MCNET: Multi-Omics Integration for Gene Regulatory Network Inference from scRNA-seq

Abstract Deep learning has emerged as a powerful approach in various domains, including biological network analysis. This paper investigates the advancements in computational techniques for inferring gene regulatory networks (GRNs) and introduces MCNET, a state-of-the-art deep learning algorithm. MCNET integrates multi-omics data to infer GRNs and extract biologically significant representations from single-cell RNA sequencing (scRNA-seq) data. By incorporating attention mechanisms and graph convolutional networks, MCNET captures intricate regulatory relationships among genes. Extensive benchmarking on diverse scRNA-seq datasets demonstrates MCNET’s superiority over existing methods in GRN inference, scRNA-seq data visualization, clustering, and simulation. Notably, MCNET accurately predicts gene regulations on cell-type marker genes in the mouse cortex, validated by epigenetic data. The introduction of MCNET paves the way for advanced analysis of scRNA-seq data and provides a powerful tool for inferring GRNs in a multi-omics context. Moreover, this paper addresses the integration of multiomics data in gene regulatory network inference, proposing MCNET as a method that efficiently analyzes and visualizes homogeneous gene regulatory networks derived from diverse omics data. The inference capability of MCNET is evaluated through extensive experiments with simulation data and applied to analyze the biological network of psychiatric disorders using human brain data.