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- Publisher Website: 10.1093/bioinformatics/btab521
- PMID: 34289014
- WOS: WOS:000736120000045
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Article: FlowGrid enables fast clustering of very large single-cell RNA-seq data
Title | FlowGrid enables fast clustering of very large single-cell RNA-seq data |
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Authors | |
Issue Date | 2022 |
Publisher | Oxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/ |
Citation | Bioinformatics, 2022, v. 38 n. 1, p. 282-283 How to Cite? |
Abstract | Motivation:
Scalable clustering algorithms are needed to analyze millions of cells in single cell RNA-seq (scRNA-seq) data.
Results:
Here, we present an open source python package called FlowGrid that can integrate into the Scanpy workflow to perform clustering on very large scRNA-seq datasets. FlowGrid implements a fast density-based clustering algorithm originally designed for flow cytometry data analysis. We introduce a new automated parameter tuning procedure, and show that FlowGrid can achieve comparable clustering accuracy as state-of-the-art clustering algorithms but at a substantially reduced run time for very large single cell RNA-seq datasets. For example, FlowGrid can complete a one-hour clustering task for one million cells in about five min.
Availability and implementation:
https://github.com/holab-hku/FlowGrid.
Supplementary information:
Supplementary data are available at Bioinformatics online. |
Persistent Identifier | http://hdl.handle.net/10722/301912 |
ISSN | 2023 Impact Factor: 4.4 2023 SCImago Journal Rankings: 2.574 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Fang, X | - |
dc.contributor.author | Ho, JWK | - |
dc.date.accessioned | 2021-08-21T03:28:49Z | - |
dc.date.available | 2021-08-21T03:28:49Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Bioinformatics, 2022, v. 38 n. 1, p. 282-283 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301912 | - |
dc.description.abstract | Motivation: Scalable clustering algorithms are needed to analyze millions of cells in single cell RNA-seq (scRNA-seq) data. Results: Here, we present an open source python package called FlowGrid that can integrate into the Scanpy workflow to perform clustering on very large scRNA-seq datasets. FlowGrid implements a fast density-based clustering algorithm originally designed for flow cytometry data analysis. We introduce a new automated parameter tuning procedure, and show that FlowGrid can achieve comparable clustering accuracy as state-of-the-art clustering algorithms but at a substantially reduced run time for very large single cell RNA-seq datasets. For example, FlowGrid can complete a one-hour clustering task for one million cells in about five min. Availability and implementation: https://github.com/holab-hku/FlowGrid. Supplementary information: Supplementary data are available at Bioinformatics online. | - |
dc.language | eng | - |
dc.publisher | Oxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/ | - |
dc.relation.ispartof | Bioinformatics | - |
dc.title | FlowGrid enables fast clustering of very large single-cell RNA-seq data | - |
dc.type | Article | - |
dc.identifier.email | Ho, JWK: jwkho@hku.hk | - |
dc.identifier.authority | Ho, JWK=rp02436 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1093/bioinformatics/btab521 | - |
dc.identifier.pmid | 34289014 | - |
dc.identifier.hkuros | 324193 | - |
dc.identifier.volume | 38 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 282 | - |
dc.identifier.epage | 283 | - |
dc.identifier.isi | WOS:000736120000045 | - |
dc.publisher.place | United Kingdom | - |