text: user12477: I am learning the Seurat algorithms to cluster the scRNA-seq datasets. I found this explanation, but am confused. Can someone explain it to me, "The FindClusters function implements the procedure, and contains a resolution parameter that sets the ‘granularity’ of the downstream clustering, with increased values leading to a greater number of clusters. We find that setting this parameter between 0.6-1.2 typically returns good results for single cell datasets of around 3K cells". My questions are:
  
  What is "granularity" of the downstream analysis?
  How does the parameter "resolution" work?
  How to know the optimum value for parameter "resolution"?
  
  I found this paper about the resolution that might help, but not clear to me yet. https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/pdf.
  I think the higher the resolution, the more clusters produced meaning the smaller clusters obtained? The smaller the clusters the more specific cell types we get, is that correct?
  
  user104: 
  What is "granularity" of the downstream analysis?
  
  Low granularity implies basic cell types (e.g. T cells, B cells), implies few larger clusters.
  High granularity implies more refined cell types (e.g. CD4+ Central Memory T, Treg, Memory B cell etc.), implies more smaller clusters
  
  How does the parameter "resolution" work?
  
  A high resolution parameter value produces more smaller clusters, i.e. higher granularity. If you want to know how modularity clustering works, I recommend the Leiden algorithm paper https://www.nature.com/articles/s41598-019-41695-z
  
  How to know the optimum value for parameter "resolution"?
  
  Tricky, and it depends on what questions you want to answer. Normally it's a subjective iterative process of trying a clustering resolution, checking if the clusters make sense, re-trying etc. but I also quite like the clustree approach https://cran.r-project.org/web/packages/clustree/index.html
  
  The smaller the clusters the more specific cell types we get, is that correct?
  
  Yes correct
  
original_text: 12477: <p>I am learning the Seurat algorithms to cluster the scRNA-seq datasets. I found this explanation, but am confused. Can someone explain it to me, &quot;The FindClusters function implements the procedure, and contains a resolution parameter that sets the ‘granularity’ of the downstream clustering, with increased values leading to a greater number of clusters. We find that setting this parameter between 0.6-1.2 typically returns good results for single cell datasets of around 3K cells&quot;. My questions are:</p>
  <ol>
  <li>What is &quot;granularity&quot; of the downstream analysis?</li>
  <li>How does the parameter &quot;resolution&quot; work?</li>
  <li>How to know the optimum value for parameter &quot;resolution&quot;?</li>
  </ol>
  <p>I found this paper about the resolution that might help, but not clear to me yet. <a href="https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/pdf" rel="nofollow noreferrer">https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/pdf</a>.</p>
  <p>I think the higher the resolution, the more clusters produced meaning the smaller clusters obtained? The smaller the clusters the more specific cell types we get, is that correct?</p>
   104: <blockquote>
  <p>What is &quot;granularity&quot; of the downstream analysis?</p>
  </blockquote>
  <p>Low granularity implies basic cell types (e.g. T cells, B cells), implies few larger clusters.</p>
  <p>High granularity implies more refined cell types (e.g. CD4+ Central Memory T, Treg, Memory B cell etc.), implies more smaller clusters</p>
  <blockquote>
  <p>How does the parameter &quot;resolution&quot; work?</p>
  </blockquote>
  <p>A high resolution parameter value produces more smaller clusters, i.e. higher granularity. If you want to know how modularity clustering works, I recommend the Leiden algorithm paper <a href="https://www.nature.com/articles/s41598-019-41695-z" rel="nofollow noreferrer">https://www.nature.com/articles/s41598-019-41695-z</a></p>
  <blockquote>
  <p>How to know the optimum value for parameter &quot;resolution&quot;?</p>
  </blockquote>
  <p>Tricky, and it depends on what questions you want to answer. Normally it's a subjective iterative process of trying a clustering resolution, checking if the clusters make sense, re-trying etc. but I also quite like the clustree approach <a href="https://cran.r-project.org/web/packages/clustree/index.html" rel="nofollow noreferrer">https://cran.r-project.org/web/packages/clustree/index.html</a></p>
  <blockquote>
  <p>The smaller the clusters the more specific cell types we get, is that correct?</p>
  </blockquote>
  <p>Yes correct</p>
  