Deciphering hierarchical regulatory network of cell fate via an epigenetics-informed heterogeneous graph transformer on single-cell multi-omics data.

ABSTRACT: The precise control of cell fate is driven by a hierarchical regulatory network (HRNet) where transcription factors (TFs) and cis-regulatory elements (CREs) orchestrate the expression of target genes (TGs) through complex causal actions. While single-cell multi-omics technologies provide multi-dimensional data to resolve regulatory networks, existing methods often fail to capture their hierarchical and causal properties. We propose SMOGT (Single-cell Multi-Omics Graph Transformer), a graph representation learning method to decipher HRNet. SMOGT embeds epigenetic mechanism into Heterogeneous Graph Transformer (HGT) by structuring information flow along a hierarchical-guided meta-path (TF-TF → TF-CRE → CRE-CRE → CRE-TG), and employs a semi-supervised strategy to ensure network accuracy. Vali

SUBMITTER: Huang Y 

PROVIDER: S-EPMC12875533 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature