Computational Analysis of Microbial Flow Cytometry Data

ABSTRACT Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and st...

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Autores principales:	Peter Rubbens, Ruben Props
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2021
Materias:	bioinformatics cytometry fingerprinting data analysis microbial ecology single cell Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/14cab479e40845b6a3d45375a0994283
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spelling	oai:doaj.org-article:14cab479e40845b6a3d45375a09942832021-12-02T17:07:26ZComputational Analysis of Microbial Flow Cytometry Data10.1128/mSystems.00895-202379-5077https://doaj.org/article/14cab479e40845b6a3d45375a09942832021-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00895-20https://doaj.org/toc/2379-5077ABSTRACT Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.Peter RubbensRuben PropsAmerican Society for Microbiologyarticlebioinformaticscytometryfingerprintingdata analysismicrobial ecologysingle cellMicrobiologyQR1-502ENmSystems, Vol 6, Iss 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bioinformatics cytometry fingerprinting data analysis microbial ecology single cell Microbiology QR1-502
spellingShingle	bioinformatics cytometry fingerprinting data analysis microbial ecology single cell Microbiology QR1-502 Peter Rubbens Ruben Props Computational Analysis of Microbial Flow Cytometry Data
description	ABSTRACT Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.
format	article
author	Peter Rubbens Ruben Props
author_facet	Peter Rubbens Ruben Props
author_sort	Peter Rubbens
title	Computational Analysis of Microbial Flow Cytometry Data
title_short	Computational Analysis of Microbial Flow Cytometry Data
title_full	Computational Analysis of Microbial Flow Cytometry Data
title_fullStr	Computational Analysis of Microbial Flow Cytometry Data
title_full_unstemmed	Computational Analysis of Microbial Flow Cytometry Data
title_sort	computational analysis of microbial flow cytometry data
publisher	American Society for Microbiology
publishDate	2021
url	https://doaj.org/article/14cab479e40845b6a3d45375a0994283
work_keys_str_mv	AT peterrubbens computationalanalysisofmicrobialflowcytometrydata AT rubenprops computationalanalysisofmicrobialflowcytometrydata
_version_	1718381540237377536

Computational Analysis of Microbial Flow Cytometry Data

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