Lin, Peijie and Troup, Michael and Ho, Joshua W K (2017) CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biology, 18 (1). p. 59. ISSN 1474-760X (Gold OA)
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Abstract
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 .
| Item Type: | Article |
|---|---|
| Subjects: | R Medicine > R Medicine (General) |
| Depositing User: | Repository Administrator |
| Date Deposited: | 31 Mar 2017 03:45 |
| Last Modified: | 31 Mar 2017 03:45 |
| URI: | https://eprints.victorchang.edu.au/id/eprint/577 |
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