Consensus clustering of single-cell RNA-seq data by enhancing network affinity

• Published in: Briefings in bioinformatics Vol. 22; no. 6
• Main Authors: Cui, Yaxuan, Zhang, Shaoqiang, Liang, Ying, Wang, Xiangyun, Ferraro, Thomas N, Chen, Yong
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 05-11-2021
• Subjects: Algorithms; Cluster Analysis; Computational Biology - methods; Databases, Genetic; Problem Solving Protocol; Reproducibility of Results; RNA-Seq; Single-Cell Analysis - methods; Web Browser; single-cell RNA-seq; cell typing; clustering algorithm; bioinformatics
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbab236

Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised clustering methods have been proposed for de novo identification of cell populations, their performance and robustness suffer from the high variability, low capture efficiency and high dropout rates which are characteristic of scRNA-seq experiments. Here, we present a novel unsupervised method for Single-cell Clustering by Enhancing Network Affinity (SCENA), which mainly employed three strategies: selecting multiple gene sets, enhancing local affinity among cells and clustering of consensus matrices. Large-scale validations on 13 real scRNA-seq datasets show that SCENA has high accuracy in detecting cell populations and is robust against dropout noise. When we applied SCENA to large-scale scRNA-seq data of mouse brain cells, known cell types were successfully detected, and novel cell types of interneurons were identified with differential expression of gamma-aminobutyric acid receptor subunits and transporters. SCENA is equipped with CPU + GPU (Central Processing Units + Graphics Processing Units) heterogeneous parallel computing to achieve high running speed. The high performance and running speed of SCENA combine into a new and efficient platform for biological discoveries in clustering analysis of large and diverse scRNA-seq datasets.