text: user2005: I am new to data science. I have a dataset of single-cell gene expression from multiple cell types in C. Elegans. The dataset is from the paper Comprehensive single-cell transcriptional profiling of a multicellular organism
  My main question is, Which approaches should I use for filtering out bad cells in this case when we have multiple cell types in the dataset?
  So far I tried to filter out genes that have too high mitochondrial genes content following the Bioconductor “simpleSingleCell” workflow.
  However, the tutorial specifically says that the method of filtering out based on mitochondrial genes most probably will not work when the dataset has multiple cell types:
  
  Analyzing all cell types together would unnecessarily inflate the MAD and 
        compromise the removal of low-quality cells, at best; or lead to the 
        entire loss of one cell type, at worst.
  
  Any suggestions would be greatly appreciated. 
  
  user678: From what I known, there is no clear consensus in the field and it depends on the type of cells you are interrogating. 
  However, if mitoRNA/endogenousRNA ratio does not fit for your purposes, other option is to check at the total number of genes/transcripts detected in each cell. In this way, you can filter out cells that have considerable less genes/transcripts detected than the rest since this may be indicative of bad quality cells for any reason (apoptosis, RNA degradation, sequencing itself, etc). For instance, to put a threshold of >1000 genes/transcripts in a cell to be considered for further analysis.
  Consider also to take a look at the number of mapped reads for each cell, as cells with low mapped reads respect to the rest could be potentially problematic.
  
original_text: 2005: <p>I am new to data science. I have a dataset of single-cell gene expression from multiple cell types in <em>C. Elegans</em>. The dataset is from the paper <a href="https://www.ncbi.nlm.nih.gov/pubmed/28818938" rel="nofollow noreferrer">Comprehensive single-cell transcriptional profiling of a multicellular organism</a></p>
  
  <p>My main question is, <strong>Which approaches should I use for filtering out bad cells in this case when we have multiple cell types in the dataset?</strong></p>
  
  <p>So far I tried to filter out genes that have too high mitochondrial genes content following the <a href="https://www.bioconductor.org/help/workflows/simpleSingleCell/" rel="nofollow noreferrer">Bioconductor “simpleSingleCell” workflow</a>.</p>
  
  <p>However, the tutorial specifically says that the method of filtering out based on mitochondrial genes most probably will not work when the dataset has multiple cell types:</p>
  
  <blockquote>
    <p>Analyzing all cell types together would unnecessarily inflate the MAD and 
        compromise the removal of low-quality cells, at best; or lead to the 
        entire loss of one cell type, at worst.</p>
  </blockquote>
  
  <p>Any suggestions would be greatly appreciated. </p>
   678: <p>From what I known, there is no clear consensus in the field and it depends on the type of cells you are interrogating. </p>
  
  <p>However, if mitoRNA/endogenousRNA ratio does not fit for your purposes, other option is to <strong>check at the total number of genes/transcripts detected</strong> in each cell. In this way, you can filter out cells that have considerable less genes/transcripts detected than the rest since this may be indicative of bad quality cells for any reason (apoptosis, RNA degradation, sequencing itself, etc). For instance, to <strong>put a threshold</strong> of >1000 genes/transcripts in a cell to be considered for further analysis.</p>
  
  <p>Consider also to take a look at the number of mapped reads for each cell, as cells with low mapped reads respect to the rest could be potentially problematic.</p>
  