Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-06-30T18:54:47.543Z
Answers.LastActivityDate: 2021-07-02T12:59:24.723Z
Answers.LastEditDate: 2021-07-02T12:59:24.723Z
Answers.UserDisplayName: Chris_Rands
Body: <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>
  
Tags: <scrnaseq><seurat><single-cell><clustering>
Title: Seurat clustering Methods-resolution parameter explanation
ThreadText: # Seurat clustering Methods-resolution parameter explanation
  
  (Asked by: username_0 on 2021-06-30)
  
  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:
  
  1. What is "granularity" of the downstream analysis?
  2. How does the parameter "resolution" work?
  3. 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?
  
  **Tags:** scrnaseq, seurat, single-cell, clustering
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-06-30. Score: 2
  
  > 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
  
  
  ---
  
  