Answers.Body: <p>Yes, you end up placing filters where things become inconsistent or similar to background. In the example, which appears to be from the Seurat tutorial, there's a downward plunge in <code>nGenes vs. nUMI</code> at around 200 genes and few believable cells with more than 2000-2500 genes, so the tutorials sets filters around there. The 0.05 mitochondrial contamination is a more arbitrary round number and is less supported by the plots (one would use more like 0.06 or 0.07 if one based this solely on the plots).</p>
  
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Answers.CreationDate: 2018-08-16T14:49:13.207Z
Answers.Text: Thank you@Devon Ryan If my geneplot result shows below/Users/huali/Desktop/Screen Shot 2018-08-15 at 7.35.30 PM.png.                 How should I setting my threshold ?
Answers.UserDisplayName: hua
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Answers.CreationDate: 2018-08-16T15:03:02.323Z
Answers.Text: What do you think would be good decent values given those plots? I don't want you to just blindly follow what I say :)
Answers.UserDisplayName: Devon Ryan
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Answers.CreationDate: 2018-08-16T15:13:06.593Z
Answers.Text: I just don't know how people set their thresholds, and want to figure out how they can do to do further analysis base on their QC. So I show the data that I got. Want to find something that can be dig out.
Answers.UserDisplayName: hua
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Answers.Text: In your case `percent.mito` should be less than 0.1 and `nGenes` should be at least 1000.
Answers.UserDisplayName: Devon Ryan
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Answers.CreationDate: 2019-08-07T19:00:42.533Z
Answers.Text: From the graphs, what would be a good upper limit cut-off for nFeatures per UMI?
Answers.UserDisplayName: Matt Thornton
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Answers.CreationDate: 2018-08-16T06:52:38.453Z
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Answers.UserDisplayName: Devon Ryan
Body: <p>I was wondering that the threshold that we set after doing QC in Seurat. there is an example</p>
  
  <pre><code># GenePlot is typically used to visualize gene-gene relationships, but can be used for anything 
  # calculated by the object, i.e. columns in object@meta.data, PC scores etc.
  # Since there is a rare subset of cells with an outlier level of high mitochondrial percentage
  # and also low UMI content, we filter these as well
  par(mfrow = c(1, 2))
  GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "percent.mito")
  GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "nGene")`
  </code></pre>
  
  <p><a href="https://i.sstatic.net/BCbyf.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/BCbyf.png" alt="QC ANALYSIS"></a></p>
  
  <p>Then its filter cell command is under:</p>
  
  <pre><code># We filter out cells that have unique gene counts over 2,500 or less than 200
  # Note that low.thresholds and high.thresholds are used to define a 'gate'
  # -Inf and Inf should be used if you don't want a lower or upper threshold.
  pbmc &lt;- FilterCells(object = pbmc, subset.names = c("nGene", "percent.mito"), low.thresholds = c(200, -Inf), high.thresholds = c(2500, 0.05))` 
  </code></pre>
  
  <p>So I want to know whether the threshold setting is depending on the picture I show?</p>
  
  <p>And the below is the Geneplot I have got base on my data. How can I set my thresholds?
  <a href="https://i.sstatic.net/IJvQv.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/IJvQv.png" alt="my old geneplot"></a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-04-21T20:46:10.553Z
Comments.Text: Why have an upper bound on ngenes? Doesn't it indicate a cell is healthy if it is is able to express many genes?
Comments.UserDisplayName: jjeang
Tags: <scrnaseq>
Title: how to filter out cell after doing QC
ThreadText: # how to filter out cell after doing QC
  
  (Asked by: username_0 on 2018-08-16)
  
  I was wondering that the threshold that we set after doing QC in Seurat. there is an example
  
  ```
  # GenePlot is typically used to visualize gene-gene relationships, but can be used for anything 
  # calculated by the object, i.e. columns in object@meta.data, PC scores etc.
  # Since there is a rare subset of cells with an outlier level of high mitochondrial percentage
  # and also low UMI content, we filter these as well
  par(mfrow = c(1, 2))
  GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "percent.mito")
  GenePlot(object = pbmc, gene1 = "nUMI", gene2 = "nGene")`
  
  ```
  
  [![QC ANALYSIS](https://i.sstatic.net/BCbyf.png)](https://i.sstatic.net/BCbyf.png)
  
  Then its filter cell command is under:
  
  ```
  # We filter out cells that have unique gene counts over 2,500 or less than 200
  # Note that low.thresholds and high.thresholds are used to define a 'gate'
  # -Inf and Inf should be used if you don't want a lower or upper threshold.
  pbmc <- FilterCells(object = pbmc, subset.names = c("nGene", "percent.mito"), low.thresholds = c(200, -Inf), high.thresholds = c(2500, 0.05))` 
  
  ```
  
  So I want to know whether the threshold setting is depending on the picture I show?
  
  And the below is the Geneplot I have got base on my data. How can I set my thresholds?
  [![my old geneplot](https://i.sstatic.net/IJvQv.png)](https://i.sstatic.net/IJvQv.png)
  
  **Tags:** scrnaseq
  
  > **Comments:**
  > **username_1**: Why have an upper bound on ngenes? Doesn't it indicate a cell is healthy if it is is able to express many genes?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2018-08-16. Score: 1 | Accepted answer
  
  Yes, you end up placing filters where things become inconsistent or similar to background. In the example, which appears to be from the Seurat tutorial, there's a downward plunge in `nGenes vs. nUMI` at around 200 genes and few believable cells with more than 2000-2500 genes, so the tutorials sets filters around there. The 0.05 mitochondrial contamination is a more arbitrary round number and is less supported by the plots (one would use more like 0.06 or 0.07 if one based this solely on the plots).
  
  > **Comments:**
  > **username_0**: Thank you@username_2 If my geneplot result shows below/Users/huali/Desktop/Screen Shot 2018-08-15 at 7.35.30 PM.png.                 How should I setting my threshold ?  
  > **username_2**: What do you think would be good decent values given those plots? I don't want you to just blindly follow what I say :)  
  > **username_0**: I just don't know how people set their thresholds, and want to figure out how they can do to do further analysis base on their QC. So I show the data that I got. Want to find something that can be dig out.  
  > **username_2**: In your case `percent.mito` should be less than 0.1 and `nGenes` should be at least 1000.  
  > **username_3**: From the graphs, what would be a good upper limit cut-off for nFeatures per UMI?  
  
  ---
  
  