biostudies-literatureUnknownZhao FUnderstanding gene regulatory networks (GRNs) is essential for deciphering biological processes and disease mechanisms. Single-cell multiome technologies now enable joint profiling of chromatin accessibility and gene expression, offering an powerful means to infer cell type-specific GRNs. However, existing methods analyze each cell type independently or aggregate data into pseudo-bulk profiles, limiting their ability to resolve rare populations and capture cellular heterogeneity. We introduce BayesCNet, a Bayesian hierarchical model that jointly infers enhancer-gene linkages across all cell types while leveraging their hierarchical relationships for information sharing. Through extensive simulations, BayesCNet consistently outperforms state-of-the-art methods, with the largest improvements in rare cell types. When applied to real datasets, BayesCNet identifies enhancer-gene linkages with higher accuracy validated by promoter-capture Hi-C data, and reconstructs cell type-specific GRNs that highlight key regulators, demonstrating its power to resolve gene regulatory programs across diverse cell types.bioRxiv : the preprint server for biology2025.09.11.675686https://www.ebi.ac.uk/biostudies/studies/S-EPMC12458317biostudies-literatureBayesCNet: Bayesian inference for cell type-specific regulatory networks leveraging cell type hierarchy in single-cell data.PMC12458317Jin WEsser KSun RBrusko TChen LZhao FPeters LRoy ALu QfalseBayesCNet: Bayesian inference for cell type-specific regulatory networks leveraging cell type hierarchy in single-cell data.Understanding gene regulatory networks (GRNs) is essential for deciphering biological processes and disease mechanisms. Single-cell multiome technologies now enable joint profiling of chromatin accessibility and gene expression, offering an powerful means to infer cell type-specific GRNs. However, existing methods analyze each cell type independently or aggregate data into pseudo-bulk profiles, limiting their ability to resolve rare populations and capture cellular heterogeneity. We introduce BayesCNet, a Bayesian hierarchical model that jointly infers enhancer-gene linkages across all cell types while leveraging their hierarchical relationships for information sharing. Through extensive simulations, BayesCNet consistently outperforms state-of-the-art methods, with the largest improvements in rare cell types. When applied to real datasets, BayesCNet identifies enhancer-gene linkages with higher accuracy validated by promoter-capture Hi-C data, and reconstructs cell type-specific GRNs that highlight key regulators, demonstrating its power to resolve gene regulatory programs across diverse cell types.2025-01-01T00:00:00Z2025 Sep2026-05-04T03:20:42.593Z2026-05-04T03:14:24.609ZS-EPMC124583174100068510.1101/2025.09.11.675686