CytoPy: An autonomous cytometry analysis framework

Ross J Burton, Raya Ahmed, Simone M Cuff, Sarah Baker, Andreas Artemiou and Matthias Eberl

PLOS Computational Biology, 2021, vol. 17, issue 6, 1-21

Abstract: 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 https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.Author summary: Cytometry is a popular technology used to quantify biological material. In recent years, the capabilities of cytometry have expanded, resulting in ever larger datasets. In order to analyse these data, new approaches are required, giving rise to the field of cytometry bioinformatics. Despite the success of numerous algorithms and tools in this domain, widespread adoption by the scientific community has yet to be realised. Here we introduce CytoPy, a comprehensive cytometry data analysis framework deployed in Python, a beginner friendly programming language. We validate CytoPy’s ability to handle batch effects and identify immune cell populations in human blood. Subsequently, we apply CytoPy to the analysis of drain fluid from patients undergoing peritoneal dialysis and compare the local immune response of stable patients to those presenting with acute peritonitis. CytoPy is open source and available online: https://cytopy.readthedocs.io/en/latest/.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009071 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 09071&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1009071

DOI: 10.1371/journal.pcbi.1009071

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().