Zeng Xin, Liu Shu, Liu Bowen, Zhang Weihang, Xu Wanzhe, Toriumi Fujio, Nakai Kenta
Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan.
Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan.
BMC Bioinformatics. 2025 May 24;26(1):134. doi: 10.1186/s12859-025-06128-x.
Understanding the dynamics of gene regulatory networks (GRNs) across various cellular states is crucial for deciphering the underlying mechanisms governing cell behavior and functionality. However, current comparative analytical methods, which often focus on simple topological information such as the degree of genes, are limited in their ability to fully capture the similarities and differences among the complex GRNs.
We present Gene2role, a gene embedding approach that leverages multi-hop topological information from genes within signed GRNs. Initially, we demonstrated the effectiveness of Gene2role in capturing the intricate topological nuances of genes using GRNs inferred from four distinct data sources. Then, applying Gene2role to integrated GRNs allowed us to identify genes with significant topological changes across cell types or states, offering a fresh perspective beyond traditional differential gene expression analyses. Additionally, we quantified the stability of gene modules between two cellular states by measuring the changes in the gene embeddings within these modules.
Our method augments the existing toolkit for probing the dynamic regulatory landscape, thereby opening new avenues for understanding gene behavior and interaction patterns across cellular transitions.
了解基因调控网络（GRN）在各种细胞状态下的动态变化对于解读控制细胞行为和功能的潜在机制至关重要。然而，当前的比较分析方法通常侧重于简单的拓扑信息，如基因的度，在充分捕捉复杂GRN之间的异同方面能力有限。
我们提出了Gene2role，一种利用有符号GRN中基因的多跳拓扑信息的基因嵌入方法。首先，我们通过使用从四个不同数据源推断出的GRN证明了Gene2role在捕捉基因复杂拓扑细微差别方面的有效性。然后，将Gene2role应用于整合的GRN使我们能够识别跨细胞类型或状态具有显著拓扑变化的基因，提供了超越传统差异基因表达分析的新视角。此外，我们通过测量这些模块内基因嵌入的变化来量化两个细胞状态之间基因模块的稳定性。
我们的方法扩充了用于探索动态调控格局的现有工具包，从而为理解细胞转变过程中的基因行为和相互作用模式开辟了新途径。
