scGFormer: A Multi-Scale Graph-Transformer for Cell Type Annotation in Single-Cell RNA Sequencing

Abstract

Despite the rapid progress of single-cell RNA sequencing (scRNA-seq), accurate cell type annotation remains a major challenge. Existing approaches often struggle with sparse and heterogeneous expression profiles, insufficient gene-level modeling, and complications such as zero inflation and class imbalance. To address these issues, we propose scGFormer (Single-cell Multi-scale Graph Transformer), a unified framework that integrates: (i) Performer-based Global Attention (PGA) to capture long-range dependencies, (ii) Graph-based Local Attention (GLA) to model neighborhood structures, and (iii) a Squeeze-and-Excitation Gene Reweighting module (GeneSE) to enhance gene-level representations. Furthermore, scGFormer is equipped with a biology-guided adaptive contrastive learning strategy, which is designed to account for zero inflation, balance class distributions, and refine dynamic graphs during training, thereby facilitating robustness and adaptability. By explicitly modeling both global and local dependencies while strengthening gene-level representations, scGFormer achieves improved robustness and generalization. Extensive experiments across public datasets demonstrate that scGFormer achieves competitive or superior performance compared with state-of-the-art methods, offering a robust solution for single-cell annotation across diverse datasets and species.