<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>

CC BY-SA 3.0
2017-06-14T01:38:01.883Z
2017-06-14T01:38:01.883Z
Alec
<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>

CC BY-SA 3.0
2017-06-02T19:50:21.880Z
2017-06-02T19:50:21.880Z
olga
<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>

CC BY-SA 3.0
2017-06-02T19:29:47.723Z
There's a bit of folly in trying to computationally validate computational methods without a known truth dataset...
Devon Ryan
CC BY-SA 3.0
2017-06-02T19:39:05.773Z
I changed scRNA to single-cell RNA (since scRNA also means small conditional RNA).
Kamil S Jaron
CC BY-SA 3.0
2017-06-03T12:33:55.120Z
@Kamil “scRNA-seq” is an established name for single-cell RNA-seq.
Konrad Rudolph
<scrnaseq><clustering>
validating identified sub-populations of cells in scRNA-seq
# 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**: @Kamil “scRNA-seq” is an established name for single-cell RNA-seq.  

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## 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>


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### 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.


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