CytoPy: An autonomous cytometry analysis framework.

Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional...

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Autores principales:	Ross J Burton, Raya Ahmed, Simone M Cuff, Sarah Baker, Andreas Artemiou, Matthias Eberl
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f0baf2a6344c4c2892eac5cab14ee123
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spelling	oai:doaj.org-article:f0baf2a6344c4c2892eac5cab14ee1232021-11-25T05:40:38ZCytoPy: An autonomous cytometry analysis framework.1553-734X1553-735810.1371/journal.pcbi.1009071https://doaj.org/article/f0baf2a6344c4c2892eac5cab14ee1232021-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009071https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.Ross J BurtonRaya AhmedSimone M CuffSarah BakerAndreas ArtemiouMatthias EberlPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 6, p e1009071 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ross J Burton Raya Ahmed Simone M Cuff Sarah Baker Andreas Artemiou Matthias Eberl CytoPy: An autonomous cytometry analysis framework.
description	Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.
format	article
author	Ross J Burton Raya Ahmed Simone M Cuff Sarah Baker Andreas Artemiou Matthias Eberl
author_facet	Ross J Burton Raya Ahmed Simone M Cuff Sarah Baker Andreas Artemiou Matthias Eberl
author_sort	Ross J Burton
title	CytoPy: An autonomous cytometry analysis framework.
title_short	CytoPy: An autonomous cytometry analysis framework.
title_full	CytoPy: An autonomous cytometry analysis framework.
title_fullStr	CytoPy: An autonomous cytometry analysis framework.
title_full_unstemmed	CytoPy: An autonomous cytometry analysis framework.
title_sort	cytopy: an autonomous cytometry analysis framework.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f0baf2a6344c4c2892eac5cab14ee123
work_keys_str_mv	AT rossjburton cytopyanautonomouscytometryanalysisframework AT rayaahmed cytopyanautonomouscytometryanalysisframework AT simonemcuff cytopyanautonomouscytometryanalysisframework AT sarahbaker cytopyanautonomouscytometryanalysisframework AT andreasartemiou cytopyanautonomouscytometryanalysisframework AT matthiaseberl cytopyanautonomouscytometryanalysisframework
_version_	1718414553851625472

CytoPy: An autonomous cytometry analysis framework.

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