ScGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization.

ABSTRACT: Single-cell technologies have transformed our understanding of cellular heterogeneity through multimodal data acquisition. However, robust cell alignment remains a major challenge for data integration and harmonization, including batch correction, label transfer, and multi-omics integration. Many existing methods constrain alignment based on rigid feature-wise distance metrics, limiting their ability to capture accurate cell correspondence across diverse cell populations and conditions. We introduce scGALA, a graph-based learning framework that redefines cell alignment by combining graph attention networks with a score-driven, task-independent optimization strategy. scGALA constructs enriched graphs of cell-cell relationships by integrating gene expression profiles with auxiliary informati

SUBMITTER: Jiang G 

PROVIDER: S-EPMC12748608 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature