RT Journal Article SR Electronic T1 CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-Seq data JF bioRxiv FD Cold Spring Harbor Laboratory SP 068775 DO 10.1101/068775 A1 Peijie Lin A1 Michael Troup A1 Joshua W. K. Ho YR 2016 UL http://biorxiv.org/content/early/2016/08/31/068775.abstract AB Most existing dimensionality reduction and clustering packages for single-cell RNA-Seq (scRNA-Seq) data deal with dropouts by heavy modelling and computational machinery. Here we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm which 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 have shown that CIDR outperforms the state-of-the-art methods, namely t-SNE, ZIFA and RaceID, by at least 50% in terms of clustering accuracy, and typically completes within seconds for processing a dataset of hundreds of cells.CIDR can be downloaded at https://github.org/VCCRI/CIDR.