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

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