ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data

• Published in: Genome Biology Vol. 24; no. 1; pp. 1 - 28
• Main Authors: Li, Yang, Wu, Mingcong, Ma, Shuangge, Wu, Mengyun
• Format: Journal Article
• Language: English
• Published: London BioMed Central 11-09-2023; BMC
• Subjects: Clustering analysis; Feature selection; Gene expression; Gene selection; Method; Methods; Performance evaluation; ScRNA-seq data; Stem cells; Transcriptomics
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-023-03046-0

Abstract
Clustering is a critical component of single-cell RNA sequencing (scRNA-seq) data analysis and can help reveal cell types and infer cell lineages. Despite considerable successes, there are few methods tailored to investigating cluster-specific genes contributing to cell heterogeneity, which can promote biological understanding of cell heterogeneity. In this study, we propose a zero-inflated negative binomial mixture model (ZINBMM) that simultaneously achieves effective scRNA-seq data clustering and gene selection. ZINBMM conducts a systemic analysis on raw counts, accommodating both batch effects and dropout events. Simulations and the analysis of five scRNA-seq datasets demonstrate the practical applicability of ZINBMM.