scACAN: An Adaptive Learning Framework Aggregating Local Graph Structure Context for Rare Cell Type Identification

Single-cell RNA sequencing (scRNA-seq) technology has become an essential tool for dissecting cellular heterogeneity and elucidating complex biological systems. Nevertheless, the uneven distribution of cell types and the limited representation of rare cell populations present substantial challenges for effective modeling and accurate identification. Most existing methods primarily focus on the annotation of abundant cell types, often overlooking rare, yet biologically significant subpopulations. In addition, the variability of cellular distributions across different biological contexts highlights the need for models with greater adaptability and a stronger capacity for contextual information integration. To overcome these challenges, we introduced scACAN, an adaptive graph construction framework that leverages aggregated local graph context information to design a positive sample selection strategy. By incorporating adaptive sampling and iterative optimization based on clustering results, scACAN effectively enhances the identification of both the major and rare cell types. Comprehensive experiments on multiple real-world scRNA-seq data sets demonstrate that scACAN achieves superior performance and reveals additional biologically meaningful rare cell subpopulations, providing a robust and generalizable solution for single-cell data analysis.