Secuer: 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 sp...

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Bibliographic Details
Published in:	PLoS computational biology Vol. 18; no. 12; p. e1010753
Main Authors:	Wei, Nana, Nie, Yating, Liu, Lin, Zheng, Xiaoqi, Wu, Hua-Jun
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 01.12.2022 Public Library of Science (PLoS)
Subjects:	Algorithms Biology and Life Sciences Cluster Analysis Computer and Information Sciences Engineering and Technology Gene Expression Profiling - methods Physical Sciences Research and Analysis Methods Sequence Analysis, RNA - methods Single-Cell Analysis - methods Single-Cell Gene Expression Analysis
ISSN:	1553-7358, 1553-734X, 1553-7358
Online Access:	Get full text
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Summary:	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.
Bibliography:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1010753