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This research focuses on developing a Bayesian framework for the identification of rare cell types in single-cell RNA sequencing data using multiple subjects.

February 6, 2026Briefings in Bioinformatics0 citationsOpen Access

BayesRare: Bayesian mixture model for population-level rare cell type detection in multi-subject single-cell RNA sequencing data

YYYinqiao YanBeijing University of Technology ZWZhigang WuNingbo University

Key Points

This research focuses on developing a Bayesian framework for the identification of rare cell types in single-cell RNA sequencing data using multiple subjects.
Developed a hierarchical Bayesian framework for rare cell detection.
Augmented a Bayesian mixture model with a rare cluster indicator.
Integrated evidence across subjects for improved analysis.
Quantified uncertainty through posterior probabilities.
Achieved superior precision in rare cell detection.
Reduced false positive rates significantly.
Identified biologically meaningful disease-specific rare subtypes.

Abstract

Abstract Rare cell types in single-cell RNA sequencing (scRNA-seq) data often encode essential biological signals, such as early disease markers or key immune regulators. With advancing technologies, large-scale scRNA-seq cohorts from multiple subjects now enable population-level analyses of the prevalence, heterogeneity, and disease associations of rare cell populations. However, existing methods for rare cell detection are typically limited to single datasets and cannot effectively leverage cross-subject information. To tackle this challenge, we present BayesRare, a hierarchical Bayesian framework for population-level rare cell discovery in multi-subject scRNA-seq data. The method augments a Bayesian mixture model with a rare cluster indicator, supporting joint cell-type clustering and rare-population identification. By explicitly characterizing the statistical properties of rare cell types, BayesRare integrates evidence across subjects, quantifies uncertainty via posterior probabilities, and enables inference of group-level differences (e.g. patients versus controls). Across synthetic and three real datasets, BayesRare achieves superior precision, reduces false positives, and uncovers biologically meaningful disease-specific rare subtypes. The R package of BayesRare is available at https://github.com/yinqiaoyan/BayesRare.

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