WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for understanding cellular heterogeneity, providing unprecedented resolution in molecular regulation analysis. Existing supervised learning approaches for cell type annotation primarily utilize gene expression profiles from scRNA-seq data. Although some methods incorporated gene interaction network information, they fail to use cell-specific gene association networks. This limitation overlooks the unique gene interaction patterns within individual cells, potentially compromising the accuracy of cell type classification. We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). These networks are constructed based on highly varia

SUBMITTER: Wang YR 

PROVIDER: S-EPMC11872911 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature