ArticleGenome biology2023
ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Denoising single-cell RNA-seq data with a deep learning-embedded statistical framework.BMC bioinformatics · 2025Article
- scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization.Communications biology · 2025Article
- Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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4 authors.
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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.
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