用符号有向图卷积提升基因调控网络推断精度
Gene regulatory network inference algorithm based on spectral signed directed graph convolution
- 将基因调控网络建模为带符号有向图，引入磁性符号拉普拉斯卷积
- 在模拟与真实数据上均超越基线模型，AUROC更高
- 适合生物医学研究者用于癌症等疾病的基因机制分析
准确重构基因调控网络（GRNs）对理解基因功能和疾病机制至关重要。单细胞RNA测序（scRNA-seq）技术为计算推断GRNs提供了海量数据。由于GRNs理想上应建模为带符号的有向图以捕捉激活/抑制关系，最直观合理的方法是基于GRNs拓扑结构设计特征提取器，提取结构特征并与生物学特性结合研究。然而，传统谱图卷积难以处理此类表示。为此，我们提出MSGRNLink框架，显式将GRNs建模为带符号有向图，并采用磁性符号拉普拉斯卷积。在模拟与真实数据集上的实验表明，MSGRNLink在AUROC指标上全面优于所有基线模型。参数敏感性分析与消融实验验证了其鲁棒性及各模块的重要性。在膀胱癌案例研究中，MSGRNLink预测出更多已知边及其符号，进一步验证了其生物学相关性。
原文摘要 · Abstract (English)
Accurately reconstructing Gene Regulatory Networks (GRNs) is crucial for understanding gene functions and disease mechanisms. Single-cell RNA sequencing (scRNA-seq) technology provides vast data for computational GRN reconstruction. Since GRNs are ideally modeled as signed directed graphs to capture activation/inhibition relationships, the most intuitive and reasonable approach is to design feature extractors based on the topological structure of GRNs to extract structural features, then combine them with biological characteristics for research. However, traditional spectral graph convolution struggles with this representation. Thus, we propose MSGRNLink, a novel framework that explicitly models GRNs as signed directed graphs and employs magnetic signed Laplacian convolution. Experiments across simulated and real datasets demonstrate that MSGRNLink outperforms all baseline models in AUROC. Parameter sensitivity analysis and ablation studies confirmed its robustness and the importance of each module. In a bladder cancer case study, MSGRNLink predicted more known edges and edge signs than benchmark models, further validating its biological relevance.
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