The rapid expansion of flow cytometry applications has outpaced the functionality of traditional manual analysis tools used to interpret flow cytometry data. Scientists are faced with the daunting prospect of manually identifying interesting cell populations in 50-dimensional datasets, equalling the complexity previously only reached in mass cytometry. Data can no longer be analyzed or interpreted fully by manual approaches. While automated gating has been the focus of intense efforts, there are many significant additional steps to the analytical pipeline (e.g., cleaning the raw files, event outlier detection, extracting immunophenotypes). We review the components of a customized automated analysis pipeline that can be generally applied to large scale flow cytometry data. We demonstrate these methodologies on data collected by the International Mouse Phenotyping Consortium (IMPC).

中文翻译：

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大流式细胞术数据的高通量自动分析


流式细胞术应用的快速扩展已经超过了用于解释流式细胞术数据的传统手动分析工具的功能。科学家面临着在 50 维数据集中手动识别感兴趣的细胞群的艰巨前景，其复杂性相当于以前只有在质谱流式分析中才能达到的复杂性。无法再通过手动方法完全分析或解释数据。虽然自动门控一直是人们努力的焦点，但分析流程中还有许多重要的附加步骤（例如，清理原始文件、事件异常值检测、提取免疫表型）。我们回顾了通常可应用于大规模流式细胞术数据的定制自动化分析管道的组件。我们根据国际小鼠表型联盟 (IMPC) 收集的数据展示了这些方法。