CellScope: high-performance cell atlas workflow with tree-structured representation

Single-cell sequencing enables comprehensive profiling of individual cells, revealing cellular heterogeneity and function with unprecedented resolution. However, current analysis frameworks lack the ability to simultaneously explore and visualize cellular hierarchies at multiple biological levels. To address these limitations, we present CellScope, a promising framework for constructing high-resolution cell atlases at multiple clustering levels. CellScope employs a two-stage manifold fitting process for gene selection and noise reduction, followed by agglomerative clustering, and integrates UMAP visualization with hierarchical clustering to intuitively represent cellular relationships simultaneously at multiple levels—such as cell lineage, cell type, and cell subtype levels. Compared to established pipelines such as Seurat and Scanpy, CellScope comprehensively improves clustering performance, visualization clarity, computational efficiency, and algorithm interpretability, while reducing dependence on hyperparameters across a multitude of single-cell datasets. Most importantly, it can reveal biological insights that other contemporary methods are unable to detect, thereby deepening our understanding of cellular heterogeneity and function, and potentially informing disease research. Li and colleagues present CellScope, a tree-structured framework that reveals multi-level cellular hierarchies and gene functions in single-cell data. This approach provides clear clustering, intuitive visualization, and deep biological views into cell types and functions.