title: CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data.
abstract: Most existing dimensionality reduction and clustering packages for single-cell RNA-seq (scRNA-seq) data deal with dropouts by heavy modeling and computational machinery. Here, we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.com/VCCRI/CIDR .
authors.affiliation: Victor Chang Cardiac Research Institute, Darlinghurst, 2010, NSW, Australia.|St Vincent's Clinical School, University of New South Wales, Darlinghurst, 2010, NSW, Australia.
authors.forename: Peijie
authors.initials: P
authors.lastname: Lin
authors.affiliation: Victor Chang Cardiac Research Institute, Darlinghurst, 2010, NSW, Australia.
authors.forename: Michael
authors.initials: M
authors.lastname: Troup
authors.affiliation: Victor Chang Cardiac Research Institute, Darlinghurst, 2010, NSW, Australia. j.ho@victorchang.edu.au.|St Vincent's Clinical School, University of New South Wales, Darlinghurst, 2010, NSW, Australia. j.ho@victorchang.edu.au.
authors.forename: Joshua W K
authors.initials: JW
authors.lastname: Ho
mesh_terms: D000465:Algorithms; D001921:Brain / Q000378:metabolism; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D066264:Datasets as Topic; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods*; D006801:Humans; D009928:Organ Specificity / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods; D059010:Single-Cell Analysis / Q000379:methods; D012984:Software
keywords: Cell type; Clustering; Dimensionality reduction; Dropout; Imputation; Single-cell; scRNA-seq