%0 Journal Article %A Peijie Lin %A Michael Troup %A Joshua W. K. Ho %T CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-Seq data %D 2016 %R 10.1101/068775 %J bioRxiv %P 068775 %X 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. %U https://www.biorxiv.org/content/biorxiv/early/2016/08/31/068775.full.pdf