scGND: Graph neural diffusion model enhances single-cell RNA-seq analysis

Abstract

Single-cell sequencing technologies have played a pivotal role in advancing biomedical research over the last decade. With the evolution of deep learning, a variety of models based on deep neural networks have been developed to improve the precision of single-cell RNA sequencing (scRNA-seq) analysis from multiple angles. However, deep learning models currently used in scRNA-seq analysis frequently suffer from a lack of interpretability. In this study, we present a novel physics-informed graph generative model, termed Single Cell Graph Neural Diffusion (scGND). This model is founded on solid mathematical concepts and provides enhanced interpretability. Unlike methods that focus solely on gene expression in individual cells, scGND concentrates on the cell-cell interaction graph, incorporating two key physical concepts: local and global equilibrium. We show that achieving a balance between local and global equilibrium significantly improves the geometric properties of the graph, aiding in the extraction of inherent biological insights from the cell-cell interaction graph at multiple scales. The effectiveness of scGND has been proven through benchmark tests involving five independent scRNA-seq datasets from various tissues and species. scGND consistently achieves better or comparable results comparing with several established competitors in both clustering and trajectory analysis. scGND represents a comprehensive generative model based on cell graph diffusion, demonstrating considerable promise for both theoretical and practical applications in scRNA-seq data analysis.