scMGATGRN: a multiview graph attention network-based method for inferring gene regulatory networks from single-cell transcriptomic data.
Lin Yuan, Ling Zhao, Yufeng Jiang, Zhen Shen, Qinhu Zhang, Ming Zhang, Chun-Hou Zheng, De-Shuang Huang
Author Information
Lin Yuan: Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, 250353, Shandong, China. ORCID
Ling Zhao: Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, 250353, Shandong, China.
Yufeng Jiang: Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, 250353, Shandong, China.
Zhen Shen: School of Computer and Software, Nanyang Institute of Technology, 80 Changjiang Road, 473004, Henan, China. ORCID
Qinhu Zhang: Ningbo Institute of Digital Twin, Eastern Institute of Technology, 568 Tongxin Road, 315201, Zhejiang, China.
Ming Zhang: Department of Pediatrics, Zhongshan Hospital Xiamen University, 201 Hubinnan Road, 361004, Fujian, China.
Chun-Hou Zheng: Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, 230601, Anhui, China.
De-Shuang Huang: Ningbo Institute of Digital Twin, Eastern Institute of Technology, 568 Tongxin Road, 315201, Zhejiang, China.
The gene regulatory network (GRN) plays a vital role in understanding the structure and dynamics of cellular systems, revealing complex regulatory relationships, and exploring disease mechanisms. Recently, deep learning (DL)-based methods have been proposed to infer GRNs from single-cell transcriptomic data and achieved impressive performance. However, these methods do not fully utilize graph topological information and high-order neighbor information from multiple receptive fields. To overcome those limitations, we propose a novel model based on multiview graph attention network, namely, scMGATGRN, to infer GRNs. scMGATGRN mainly consists of GAT, multiview, and view-level attention mechanism. GAT can extract essential features of the gene regulatory network. The multiview model can simultaneously utilize local feature information and high-order neighbor feature information of nodes in the gene regulatory network. The view-level attention mechanism dynamically adjusts the relative importance of node embedding representations and efficiently aggregates node embedding representations from two views. To verify the effectiveness of scMGATGRN, we compared its performance with 10 methods (five shallow learning algorithms and five state-of-the-art DL-based methods) on seven benchmark single-cell RNA sequencing (scRNA-seq) datasets from five cell lines (two in human and three in mouse) with four different kinds of ground-truth networks. The experimental results not only show that scMGATGRN outperforms competing methods but also demonstrate the potential of this model in inferring GRNs. The code and data of scMGATGRN are made freely available on GitHub (https://github.com/nathanyl/scMGATGRN).
