Answers.Body: <p>While better methods of evaluating your clusters would be to use an external dataset or a dataset with known truth, there are a variety of internal validation metrics that can be used to compare clustering solutions without another dataset.</p>
  
  <p>Here are a few metrics:</p>
  
  <ul>
  <li>Davies-Bouldin Index</li>
  <li>Calinski-Harabasz Index</li>
  <li>Root-Mean-Square Standard Deviation</li>
  </ul>
  
  <p>Many more can be found in this clustering review: <a href="http://stke.sciencemag.org/content/9/432/re6" rel="nofollow noreferrer">http://stke.sciencemag.org/content/9/432/re6</a></p>
  
  <p>These internal validation metrics grade your clustering solution based on three measures: compactness, connectedness, and separation. When using these metrics to compare clustering solutions, be sure to consider which metric is appropriate for your results as some algorithms work by optimizing certain measures.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-06-14T01:38:01.883Z
Answers.LastActivityDate: 2017-06-14T01:38:01.883Z
Answers.UserDisplayName: Alec
Answers.Body: <p>A SC3, single-cell consensus clustering, approach could be helpful here. It aims at achieving "high accuracy and robustness by combining multiple clustering solutions through a consensus approach" <a href="https://www.nature.com/nmeth/journal/v14/n5/full/nmeth.4236.html" rel="noreferrer">https://www.nature.com/nmeth/journal/v14/n5/full/nmeth.4236.html</a></p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-06-02T19:50:21.880Z
Answers.LastActivityDate: 2017-06-02T19:50:21.880Z
Answers.UserDisplayName: olga
Body: <p>In the analyses of single-cell RNA-seq data there are different unsupervised approaches to identify putative subpopulations (e.g. as available with Suerat or SCDE packages). </p>
  
  <p>Is there a good way of computationally validating the cluster solutions? Different methods may results in slightly different clustering results. How to know which one is the best i.e. representative of biological sub-populations?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-06-02T19:29:47.723Z
Comments.Text: There's a bit of folly in trying to computationally validate computational methods without a known truth dataset...
Comments.UserDisplayName: Devon Ryan
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-06-02T19:39:05.773Z
Comments.Text: I changed scRNA to single-cell RNA (since scRNA also means small conditional RNA).
Comments.UserDisplayName: Kamil S Jaron
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-06-03T12:33:55.120Z
Comments.Text: @Kamil “scRNA-seq” is an established name for single-cell RNA-seq.
Comments.UserDisplayName: Konrad Rudolph
Tags: <scrnaseq><clustering>
Title: validating identified sub-populations of cells in scRNA-seq
ThreadText: # validating identified sub-populations of cells in scRNA-seq
  
  (Asked by: username_0 on 2017-06-02)
  
  In the analyses of single-cell RNA-seq data there are different unsupervised approaches to identify putative subpopulations (e.g. as available with Suerat or SCDE packages).
  
  Is there a good way of computationally validating the cluster solutions? Different methods may results in slightly different clustering results. How to know which one is the best i.e. representative of biological sub-populations?
  
  **Tags:** scrnaseq, clustering
  
  > **Comments:**
  > **username_1**: There's a bit of folly in trying to computationally validate computational methods without a known truth dataset...  
  > **username_2**: I changed scRNA to single-cell RNA (since scRNA also means small conditional RNA).  
  > **username_3**: @username_2 “scRNA-seq” is an established name for single-cell RNA-seq.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_5 on 2017-06-02. Score: 6 | Accepted answer
  
  A SC3, single-cell consensus clustering, approach could be helpful here. It aims at achieving "high accuracy and robustness by combining multiple clustering solutions through a consensus approach" <https://www.nature.com/nmeth/journal/v14/n5/full/nmeth.4236.html>
  
  
  ---
  
  ### Answer by username_4 on 2017-06-14. Score: 3
  
  While better methods of evaluating your clusters would be to use an external dataset or a dataset with known truth, there are a variety of internal validation metrics that can be used to compare clustering solutions without another dataset.
  
  Here are a few metrics:
  
  * Davies-Bouldin Index
  * Calinski-Harabasz Index
  * Root-Mean-Square Standard Deviation
  
  Many more can be found in this clustering review: <http://stke.sciencemag.org/content/9/432/re6>
  
  These internal validation metrics grade your clustering solution based on three measures: compactness, connectedness, and separation. When using these metrics to compare clustering solutions, be sure to consider which metric is appropriate for your results as some algorithms work by optimizing certain measures.
  
  
  ---
  
  