustering seems fine though.</p>
  <p>Do you think this is very critical thing and I have to analyze everything from scratch?</p>
  
Tags: <seurat>
Title: Seurat - single cell analysis
ThreadText: # Seurat - single cell analysis
  
  (Asked by: username_0 on 2021-08-09)
  
  I have been doing single cell analysis based on seurat and I realized after each clustering and reclustering when I run SCTransform, in my code there is vars.to.regress = percent.mt and I was running this everytime. I removed MT- contaminated cells before.
  
  My clustering seems fine though.
  
  Do you think this is very critical thing and I have to analyze everything from scratch?
  
  **Tags:** seurat
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-08-11. Score: 1
  
  I think it would be a good idea to at least look at the distribution of mitochondrial gene fraction in your cells. If they mostly have low fractions (e.g. less than 15%), then I wouldn't expect the clustering to be influenced too much by them.
  
  With SCTransform regressing on the percent.mt, I think it's okay to do post-transform filtering, but it'd be a good idea to exclude any cells that have high mitochondrial fraction prior to doing clustering (unless, for whatever reason, those are the cells you're interested in).
  
  
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