BRIDGE-GRN: Role-Aware Bi-Tower Graph Learning with Cross-View Contrast for Directed Gene Regulatory Network Inference

Abstract

Inferring directed gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data remains difficult because expression profiles are sparse, regulatory priors are incomplete, and experimentally supported TF-target labels are limited. To address these challenges, we propose BRIDGE-GRN, a role-aware graph learning framework that separates shared graph-context encoding from directional edge decoding. BRIDGE-GRN constructs an undirected support graph from training positive regulatory evidence, learns shared gene representations with an attention-based graph encoder, and projects them into transcription factor-role and target-role embedding spaces for asymmetric TF-to-target scoring. To improve robustness under noisy and incomplete supervision, the model aligns identity and edge-perturbed graph views through cross-view contrastive regularization. We evaluated BRIDGE-GRN across mouse benchmark settings spanning five cell types, three prior-network families, and two gene-scale settings, and further examined low-supervision transfer to target domains, architectural ablations, and biological interpretability. BRIDGE-GRN achieved consistently strong performance, outperforming or matching the strongest competing baseline in most benchmark configurations. Transfer initialization improved low-shot target-domain adaptation, while ablation analyses confirmed the importance of both role-specific bi-tower projections and contrastive regularization. Biological interpretation analyses further showed role-structured embeddings, enrichment of top-ranked predictions for external regulatory support, and coherent driver-centered regulatory modules. These results support BRIDGE-GRN as a robust, transferable, and interpretable framework for directed GRN inference from single-cell transcriptomic data.