Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.

Authors

Yu-Hang Yin, Fang Wang, Wei Li, Qiaoming Liu, Shengming Zhou, Murong Zhou, Zhongjun Jiang, Dong-Jun Yu, Guohua Wang

Published in

Genome biology. Volume 26. Issue 1. Pages 265. Sep 03, 2025. Epub Sep 03, 2025.

Abstract

Differences in data distribution, feature dimensions, and quality between different single-cell modalities pose challenges for clustering. Although clustering algorithms have been developed for single-cell transcriptomic or proteomic data, their performance across different omics data types and integration scenarios remains poorly investigated, which limits the selection of methods and future method development.
In this study, we conduct a systematic and comparative benchmark analysis of 28 computational algorithms on 10 paired transcriptomic and proteomic datasets, evaluating their performance across various metrics in terms of clustering, peak memory, and running time. We also discuss the impact of highly variable genes (HVGs) and cell type granularity on clustering performance. Additionally, the robustness of these clustering methods on two kinds of omics is evaluating by using 30 simulated datasets. Furthermore, to explore the benefits of integrating omics information for clustering tasks, we integrate single-cell transcriptomic and proteomic data using 7 state-of-the-art integration methods and assess the performance of existing single-omics clustering schemes on the integrated features.
Our findings reveal modality-specific strengths and limitations, highlight the complementary nature of existing methods, and provide actionable insights to guide the selection of appropriate clustering approaches for specific scenarios. Overall, for top performance across two omics, consider scAIDE, scDCC, and FlowSOM, with FlowSOM also offering excellent robustness. For users prioritizing memory efficiency scDCC and scDeepCluster are recommended, while TSCAN, SHARP, and MarkovHC are recommended for users who prioritize time efficiency, and community detection-based methods offer a balance.

PMID:
40903792
Bibliographic data and abstract were imported from PubMed on 04 Sep 2025.

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