Challenges in unsupervised clustering of single-cell RNA-seq data
Challenges in unsupervised clustering of single-cell RNA-seq data, Published online: 07 January 2019; doi:10.1038/s41576-018-0088-9
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.