{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wei N"],"funding":["Shanghai Municipal Science and Technology Major Project","Fundamental Research Funds for the Central Universities","National Key R&D Program of China","Natural Science Foundation of Shanghai","National Natural Science Foundation of China","Pujiang National Lab Grant"],"pagination":["e1010753"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9754601"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(12)"],"pubmed_abstract":["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."],"journal":["PLoS computational biology"],"pubmed_title":["Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data."],"pmcid":["PMC9754601"],"funding_grant_id":["2018YFA0900600","20JC1413800","61572327","2021SHZDZX0102","21ZR1431000","21JC1402900","32270683","61972257","12101397","WF220441912","PKU2022LCXQ027","P22KN00524","BMU2021YJ064","12090024"],"pubmed_authors":["Wei N","Zheng X","Liu L","Wu HJ","Nie Y"],"additional_accession":[]},"is_claimable":false,"name":"Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.","description":"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.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-04T19:11:42.649Z","creation":"2025-04-04T19:11:42.649Z"},"accession":"S-EPMC9754601","cross_references":{"pubmed":["36469543"],"doi":["10.1371/journal.pcbi.1010753"]}}