biostudies-literatureWei NShanghai Municipal Science and Technology Major ProjectFundamental Research Funds for the Central UniversitiesNational Key R&D Program of ChinaNatural Science Foundation of ShanghaiNational Natural Science Foundation of ChinaPujiang National Lab Grante1010753https://www.ebi.ac.uk/biostudies/studies/S-EPMC9754601biostudies-literatureUnknown18(12)Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage over one order of magnitude for datasets with more than 1 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks.PLoS computational biologySecuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.PMC97546012018YFA090060020JC1413800615723272021SHZDZX010221ZR143100021JC1402900322706836197225712101397WF220441912PKU2022LCXQ027P22KN00524BMU2021YJ06412090024Wei NZheng XLiu LWu HJNie YfalseSecuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage over one order of magnitude for datasets with more than 1 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks.2022-01-01T00:00:00Z2022 Dec2025-04-04T19:11:42.649Z2025-04-04T19:11:42.649ZS-EPMC97546013646954310.1371/journal.pcbi.1010753