{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Zhao F"],"pubmed_abstract":["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."],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2025.09.11.675686"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12458317"],"repository":["biostudies-literature"],"pubmed_title":["BayesCNet: Bayesian inference for cell type-specific regulatory networks leveraging cell type hierarchy in single-cell data."],"pmcid":["PMC12458317"],"pubmed_authors":["Jin W","Esser K","Sun R","Brusko T","Chen L","Zhao F","Peters L","Roy A","Lu Q"],"additional_accession":[]},"is_claimable":false,"name":"BayesCNet: Bayesian inference for cell type-specific regulatory networks leveraging cell type hierarchy in single-cell data.","description":"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.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-05-04T03:20:42.593Z","creation":"2026-05-04T03:14:24.609Z"},"accession":"S-EPMC12458317","cross_references":{"pubmed":["41000685"],"doi":["10.1101/2025.09.11.675686"]}}