scGMB: A scRNA‐seq Cell Classification Method Combining GCN and Mamba

ABSTRACT

This study proposes a single‐cell RNA sequencing data classification method called scGMB, which is based on graph convolutional networks (GCN) and the Mamba model. This method constructs a cell graph to capture the topological relationships between cells and combines selective state–space models (SSMs) to extract complex features of gene expression patterns. The innovations of scGMB are as follows: (1) the GCN module captures the topological relationships between cells; (2) the Mamba module efficiently processes sparse and high‐dimensional data; (3) the end‐to‐end architecture improves computational efficiency and interpretability of the results. We validated scGMB on five datasets: Zheng68 K, Zhengsorted, BaronHuman, BaronMouse and AMB, achieving classification accuracies of 72.2%, 91.1%, 98.9%, 99.2% and 99.5%, respectively, outperforming existing mainstream methods. Experimental results demonstrate that scGMB can effectively identify cell types, offering a high‐performance and stable new tool for single‐cell data analysis. Future work will expand scGMB to spatial transcriptomics and multi‐omics data analysis, further optimising model performance to aid biomedical research and cell type discovery.