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CytoPy: An autonomous cytometry analysis framework

Burton, Ross J., Ahmed, Raya, Cuff, Simone M. ORCID:, Baker, Sarah ORCID:, Artemiou, Andreas ORCID: and Eberl, Matthias ORCID: 2021. CytoPy: An autonomous cytometry analysis framework. PLoS Computational Biology 17 (6) , e1009071. 10.1371/journal.pcbi.1009071

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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 open source and available at, with notebooks accompanying this manuscript ( and software documentation at

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Publisher: Public Library of Science
ISSN: 1553-7358
Funders: MRC
Date of First Compliant Deposit: 11 June 2021
Date of Acceptance: 12 May 2021
Last Modified: 02 Jun 2023 12:32

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