validating identified sub-populations of cells in scRNA-seq 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?
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