Answers.Body: <p><em>Answer from @haci, converted from comment:</em></p>
  <p>I think you should not be using a cut-off higher than 20 or 25 for the reasons I explained above.</p>
  <p>I would advise against using cells with more than 20-25% percent.mito. This metric is used as a proxy for cell death and there is not much point keeping these cells if the cell death is so severe to the point where let's say half the reads map to the mitochondrial genome (with a 50% cut-off). There will be many caveats one being apoptotic cells from different lineages clustering together, not interesting unless you are studying apoptosis related phenomena. In my opinion 6000 good quality cells are better than 10000 apoptotic cells.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-07-28T00:30:47.550Z
Answers.LastActivityDate: 2021-07-28T00:30:47.550Z
Answers.UserDisplayName: gringer
Answers.Body: <p>From the graphs, you're losing most of the cells through the <code>percent.mitochondrial</code> threshold. The distribution is so broad on that mitochondrial plot that I probably wouldn't filter for mitochondrial expression (except possibly at ~95%). If I did have a lower threshold, I'd put it at around 50% rather than 20%, because that's where there's a change in density on the mitochondrial plot.</p>
  <p>This suggests that there's a very large mitochondria-rich population in the PBMC dataset. I don't have a good understanding of biology and cell populations, but if I were presented with these data I'd probably try clipping out those cells with 65-95% mtDNA and see if there is a strong enrichment for markers that correspond to any particular cell population (e.g. platelets).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-07-26T14:18:37.000Z
Answers.LastActivityDate: 2021-07-26T14:18:37.000Z
Answers.UserDisplayName: gringer
Body: <p>I have <em><strong>four PBMC samples from 10X scRNA-seq</strong></em></p>
  <pre><code>&gt; cancer
  An object of class Seurat 
  36601 features across 18338 samples within 1 assay 
  Active assay: RNA (36601 features, 0 variable features)
  &gt;
  </code></pre>
  <p>And this is Seurat QC plot</p>
  <p><a href="https://i.sstatic.net/Hqj48.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Hqj48.png" alt="enter image description here" /></a></p>
  <p>If I am not wrong, samples were of relatively low quality, with gene expression data revealing the presence of mitochondrial genes, as well as MALAT-1, which are suggestive of poor sample quality (dead/dying cells).</p>
  <p>Any way by this plot I filtered cells to remove data that had more than <code>20 &quot;percent&quot; mitochondrial expression</code> , <code>&gt; 2000 features</code> or <code>&lt; 100 features</code></p>
  <pre><code>cancer &lt;- subset(cancer, subset = nFeature_RNA &gt; 100 &amp; nFeature_RNA &lt; 2000 &amp; percent.mt &lt; 20)
  </code></pre>
  <p>By this I lost most of cells</p>
  <pre><code>&gt; cancer
      An object of class Seurat 
      36601 features across 6,883 samples within 1 assay 
      Active assay: RNA (36601 features, 0 variable features)
      &gt;
  </code></pre>
  <p>Please, can somebody experienced in scRNA-seq inspect this plot and my thresholds and tell me if I am wrong, any suggestion, if losing this number of cells is normal</p>
  <p>Thank you</p>
  
Tags: <scrnaseq><seurat><10x-genomics><qc>
Title: Help with Seurat QC ambiguity
ThreadText: # Help with Seurat QC ambiguity
  
  (Asked by: username_0 on 2021-07-26)
  
  I have ***four PBMC samples from 10X scRNA-seq***
  
  ```
  > cancer
  An object of class Seurat 
  36601 features across 18338 samples within 1 assay 
  Active assay: RNA (36601 features, 0 variable features)
  >
  
  ```
  
  And this is Seurat QC plot
  
  [![enter image description here](https://i.sstatic.net/Hqj48.png)](https://i.sstatic.net/Hqj48.png)
  
  If I am not wrong, samples were of relatively low quality, with gene expression data revealing the presence of mitochondrial genes, as well as MALAT-1, which are suggestive of poor sample quality (dead/dying cells).
  
  Any way by this plot I filtered cells to remove data that had more than `20 "percent" mitochondrial expression` , `> 2000 features` or `< 100 features`
  
  ```
  cancer <- subset(cancer, subset = nFeature_RNA > 100 & nFeature_RNA < 2000 & percent.mt < 20)
  
  ```
  
  By this I lost most of cells
  
  ```
  > cancer
      An object of class Seurat 
      36601 features across 6,883 samples within 1 assay 
      Active assay: RNA (36601 features, 0 variable features)
      >
  
  ```
  
  Please, can somebody experienced in scRNA-seq inspect this plot and my thresholds and tell me if I am wrong, any suggestion, if losing this number of cells is normal
  
  Thank you
  
  **Tags:** scrnaseq, seurat, 10x-genomics, qc
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2021-07-28. Score: 1 | Accepted answer
  
  *Answer from @haci, converted from comment:*
  
  I think you should not be using a cut-off higher than 20 or 25 for the reasons I explained above.
  
  I would advise against using cells with more than 20-25% percent.mito. This metric is used as a proxy for cell death and there is not much point keeping these cells if the cell death is so severe to the point where let's say half the reads map to the mitochondrial genome (with a 50% cut-off). There will be many caveats one being apoptotic cells from different lineages clustering together, not interesting unless you are studying apoptosis related phenomena. In my opinion 6000 good quality cells are better than 10000 apoptotic cells.
  
  
  ---
  
  ### Answer by username_1 on 2021-07-26. Score: 1
  
  From the graphs, you're losing most of the cells through the `percent.mitochondrial` threshold. The distribution is so broad on that mitochondrial plot that I probably wouldn't filter for mitochondrial expression (except possibly at ~95%). If I did have a lower threshold, I'd put it at around 50% rather than 20%, because that's where there's a change in density on the mitochondrial plot.
  
  This suggests that there's a very large mitochondria-rich population in the PBMC dataset. I don't have a good understanding of biology and cell populations, but if I were presented with these data I'd probably try clipping out those cells with 65-95% mtDNA and see if there is a strong enrichment for markers that correspond to any particular cell population (e.g. platelets).
  
  
  ---
  
  