scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein–protein interactions

Recent advances in single-cell RNA sequencing (scRNA-seq) techniques have provided unprecedented insights into the heterogeneity of various tissues. However, gene expression data alone often fails to capture and identify changes in cellular pathways and complexes, as they are more discernible at the protein level. Moreover, analyzing scRNA-seq data presents further challenges due to inherent characteristics such as high noise levels and zero inflation. In this study, we propose an approach to address these limitations by integrating scRNA-seq datasets with a protein–protein interaction network. Our method utilizes a unique dual-view architecture based on graph neural networks, enabling joint representation of gene expression and protein–protein interaction network data. This approach models gene-to-gene relationships under specific biological contexts and refines cell–cell relations using an attention mechanism. Next, through comprehensive evaluations, we demonstrate that scNET better captures gene annotation, pathway characterization and gene–gene relationship identification, while improving cell clustering and pathway analysis across diverse cell types and biological conditions. scNET combines single-cell gene expression information with protein–protein interaction networks using a dual-view architecture based on graph neural networks to better characterize changes in cellular pathways and complexes across cellular conditions.