text: user3171: 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")`
  
  
  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?
  
  
  user77: 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).
  
original_text: 3171: <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.stack.imgur.com/BCbyf.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/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.stack.imgur.com/IJvQv.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/IJvQv.png" alt="my old geneplot"></a></p>
   77: <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>
  