Challenges in unsupervised clustering of single-cell RNA-seq data.

TLDR: This Review discusses the multiple algorithmic options for clustering scRNA-seq data, including various technical, biological and computational considerations.

Abstract: Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.