Cytoflow：用于计算流式细胞术的用户友好型Python软件

摘要(中文译文)

现代流式细胞术实验通常要在众多样本、患者和组织中检测18种或更多的荧光标记。随着这些实验复杂性的增加，人工设门（manual gating）变得效率低下到令人难以接受的程度，并且可能引入操作者之间的差异。计算流式细胞术算法，如无监督聚类、自动设门和降维，可以提高这些分析的速度和可靠性，但使用这些算法需要许多生物医学科学家所缺乏的时间和专业知识。Cytoflow正是为弥合这一差距而构建的。Cytoflow是用Python编写的开源、用户友好的点击式软件，使非程序员能够将现代计算流式细胞术方法应用于他们的数据集。此外，Cytoflow的模块可以直接在Python脚本或JupyterLab笔记本中使用。最后，用支持未来应用的新模块扩展Cytoflow是简单直接的，而活跃用户社区的反馈持续指导着正在进行的开发。因此，Cytoflow可以为实验者节省时间，并提高其分析的可靠性和可重复性。Cytoflow可从https://cytoflow.github.io下载。源代码托管于https://github.com/cytoflow/cytoflow，文档可在https://cytoflow.readthedocs.io获取。

Abstract

Modern flow cytometry experiments routinely measure 18 or more fluorescent markers across many samples, patients, and tissues. As these experiments' complexity increases, manual gating becomes unacceptably inefficient and can introduce operator-to-operator variation. Computational cytometry algorithms such as unsupervised clustering, automated gating, and dimensionality reduction can increase the speed and reliability of these analyses, but using them requires time and expertise that many biomedical scientists lack. Cytoflow was built to bridge this gap. Cytoflow is open source, user-friendly point-and-click software written in Python that allows non-programmers to apply modern computational flow cytometry methods to their data sets. Additionally, Cytoflow's modules can be used directly in a Python script or a JupyterLab notebook. Finally, extending Cytoflow with new modules that support future applications is straightforward, and feedback from an active user community continues to guide ongoing development. As a result, Cytoflow can save an experimenter time and improve the reliability and reproducibility of their analysis. Cytoflow is available to download at https://cytoflow.github.io. Source code is hosted at https://github.com/cytoflow/cytoflow, and documentation is available at https://cytoflow.readthedocs.io.

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