LineGRN: A Line Graph Neural Network for Gene Regulatory Network Inference.

Abstract

Gene regulatory networks (GRNs) depicts the complex interactions between transcription factors and target genes, offering profound insight into deciphering the mechanisms of cellular processes. The advancement of single-cell RNA sequencing (scRNA-seq) technologies has provided a crucial perspective for inferring GRNs at single-cell resolution, leading to the development of numerous computational methods for GRN inference. However, most existing methods fail to adequately capture the association patterns between gene pairs, and the low-degree-node-dominated topology of prior GRNs imposes fundamental limitations on information propagation. In this study, we propose LineGRN, a novel line graph neural network framework for inferring GRNs from scRNA-seq data. By modeling the neighborhood relationships between gene pairs, LineGRN effectively preserves interaction signals within the topological structure. Moreover, the line graph transformation produces a high-degree-node-dominated local network topology, which enables more efficient information propagation. Comprehensive experiments on real datasets demonstrate that LineGRN significantly outperforms seven state-of-the-art methods. Furthermore, LineGRN exhibits low sensitivity to parameter variations and noise interference. Notably, case studies provide empirical evidence of the model's ability to uncover potential TF-target regulatory associations.