Diggins Kirsten E, Ferrell P Brent, Irish Jonathan M
Cancer Biology, Vanderbilt University School of Medicine, United States.
Medicine/Division of Hematology-Oncology, Vanderbilt University School of Medicine, United States.
Methods. 2015 Jul 1;82:55-63. doi: 10.1016/j.ymeth.2015.05.008. Epub 2015 May 13.
The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of machine learning to capture multi-parametric relationships and reveal cells that are easily overlooked in traditional analysis. Here, we introduce a workflow for high dimensional mass cytometry data that emphasizes unsupervised approaches and visualizes data in both single cell and population level views. This workflow includes three central components that are common across mass cytometry analysis approaches: (1) distinguishing initial populations, (2) revealing cell subsets, and (3) characterizing subset features. In the implementation described here, viSNE, SPADE, and heatmaps were used sequentially to comprehensively characterize and compare healthy and malignant human tissue samples. The use of multiple methods helps provide a comprehensive view of results, and the largely unsupervised workflow facilitates automation and helps researchers avoid missing cell populations with unusual or unexpected phenotypes. Together, these methods develop a framework for future machine learning of cell identity.
测量单个细胞40多个特征的质谱流式细胞术实验所产生的高维数据洪流，刺激了新的单细胞计算生物学工具的创建。这些工具利用机器学习领域的进展来捕捉多参数关系，并揭示在传统分析中容易被忽视的细胞。在这里，我们介绍一种用于高维质谱流式细胞术数据的工作流程，该流程强调无监督方法，并在单细胞和群体水平视图中可视化数据。此工作流程包括质谱流式细胞术分析方法中常见的三个核心组件：（1）区分初始群体，（2）揭示细胞亚群，以及（3）表征亚群特征。在此处描述的实施过程中，依次使用了viSNE、SPADE和热图来全面表征和比较健康和恶性人类组织样本。使用多种方法有助于提供全面的结果视图，并且主要为无监督的工作流程便于自动化，并帮助研究人员避免遗漏具有异常或意外表型的细胞群体。这些方法共同为未来细胞身份的机器学习建立了一个框架。
