CIDR swMATH ID: 32140 Software Authors: Lin, P., Troup, M., Ho, J. W. K. Description: CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. 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 . Homepage: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5371246/ Source Code: https://github.com/VCCRI/CIDR Related Software: ZIFA; MAST; SC3; SNN-Cliq; DESeq2; DEseq; WGCNA; mixtools; t-SNE; mixfdr; EBSeq; GlobalMIT; MrTADFinder; slingshot; ToppGene Suite; GenClust; DINGO; SCODE; SCENIC; DNA Cited in: 7 Publications all top 5 Cited by 21 Authors 2 Zhao, Hongyu 1 Daley, Timothy 1 Devlin, Bernie 1 Huang, Haiyan 1 Kendziorski, Christina 1 Korthauer, Keegan 1 Lei, Jing 1 Li, Jingyi Jessica 1 Li, Lexin 1 Lin, Zhixiang 1 Liu, Yiyi 1 Ma, Shining 1 Ma, Xiuyu 1 Newton, Michael A. 1 Park, Seyoung 1 Roeder, Kathryn 1 Suner, Aslı 1 Warren, Joshua L. 1 Wong, Wing Hung 1 Zamanighomi, Mahdi 1 Zhu, Lingxue Cited in 3 Serials 4 The Annals of Applied Statistics 2 Statistical Science 1 Statistical Applications in Genetics and Molecular Biology Cited in 2 Fields 7 Statistics (62-XX) 5 Biology and other natural sciences (92-XX) Citations by Year