Automated clustering of heterotrophic bacterioplankton in flow cytometry data

Flow cytometry has become a standard method to analyze bacterioplankton. Analysis of samples by flow cytometry is automatic, but it is followed by manual classification of the bacterioplankton groups in flow cytometry standard (FCS) files. This classification is a time consuming and subjective task...

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Detalles Bibliográficos
Autores: García-García, Francisca del Carmen, López-Urrutia-Lorente, Ángel, Morán, Xosé Anxelu G.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2014
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:dnet:digitalcsic_::8554cd67c7a703923b57be7bd4cd0aa0
Acceso en línea:http://hdl.handle.net/10261/319517
Access Level:acceso abierto
Palabra clave:Centro Oceanográfico de Gijón
flow cytometry
Medio Marino
bacterioplankton
automatic clustering
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spelling Automated clustering of heterotrophic bacterioplankton in flow cytometry dataGarcía-García, Francisca del CarmenLópez-Urrutia-Lorente, ÁngelMorán, Xosé Anxelu G.Centro Oceanográfico de Gijónflow cytometryMedio Marinobacterioplanktonautomatic clusteringFlow cytometry has become a standard method to analyze bacterioplankton. Analysis of samples by flow cytometry is automatic, but it is followed by manual classification of the bacterioplankton groups in flow cytometry standard (FCS) files. This classification is a time consuming and subjective task performed by manually drawing the limits of the groups present in cytograms, a process referred to as gating. The automation of flow cytometry data processing based on pattern recognition techniques could provide an efficient tool to overcome some of these disadvantages. Here, we propose the use of model-based clustering techniques for the automatic detection of low (LNA) and high (HNA) nucleic acid bacterioplankton groups in FCS files. To validate our method, we compared the automatic classification with a flow cytometry database from a 9 yr time series collected in the central Cantabrian Sea that had been manually analyzed. The correlation between automatic and manual gating methods was >0.9 for cell counts and 0.7 to 0.95 for side scatter values, a proxy of cell size. In addition, no significant differences were found in the mean annual cycle of LNA and HNA cell abundance depicted by both methods. We also quantified the subjectivity of manual gating. The coefficient of variation for heterotrophic bacteria counts obtained by different analysts was around 10 to 20%. Our results suggest that the combination of flow cytometry and automatic gating provides a valuable tool to analyze microbial communities objectively and accurately, allowing us to safely compare bacterioplankton samples from different environments.SI202320232014info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/319517reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésCentro Oceanográfico de Gijónhttp://www.int-res.com/articles/ame2013/72/a072p175.pdfinfo:eu-repo/semantics/openAccessoai:dnet:digitalcsic_::8554cd67c7a703923b57be7bd4cd0aa02026-05-22T06:33:51Z
dc.title.none.fl_str_mv Automated clustering of heterotrophic bacterioplankton in flow cytometry data
title Automated clustering of heterotrophic bacterioplankton in flow cytometry data
spellingShingle Automated clustering of heterotrophic bacterioplankton in flow cytometry data
García-García, Francisca del Carmen
Centro Oceanográfico de Gijón
flow cytometry
Medio Marino
bacterioplankton
automatic clustering
title_short Automated clustering of heterotrophic bacterioplankton in flow cytometry data
title_full Automated clustering of heterotrophic bacterioplankton in flow cytometry data
title_fullStr Automated clustering of heterotrophic bacterioplankton in flow cytometry data
title_full_unstemmed Automated clustering of heterotrophic bacterioplankton in flow cytometry data
title_sort Automated clustering of heterotrophic bacterioplankton in flow cytometry data
dc.creator.none.fl_str_mv García-García, Francisca del Carmen
López-Urrutia-Lorente, Ángel
Morán, Xosé Anxelu G.
author García-García, Francisca del Carmen
author_facet García-García, Francisca del Carmen
López-Urrutia-Lorente, Ángel
Morán, Xosé Anxelu G.
author_role author
author2 López-Urrutia-Lorente, Ángel
Morán, Xosé Anxelu G.
author2_role author
author
dc.subject.none.fl_str_mv Centro Oceanográfico de Gijón
flow cytometry
Medio Marino
bacterioplankton
automatic clustering
topic Centro Oceanográfico de Gijón
flow cytometry
Medio Marino
bacterioplankton
automatic clustering
description Flow cytometry has become a standard method to analyze bacterioplankton. Analysis of samples by flow cytometry is automatic, but it is followed by manual classification of the bacterioplankton groups in flow cytometry standard (FCS) files. This classification is a time consuming and subjective task performed by manually drawing the limits of the groups present in cytograms, a process referred to as gating. The automation of flow cytometry data processing based on pattern recognition techniques could provide an efficient tool to overcome some of these disadvantages. Here, we propose the use of model-based clustering techniques for the automatic detection of low (LNA) and high (HNA) nucleic acid bacterioplankton groups in FCS files. To validate our method, we compared the automatic classification with a flow cytometry database from a 9 yr time series collected in the central Cantabrian Sea that had been manually analyzed. The correlation between automatic and manual gating methods was >0.9 for cell counts and 0.7 to 0.95 for side scatter values, a proxy of cell size. In addition, no significant differences were found in the mean annual cycle of LNA and HNA cell abundance depicted by both methods. We also quantified the subjectivity of manual gating. The coefficient of variation for heterotrophic bacteria counts obtained by different analysts was around 10 to 20%. Our results suggest that the combination of flow cytometry and automatic gating provides a valuable tool to analyze microbial communities objectively and accurately, allowing us to safely compare bacterioplankton samples from different environments.
publishDate 2014
dc.date.none.fl_str_mv 2014
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/319517
url http://hdl.handle.net/10261/319517
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Centro Oceanográfico de Gijón
http://www.int-res.com/articles/ame2013/72/a072p175.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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