A systematic performance evaluation of clustering methods for single-cell RNA-seq data

Subpopulation identification, usually via some form of unsupervised clustering, is a fundamental step in the analysis of many single-cell RNA-seq data sets. This has motivated the development and application of a broad range of clustering methods, based on various underlying algorithms. Here, researchers from the University of Zurich provide a systematic and extensible performance evaluation of 14 clustering algorithms implemented in R, including both methods developed explicitly for scRNA-seq data and more general-purpose methods. The methods were evaluated using nine publicly available scRNA-seq data sets as well as three simulations with varying degree of cluster separability. The same feature selection approaches were used for all methods, allowing them to focus on the investigation of the performance of the clustering algorithms themselves. The researchers evaluated the ability of recovering known subpopulations, the stability and the run time and scalability of the methods. Additionally, they investigated whether the performance could be improved by generating consensus partitions from multiple individual clustering methods. They found substantial differences in the performance, run time and stability between the methods, with SC3 and Seurat showing the most favorable results. Additionally, the researchers found that consensus clustering typically did not improve the performance compared to the best of the combined methods, but that several of the top-performing methods already perform some type of consensus clustering.

Clustering of the methods based on the average similarity of their partitions across data sets, for the true number of clusters.

Numbers on internal nodes indicate the fraction of dendrograms from individual data sets where a particular subcluster was found.

Availability – All the code used for the evaluation is available on GitHub ( https://github.com/markrobinsonuzh/scRNAseq_clustering_comparison). In addition,an R package providing access to data and clustering results, thereby facilitating inclusion of new methods and data sets, is available from Bioconductor ( https://bioconductor.org/packages/DuoClustering2018).