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

ABSTRACT: 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 model

SUBMITTER: Sheinin R 

PROVIDER: S-EPMC11978505 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature