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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?

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  • $\begingroup$ There's a bit of folly in trying to computationally validate computational methods without a known truth dataset... $\endgroup$
    – Devon Ryan
    Jun 2, 2017 at 19:29
  • $\begingroup$ I changed scRNA to single-cell RNA (since scRNA also means small conditional RNA). $\endgroup$
    – Kamil S Jaron
    Jun 2, 2017 at 19:39
  • $\begingroup$ @Kamil “scRNA-seq” is an established name for single-cell RNA-seq. $\endgroup$
    – Konrad Rudolph
    Jun 3, 2017 at 12:33

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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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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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