{"title":"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","doi":"10.1186/s13059-025-03719-y","DOIUrl":null,"url":null,"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.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"28 1","pages":""},"PeriodicalIF":10.1000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13059-025-03719-y","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
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.
Genome BiologyBiochemistry, Genetics and Molecular Biology-Genetics
CiteScore
21.00
自引率
3.30%
发文量
241
审稿时长
2 months
期刊介绍:
Genome Biology stands as a premier platform for exceptional research across all domains of biology and biomedicine, explored through a genomic and post-genomic lens.
With an impressive impact factor of 12.3 (2022),* the journal secures its position as the 3rd-ranked research journal in the Genetics and Heredity category and the 2nd-ranked research journal in the Biotechnology and Applied Microbiology category by Thomson Reuters. Notably, Genome Biology holds the distinction of being the highest-ranked open-access journal in this category.
Our dedicated team of highly trained in-house Editors collaborates closely with our esteemed Editorial Board of international experts, ensuring the journal remains on the forefront of scientific advances and community standards. Regular engagement with researchers at conferences and institute visits underscores our commitment to staying abreast of the latest developments in the field.