Multi-view Graph Neural Network Guided Transformer for Cell-Type Annotation

Abstract

Graph neural networks (GNNs) provide an effective mechanism for enhancing transformer representations by modeling relational structures that sequence-based models cannot directly capture. In this study, a multiview graph neural network enhanced transformer architecture is proposed for cell-type annotation in single-cell RNA sequencing (scRNA-seq) data. The proposed approach models complementary relationships between cells using multiple graph views constructed from the training expression matrix, allowing the GNN component to refine transformer-derived embeddings through neighborhood-based information propagation. By combining transformer-based contextual representations with relational information from multiple graph structures, the framework exploits both structural and feature-level patterns in scRNA-seq data. Experimental results show that the proposed method improves cell-type annotation performance compared with existing approaches.