Title |
CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
|
---|---|
Published in |
Genome Biology, March 2017
|
DOI | 10.1186/s13059-017-1188-0 |
Pubmed ID | |
Authors |
Peijie Lin, Michael Troup, Joshua W. K. Ho |
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 . |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Australia | 4 | 16% |
United States | 4 | 16% |
United Kingdom | 2 | 8% |
Netherlands | 1 | 4% |
France | 1 | 4% |
Germany | 1 | 4% |
Hong Kong | 1 | 4% |
Taiwan | 1 | 4% |
Unknown | 10 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 16 | 64% |
Members of the public | 5 | 20% |
Science communicators (journalists, bloggers, editors) | 3 | 12% |
Practitioners (doctors, other healthcare professionals) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 1% |
United Kingdom | 2 | <1% |
Denmark | 1 | <1% |
Mexico | 1 | <1% |
Japan | 1 | <1% |
Poland | 1 | <1% |
Unknown | 364 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 101 | 27% |
Researcher | 67 | 18% |
Student > Bachelor | 29 | 8% |
Student > Master | 28 | 7% |
Student > Doctoral Student | 21 | 6% |
Other | 56 | 15% |
Unknown | 72 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 93 | 25% |
Agricultural and Biological Sciences | 71 | 19% |
Computer Science | 58 | 16% |
Medicine and Dentistry | 14 | 4% |
Mathematics | 13 | 3% |
Other | 41 | 11% |
Unknown | 84 | 22% |