scALGSL: Active Learning and Graph Structure Learning for Cell Type Annotation From Single-Cell RNA-Seq Data

The breakthrough development of single-cell RNA sequencing technology enables tissue heterogeneity analysis at single-cell resolution, where accurate cell type annotation is crucial for unlocking its full potential. To address three key challenges in current annotation methods-scarce labeled data, suboptimal graph topology, and missing cell state information-we propose scALGSL, an innovative framework integrating dynamic graph optimization with active learning. Our core contributions are threefold: (1) A graph-guided active learning mechanism adaptively selects high-value training samples, significantly alleviating label scarcity; (2) A learnable graph structure optimization module dynamically refines adjacency matrices to eliminate spurious connections caused by data sparsity; (3) A novel cell state auxiliary pathway extracts critical functional features via pre-trained models to enhance type discrimination. The systematic review showed that the average accuracy and f1 of scALGSL on the cancer dataset were 0.896 and 0.771, respectively, and it showed good robustness in cross-platform tasks. Integration of cell state information substantially boosts performance, while ablation studies validate the necessity of node selection and edge optimization modules. This framework provides a scalable solution for precise cell annotation, facilitating tumor microenvironment analysis and precision medicine applications.