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 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 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 for 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.org/VCCRI/CIDR.