Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada; Department of Bioinformatics, University of British Columbia, Vancouver, BC, Canada.
Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada.
Methods. 2018 Feb 1;134-135:164-176. doi: 10.1016/j.ymeth.2017.12.015. Epub 2017 Dec 27.
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).
流式细胞术应用的迅速扩展已经超过了传统的手动分析工具的功能，这些工具用于解释流式细胞术数据。科学家们面临着在 50 维数据集中手动识别有趣细胞群体的艰巨任务，这与以前在质谱流式细胞术中达到的复杂性相当。数据不再可以通过手动方法进行充分分析或解释。虽然自动化门控一直是集中努力的焦点，但分析管道还有许多其他重要步骤（例如，清理原始文件、事件异常值检测、提取免疫表型）。我们回顾了可一般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。我们在国际小鼠表型分析联盟（IMPC）收集的数据上演示了这些方法。
