Fig. 1.

Typical computational strategies and methods for clustering cells using scRNA-seq data. With the processed scRNA-seq data, the SC3 approach, the Seurat clustering implementation based on the community detection method, and the NMF method are popular choices. scRNA-seq single-cell RNA sequencing, SC3 single-cell consensus clustering, NMF non-negative matrix factorization, PC principal component, SNN shared nearest neighbor, scVDMC variance-driven multitask clustering of scRNA-seq data, SIMLR single-cell interpretation via multikernel learning, UMAP uniform manifold approximation and projection, t-SNE t-distributed stochastic neighbor embedding