Learning Crossmodal Interaction Patterns via Attributed Bipartite Graphs for Single-Cell Omics
Xiaotang Wang, Xuanwei Lin, Yun Zhu, Hao Li, Yongqi Zhang
Crossmodal matching in single-cell omics is essential for explaining biological regulatory mechanisms and enhancing downstream analyses. However, current single-cell crossmodal models often suffer from three limitations: sparse modality signals, underutilization of biological attributes, and insufficient modeling of regulatory interactions. In this work, we present a novel framework which reformulates crossmodal matching as a graph classification task on Attributed Bipartite Graphs (ABGs).  It models single-cell ATAC-RNA data as an ABG, where each expressed ATAC and RNA is treated as a distinct node with unique IDs and biological features. To model crossmodal interaction patterns on the constructed ABG, we propose $\text{Bi}^2\text{Former}$, a biologically-driven bipartite graph transformer that learns interpretable attention over ATAC–RNA pairs. This design enables the model to effectively learn and explain biological regulatory relationships between ATAC and RNA modalities.Extensive experiments demonstrate that $\text{Bi}^2\text{Former}$ achieves state-of-the-art performance in crossmodal matching across diverse datasets, generalizes well to unseen cell types, and reveals biologically meaningful regulatory patterns. This work pioneers an ABG-based approach for single-cell crossmodal matching, offering a powerful framework for uncovering regulatory interactions at the single-cell omics. Our code is available at: https://anonymous.4open.science/r/Bi2Former.