PT  - JOURNAL ARTICLE
AU  - Peijie Lin
AU  - Michael Troup
AU  - Joshua W. K. Ho
TI  - CIDR: Ultrafast and accurate clustering through imputation for single cell RNA-Seq data
AID  - 10.1101/068775
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 068775
4099  - http://biorxiv.org/content/early/2016/08/10/068775.short
4100  - http://biorxiv.org/content/early/2016/08/10/068775.full
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.