![]() Where cytometry is used for data acquisition, the typical objective is to discern differences between groups of subjects or experimental conditions, or to identify a phenotype that correlates with an experimental or clinical endpoint. In response to these shortcomings, a cross-disciplinary effort has given birth to a new approach often termed ‘cytometry bioinformatics’, to leverage complex computer algorithms and machine learning to automate analysis and improve the investigator’s ability to extract meaning from high-dimensional data. As the field evolves, the traditional method of manual gating by sub-setting single cell data into populations and encircling data points in hand-drawn polygons in two-dimensional space is proving laborious, subjective, and difficult to standardise. This is a PLOS Computational Biology Software paper.Ĭytometry data analysis has undergone a paradigm shift in response to the growing number of parameters that can be observed in any one experiment. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. įunding: This research received support from the UK Clinical Research Network (UKCRN) Study Portfolio grant number: 11838 (to M.E.), the Welsh European Funding Office’s Accelerate programme grant number:PR-0013 (to M.E.), Medical Research Council (MRC) grant number: MR/N023145/1(to M.E.), Wales Kidney Research Unit (WKRU) (to M.E.), and a School of Medicine PhD Studentship (to R.J.B.). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Details on how to download the original dataset can be found in the software documentation. Received: ApAccepted: Published: June 8, 2021Ĭopyright: © 2021 Burton et al. PLoS Comput Biol 17(6):Įditor: Manja Marz, bioinformatics, GERMANY Ĭitation: Burton RJ, Ahmed R, Cuff SM, Baker S, Artemiou A, Eberl M (2021) CytoPy: An autonomous cytometry analysis framework. CytoPy is available at, with notebooks accompanying this manuscript ( ) and software documentation at. CytoPy is open-source and licensed under the MIT license. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. 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. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. 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. 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. 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. Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. ![]()
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