File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Ultrafast clustering of single-cell flow cytometry data using FlowGrid

TitleUltrafast clustering of single-cell flow cytometry data using FlowGrid
Authors
KeywordsClustering
DBSCAN
Flow cytometry
Single cell
Issue Date2019
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcsystbiol/
Citation
17th Asia Pacific Bioinformatics Conference (APBC 2019): systems biology, Wuhan, China. 14-16 January 2019. In BMC Systems Biology, v. 13, p. 35 How to Cite?
AbstractBACKGROUND: Flow cytometry is a popular technology for quantitative single-cell profiling of cell surface markers. It enables expression measurement of tens of cell surface protein markers in millions of single cells. It is a powerful tool for discovering cell sub-populations and quantifying cell population heterogeneity. Traditionally, scientists use manual gating to identify cell types, but the process is subjective and is not effective for large multidimensional data. Many clustering algorithms have been developed to analyse these data but most of them are not scalable to very large data sets with more than ten million cells. RESULTS: Here, we present a new clustering algorithm that combines the advantages of density-based clustering algorithm DBSCAN with the scalability of grid-based clustering. This new clustering algorithm is implemented in python as an open source package, FlowGrid. FlowGrid is memory efficient and scales linearly with respect to the number of cells. We have evaluated the performance of FlowGrid against other state-of-the-art clustering programs and found that FlowGrid produces similar clustering results but with substantially less time. For example, FlowGrid is able to complete a clustering task on a data set of 23.6 million cells in less than 12 seconds, while other algorithms take more than 500 seconds or get into error. CONCLUSIONS: FlowGrid is an ultrafast clustering algorithm for large single-cell flow cytometry data. The source code is available at https://github.com/VCCRI/FlowGrid .
Persistent Identifierhttp://hdl.handle.net/10722/271406
ISSN
2018 Impact Factor: 2.048
2015 SCImago Journal Rankings: 1.493
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYe, X-
dc.contributor.authorHo, JWK-
dc.date.accessioned2019-06-24T01:09:15Z-
dc.date.available2019-06-24T01:09:15Z-
dc.date.issued2019-
dc.identifier.citation17th Asia Pacific Bioinformatics Conference (APBC 2019): systems biology, Wuhan, China. 14-16 January 2019. In BMC Systems Biology, v. 13, p. 35-
dc.identifier.issn1752-0509-
dc.identifier.urihttp://hdl.handle.net/10722/271406-
dc.description.abstractBACKGROUND: Flow cytometry is a popular technology for quantitative single-cell profiling of cell surface markers. It enables expression measurement of tens of cell surface protein markers in millions of single cells. It is a powerful tool for discovering cell sub-populations and quantifying cell population heterogeneity. Traditionally, scientists use manual gating to identify cell types, but the process is subjective and is not effective for large multidimensional data. Many clustering algorithms have been developed to analyse these data but most of them are not scalable to very large data sets with more than ten million cells. RESULTS: Here, we present a new clustering algorithm that combines the advantages of density-based clustering algorithm DBSCAN with the scalability of grid-based clustering. This new clustering algorithm is implemented in python as an open source package, FlowGrid. FlowGrid is memory efficient and scales linearly with respect to the number of cells. We have evaluated the performance of FlowGrid against other state-of-the-art clustering programs and found that FlowGrid produces similar clustering results but with substantially less time. For example, FlowGrid is able to complete a clustering task on a data set of 23.6 million cells in less than 12 seconds, while other algorithms take more than 500 seconds or get into error. CONCLUSIONS: FlowGrid is an ultrafast clustering algorithm for large single-cell flow cytometry data. The source code is available at https://github.com/VCCRI/FlowGrid .-
dc.languageeng-
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcsystbiol/-
dc.relation.ispartofBMC Systems Biology-
dc.rightsBMC Systems Biology. Copyright © BioMed Central Ltd.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectClustering-
dc.subjectDBSCAN-
dc.subjectFlow cytometry-
dc.subjectSingle cell-
dc.titleUltrafast clustering of single-cell flow cytometry data using FlowGrid-
dc.typeConference_Paper-
dc.identifier.emailHo, JWK: jwkho@hku.hk-
dc.identifier.authorityHo, JWK=rp02436-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s12918-019-0690-2-
dc.identifier.pmid30953498-
dc.identifier.pmcidPMC6449887-
dc.identifier.scopuseid_2-s2.0-85064006754-
dc.identifier.hkuros298176-
dc.identifier.volume13-
dc.identifier.spage35-
dc.identifier.epage35-
dc.identifier.isiWOS:000463771600001-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats