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CytoPy: an autonomous cytometry analysis framework

View ORCID ProfileRoss J. Burton, Raya Ahmed, Simone M. Cuff, Andreas Artemiou, Matthias Eberl
doi: https://doi.org/10.1101/2020.04.08.031898
Ross J. Burton
1Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
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  • ORCID record for Ross J. Burton
  • For correspondence: burtonrj@cardiff.ac.uk
Raya Ahmed
1Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
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Simone M. Cuff
1Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
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Andreas Artemiou
2School of Mathematics, Cardiff University, Cardiff, Wales, UK
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Matthias Eberl
1Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
3Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom
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Abstract

Cytometry analysis has grown in recent years with the expansion in the maximum number of parameters that can be acquired in a single experiment. In response to this there has been an increased effort to develop 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 cytometry. Here we present CytoPy, a Python framework for automated analysis of high dimensional cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. The capability of supervised classification algorithms in CytoPy to identify cell subsets was successfully confirmed by using the FlowCAP-I competition data. The applicability of the complete analytical pipeline to real world datasets was validated by immunophenotyping the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. Source code is available online at the https://github.com/burtonrj/CytoPy, and software documentation can be found at https://cytopy.readthedocs.io/.

Footnotes

  • https://cytopy.readthedocs.io/en/latest/

  • https://github.com/burtonrj/CytoPy

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 09, 2020.
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CytoPy: an autonomous cytometry analysis framework
Ross J. Burton, Raya Ahmed, Simone M. Cuff, Andreas Artemiou, Matthias Eberl
bioRxiv 2020.04.08.031898; doi: https://doi.org/10.1101/2020.04.08.031898
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CytoPy: an autonomous cytometry analysis framework
Ross J. Burton, Raya Ahmed, Simone M. Cuff, Andreas Artemiou, Matthias Eberl
bioRxiv 2020.04.08.031898; doi: https://doi.org/10.1101/2020.04.08.031898

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