CytoPy: An autonomous cytometry analysis framework

• Published in: PLoS computational biology Vol. 17; no. 6; p. e1009071
• Main Authors: Burton, Ross J., Ahmed, Raya, Cuff, Simone M., Baker, Sarah, Artemiou, Andreas, Eberl, Matthias
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Application programming interface; Automation; Bacterial diseases; Bioinformatics; Biology and Life Sciences; Classification; Clustering; Collaboration; Computational Biology; Computer and Information Sciences; Computer applications; Computer programs; Cytometry; Data acquisition; Data analysis; Databases, Factual; Design; Engineering and Technology; Experiments; Flow cytometry; Genotype & phenotype; Humans; Image Cytometry - statistics & numerical data; Immunology; Immunophenotyping - statistics & numerical data; Inflammation; Iterative methods; Lymphocytes; Lymphocytes T; Machine Learning; Mathematical analysis; Medicine and Health Sciences; Metadata; Peritoneal Dialysis - adverse effects; Peritonitis - diagnosis; Peritonitis - immunology; Peritonitis - pathology; Phenotypes; Physical Sciences; Programming Languages; Research and Analysis Methods; Science; Software; Source code; T-Lymphocyte Subsets - immunology; T-Lymphocyte Subsets - pathology; Technology application; United Kingdom
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1009071

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/ .