Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom.
School of Mathematics, Cardiff University, Cardiff, United Kingdom.
PLoS Comput Biol. 2021 Jun 8;17(6):e1009071. doi: 10.1371/journal.pcbi.1009071. eCollection 2021 Jun.
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/.
近年来，流式细胞术和质谱流式细胞术在单次实验中能够获取的参数数量方面有了相当大的扩展。为了应对这一技术进步，人们已经加大了努力，开发新的计算方法来处理通过流式或质谱流式术获取的高维单细胞数据。尽管有许多算法和已发布的软件包成功地复制和超越了传统的手动分析，但这些技术在免疫学领域尚未得到广泛采用。在这里，我们介绍了 CytoPy，这是一个用于自动化分析流式细胞术数据的 Python 框架，它集成了基于文档的数据库，以实现数据为中心和迭代的分析环境。此外，我们的算法不可知设计为 Python 生态系统中的开源流式细胞术生物信息学提供了一个平台。我们展示了 CytoPy 即使在由于技术和用户变化导致显著批次效应的情况下，也能够对全血样本中的 T 细胞亚群进行表型分析的能力。然后，我们使用完整的分析管道对患有和不患有急性细菌感染的个体中的局部炎症浸润进行免疫表型分析。CytoPy 是开源的，并根据麻省理工学院许可证获得许可。CytoPy 可在 https://github.com/burtonrj/CytoPy 上获得，随附本文的笔记本（https://github.com/burtonrj/CytoPyManuscript）和软件文档在 https://cytopy.readthedocs.io/ 上。
