scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing

TL;DR

scCluBench is a comprehensive, standardized benchmark for evaluating clustering algorithms on diverse single-cell RNA sequencing datasets, aiding method selection and understanding model performance in real-world scenarios.

Contribution

It introduces a unified benchmarking framework with curated datasets, standardized evaluation protocols, and extensive analysis of clustering methods for scRNA-seq data.

Findings

Deep learning-based methods outperform traditional algorithms in accuracy.

Performance varies significantly across different biological tasks.

scCluBench reveals robustness and scalability limits of current clustering models.

Abstract

Cell clustering is crucial for uncovering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data by identifying cell types and marker genes. Despite its importance, benchmarks for scRNA-seq clustering methods remain fragmented, often lacking standardized protocols and failing to incorporate recent advances in artificial intelligence. To fill these gaps, we present scCluBench, a comprehensive benchmark of clustering algorithms for scRNA-seq data. First, scCluBench provides 36 scRNA-seq datasets collected from diverse public sources, covering multiple tissues, which are uniformly processed and standardized to ensure consistency for systematic evaluation and downstream analyses. To evaluate performance, we collect and reproduce a range of scRNA-seq clustering methods, including traditional, deep learning-based, graph-based, and biological foundation models. We comprehensively evaluate each method both quantitatively and qualitatively, using core performance metrics as well as visualization analyses. Furthermore, we construct representative downstream biological tasks, such as marker gene identification and cell type annotation, to further assess the practical utility. scCluBench then investigates the performance differences and applicability boundaries of various clustering models across diverse analytical tasks, systematically assessing their robustness and scalability in real-world scenarios. Overall, scCluBench offers a standardized and user-friendly benchmark for scRNA-seq clustering, with curated datasets, unified evaluation protocols, and transparent analyses, facilitating informed method selection and providing valuable insights into model generalizability and application scope.