Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data
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.
Authors
- Dong-Jun Yu
- Zhongjun Jiang
- Murong Zhou (ORCID: https://orcid.org/0000-0001-9634-8164)
- Ye Yin (ORCID: https://orcid.org/0000-0002-7051-1987)
- Wei Li (ORCID: https://orcid.org/0000-0001-7501-5707)
- Fang Wang (ORCID: https://orcid.org/0000-0002-1491-5207)
- Guohua Wang (ORCID: https://orcid.org/0000-0001-7381-2374)
- Qiaoming Liu (ORCID: https://orcid.org/0000-0001-8454-9954)
- Shengming Zhou (ORCID: https://orcid.org/0009-0003-3515-2864)
Institutions
- Nankai University (CN)
- Wenzhou Medical University (CN)
- Henan University of Technology (CN)
- Nanjing University of Science and Technology (CN)
- Northeast Forestry University (CN)
Publication Details
- Journal
- Genome biology
- Published
- 2025-09-03
- DOI
- https://doi.org/10.1186/s13059-025-03719-y
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
- National Science Fund for Distinguished Young Scholars