ScGraphformer: unveiling cellular heterogeneity and interactions in scRNA-seq data using a scalable graph transformer network.

ABSTRACT: The precise classification of cell types from single-cell RNA sequencing (scRNA-seq) data is pivotal for dissecting cellular heterogeneity in biological research. Traditional graph neural network (GNN) models are constrained by reliance on predefined graphs, limiting the exploration of complex cell-to-cell relationships. We introduce scGraphformer, a transformer-based GNN that transcends these limitations by learning an all-encompassing cell-cell relational network directly from scRNA-seq data. Through an iterative refinement process, scGraphformer constructs a dense graph structure that captures the full spectrum of cellular interactions. This comprehensive approach enables the identification of subtle and previously obscured cellular patterns and relationships. Evaluated on multiple data

SUBMITTER: Fan X 

PROVIDER: S-EPMC11543810 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature