The curvHDR method for gating flow cytometry samples

Naumann, U; Luta, G; Wand, MP

The curvHDR method for gating flow cytometry samples

Naumann, U Luta, G Wand, MP

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Publication Type:: Journal Article
Citation:: BMC Bioinformatics, 2010, 11
Issue Date:: 2010-01-22

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Field	Value	Language
dc.contributor.author	Naumann, U	en_US
dc.contributor.author	Luta, G	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.available	2010-01-22	en_US
dc.date.issued	2010-01-22	en_US
dc.identifier.citation	BMC Bioinformatics, 2010, 11	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41305
dc.identifier.uri	http://hdl.handle.net/10453/14831
dc.description.abstract	Background: High-throughput flow cytometry experiments produce hundreds of large multivariate samples of cellular characteristics. These samples require specialized processing to obtain clinically meaningful measurements. A major component of this processing is a form of cell subsetting known as gating. Manual gating is time-consuming and subjective. Good automatic and semi-automatic gating algorithms are very beneficial to high-throughput flow cytometry.Results: We develop a statistical procedure, named curvHDR, for automatic and semi-automatic gating. The method combines the notions of significant high negative curvature regions and highest density regions and has the ability to adapt well to human-perceived gates. The underlying principles apply to dimension of arbitrary size, although we focus on dimensions up to three. Accompanying software, compatible with contemporary flow cytometry infor-matics, is developed.Conclusion: The method is seen to adapt well to nuances in the data and, to a reasonable extent, match human perception of useful gates. It offers big savings in human labour when processing high-throughput flow cytometry data whilst retaining a good degree of efficacy. © 2010 Naumann et al; licensee BioMed Central Ltd.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0556518
dc.relation.ispartof	BMC Bioinformatics	en_US
dc.relation.isbasedon	10.1186/1471-2105-11-44	en_US
dc.subject.classification	Bioinformatics	en_US
dc.subject.mesh	Flow Cytometry	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Software	en_US
dc.subject.mesh	Databases, Factual	en_US
dc.title	The curvHDR method for gating flow cytometry samples	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.for	0104 Statistics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

Background: High-throughput flow cytometry experiments produce hundreds of large multivariate samples of cellular characteristics. These samples require specialized processing to obtain clinically meaningful measurements. A major component of this processing is a form of cell subsetting known as gating. Manual gating is time-consuming and subjective. Good automatic and semi-automatic gating algorithms are very beneficial to high-throughput flow cytometry.Results: We develop a statistical procedure, named curvHDR, for automatic and semi-automatic gating. The method combines the notions of significant high negative curvature regions and highest density regions and has the ability to adapt well to human-perceived gates. The underlying principles apply to dimension of arbitrary size, although we focus on dimensions up to three. Accompanying software, compatible with contemporary flow cytometry infor-matics, is developed.Conclusion: The method is seen to adapt well to nuances in the data and, to a reasonable extent, match human perception of useful gates. It offers big savings in human labour when processing high-throughput flow cytometry data whilst retaining a good degree of efficacy. © 2010 Naumann et al; licensee BioMed Central Ltd.

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http://hdl.handle.net/10453/14831