Liu Wei, Sun Xingen, Yang Li, Li Kaiwen, Yang Yu, Fu Xiangzheng
Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China.
School of Computer Science, Xiangtan University, Xiangtan, 411105, China.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac156.
Accurate inference of gene regulatory networks (GRNs) is an essential premise for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but the identification of redundant regulation remains a challenge faced by researchers. Although combining global and local topology can identify and reduce redundant regulations, the topologies' specific forms and cooperation modes are unclear and real regulations may be sacrificed. Here, we propose a network structure control method [network-structure-controlling-based GRN inference method (NSCGRN)] that stipulates the global and local topology's specific forms and cooperation mode. The method is carried out in a cooperative mode of 'global topology dominates and local topology refines'. Global topology requires layering and sparseness of the network, and local topology requires consistency of the subgraph association pattern with the network motifs (fan-in, fan-out, cascade and feedforward loop). Specifically, an ordered gene list is obtained by network topology centrality sorting. A Bernaola-Galvan mutation detection algorithm applied to the list gives the hierarchy of GRNs to control the upstream and downstream regulations within the global scope. Finally, four network motifs are integrated into the hierarchy to optimize local complex regulations and form a cooperative mode where global and local topologies play the dominant and refined roles, respectively. NSCGRN is compared with state-of-the-art methods on three different datasets (six networks in total), and it achieves the highest F1 and Matthews correlation coefficient. Experimental results show its unique advantages in GRN inference.
准确推断基因调控网络（GRNs）是理解发病机制和治愈疾病的重要前提。已经开发了各种用于GRN推断的计算方法，但冗余调控的识别仍然是研究人员面临的挑战。尽管结合全局和局部拓扑可以识别并减少冗余调控，但拓扑的具体形式和合作模式尚不清楚，而且可能会牺牲真实的调控。在此，我们提出一种网络结构控制方法[基于网络结构控制的GRN推断方法（NSCGRN）]，该方法规定了全局和局部拓扑的具体形式及合作模式。该方法以“全局拓扑主导，局部拓扑细化”的合作模式进行。全局拓扑要求网络分层且稀疏，局部拓扑要求子图关联模式与网络基序（入扇、出扇、级联和前馈环）一致。具体而言，通过网络拓扑中心性排序获得一个有序基因列表。将Bernaola-Galvan突变检测算法应用于该列表，得到GRNs的层次结构，以在全局范围内控制上下游调控。最后，将四种网络基序整合到层次结构中，以优化局部复杂调控，并形成一种全局和局部拓扑分别发挥主导和细化作用的合作模式。在三个不同数据集（总共六个网络）上，将NSCGRN与现有方法进行了比较，它实现了最高的F1和马修斯相关系数。实验结果表明了其在GRN推断中的独特优势。
