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- Publisher Website: 10.1186/s13059-017-1188-0
- Scopus: eid_2-s2.0-85016502564
- PMID: 28351406
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Article: CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
Title | CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data |
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Authors | |
Keywords | Clustering Cell type Dimensionality reduction Dropout Imputation ScRNA-seq Single-cell |
Issue Date | 2017 |
Citation | Genome Biology, 2017, v. 18, n. 1 How to Cite? |
Abstract | © 2017 The Author(s). 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. |
Persistent Identifier | http://hdl.handle.net/10722/262852 |
ISSN | 2012 Impact Factor: 10.288 2023 SCImago Journal Rankings: 7.197 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lin, Peijie | - |
dc.contributor.author | Troup, Michael | - |
dc.contributor.author | Ho, Joshua W.K. | - |
dc.date.accessioned | 2018-10-08T02:47:16Z | - |
dc.date.available | 2018-10-08T02:47:16Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Genome Biology, 2017, v. 18, n. 1 | - |
dc.identifier.issn | 1474-7596 | - |
dc.identifier.uri | http://hdl.handle.net/10722/262852 | - |
dc.description.abstract | © 2017 The Author(s). 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. | - |
dc.language | eng | - |
dc.relation.ispartof | Genome Biology | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Clustering | - |
dc.subject | Cell type | - |
dc.subject | Dimensionality reduction | - |
dc.subject | Dropout | - |
dc.subject | Imputation | - |
dc.subject | ScRNA-seq | - |
dc.subject | Single-cell | - |
dc.title | CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1186/s13059-017-1188-0 | - |
dc.identifier.pmid | 28351406 | - |
dc.identifier.scopus | eid_2-s2.0-85016502564 | - |
dc.identifier.volume | 18 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | null | - |
dc.identifier.epage | null | - |
dc.identifier.eissn | 1474-760X | - |
dc.identifier.isi | WOS:000397557000004 | - |
dc.identifier.issnl | 1474-7596 | - |