RNA-Seq

Transcriptome

Bioinformatics

Biology

Gene

Gene expression

Artificial intelligence

Annotation

Computer science

Computational biology

Cluster analysis

Genetics

Nature Reviews Genetics

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. Single-cell RNA sequencing (scRNA-seq) enables transcriptome-based characterization of the constituent cell types within a heterogeneous sample. However, reliable analysis and biological interpretation typically require optimal use of clustering algorithms. This Review discusses the multiple algorithmic options for clustering scRNA-seq data, including various technical, biological and computational considerations.

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