Four datasets used in this work.

Understanding gene regulation during cell differentiation requires effective integration of multi-omics single-cell data. In this study, we propose BranchKGN, a heterogeneous graph transformer-based framework for identifying branch-specific key genes along cell differentiation trajectories. By integrating scRNA-seq and scATAC-seq data into a unified gene representation, we infer differentiation trajectories using Slingshot and construct a heterogeneous graph capturing gene–cell relationships. Through attention-based graph learning, BranchKGN assigns gene importance scores within each cell, enabling the identification of genes consistently informative across branch point cells and their descendant lineages. These genes are then used to reconstruct gene regulatory networks and differentiation trajectories. Validation on three independent datasets demonstrates that the identified gene sets not only capture key regulators of cell fate bifurcation but also support accurate reconstruction of differentiation trajectories. Our results highlight the effectiveness of BranchKGN in dissecting gene regulation dynamics during cellular transitions and provide a valuable tool for multi-omics single-cell analysis.

解析细胞分化过程中的基因调控机制，需有效整合多组学单细胞数据。本研究提出BranchKGN，一款基于异构图Transformer（Heterogeneous Graph Transformer）的分析框架，用于识别细胞分化轨迹上的分支特异性关键基因。研究将单细胞RNA测序（scRNA-seq）与单细胞转座酶可及性测序（scATAC-seq）数据整合为统一的基因表征，通过Slingshot工具推断分化轨迹，并构建捕捉基因-细胞关联的异构图。借助基于注意力机制的图学习，BranchKGN可为每个细胞分配基因重要性评分，从而识别在分支点细胞及其后代谱系中始终具有信息价值的基因。随后可利用这些基因重构基因调控网络与分化轨迹。在三个独立数据集上开展的验证结果表明，所识别的基因集不仅能够捕捉细胞命运分叉的关键调控因子，还可支持精准的分化轨迹重构。本研究结果证实了BranchKGN在解析细胞转变过程中基因调控动态变化方面的有效性，同时为多组学单细胞分析提供了一款极具价值的工具。