Answers.Body: <p>Yes! Multiome seems to have cells with much higher mitochondrial gene content. I don't understand the biology behind this. But in using a cut off of 5% (i.e. remove cells with &gt;5% MT genes) I lose over 80% of cells in multiome data. Also, here is the Signac tutorial where they use a 20% cut off for multiome data - <a href="https://satijalab.org/seurat/articles/weighted_nearest_neighbor_analysis.html" rel="nofollow noreferrer">https://satijalab.org/seurat/articles/weighted_nearest_neighbor_analysis.html</a> (&quot;WNN analysis of 10x Multiome, RNA + ATAC&quot;)</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-04-18T15:52:55.760Z
Answers.LastActivityDate: 2023-04-18T15:52:55.760Z
Answers.UserDisplayName: Prasida Holla
Answers.Body: <p>As I mentioned in <a href="https://bioinformatics.stackexchange.com/a/17513/73">my</a> <a href="https://bioinformatics.stackexchange.com/a/16448/73">answers</a> to similar questions, there are a lot of cells with very high mitochondrial gene content. It would be a good idea to work out why that is before doing any more downstream processing.</p>
  <p>These cells will, of course, be filtered out by the thresholds you have set.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-11T01:24:48.413Z
Answers.LastActivityDate: 2021-08-11T01:24:48.413Z
Answers.UserDisplayName: gringer
Body: <p>I have <code>10X scRNA-seq multiome's 3' poly-A capture (scRNA-seq+ATAC-seq)</code> from PBMC</p>
  <p>Using Seuratreference mapping, I mapped my scRNA-seq part on reference PBMC (for cell type annotation)</p>
  <p>I have also tried Seurat zimuth shiny app</p>
  <p>But likely just a few of my cells has been mapped on the reference</p>
  <p>Please look at this</p>
  <p><a href="https://i.sstatic.net/UEGmA.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/UEGmA.jpg" alt="enter image description here" /></a></p>
  <p>Do you think this because my data is multiome or something else is going wrong?</p>
  <p>Please any body has some experience help me</p>
  <p>Thank you</p>
  
Tags: <scrnaseq><seurat><sequence-annotation><10x-genomics><cellranger>
Title: Problem with Seurat reference mapping
ThreadText: # Problem with Seurat reference mapping
  
  (Asked by: username_0 on 2021-08-10)
  
  I have `10X scRNA-seq multiome's 3' poly-A capture (scRNA-seq+ATAC-seq)` from PBMC
  
  Using Seuratreference mapping, I mapped my scRNA-seq part on reference PBMC (for cell type annotation)
  
  I have also tried Seurat zimuth shiny app
  
  But likely just a few of my cells has been mapped on the reference
  
  Please look at this
  
  [![enter image description here](https://i.sstatic.net/UEGmA.jpg)](https://i.sstatic.net/UEGmA.jpg)
  
  Do you think this because my data is multiome or something else is going wrong?
  
  Please any body has some experience help me
  
  Thank you
  
  **Tags:** scrnaseq, seurat, sequence-annotation, 10x-genomics, cellranger
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2023-04-18. Score: 1
  
  Yes! Multiome seems to have cells with much higher mitochondrial gene content. I don't understand the biology behind this. But in using a cut off of 5% (i.e. remove cells with >5% MT genes) I lose over 80% of cells in multiome data. Also, here is the Signac tutorial where they use a 20% cut off for multiome data - <https://satijalab.org/seurat/articles/weighted_nearest_neighbor_analysis.html> ("WNN analysis of 10x Multiome, RNA + ATAC")
  
  
  ---
  
  ### Answer by username_2 on 2021-08-11. Score: 0
  
  As I mentioned in [my](https://bioinformatics.stackexchange.com/a/17513/73) [answers](https://bioinformatics.stackexchange.com/a/16448/73) to similar questions, there are a lot of cells with very high mitochondrial gene content. It would be a good idea to work out why that is before doing any more downstream processing.
  
  These cells will, of course, be filtered out by the thresholds you have set.
  
  
  ---
  
  