Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).
Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).
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DOI：
10.1002/cpcy.34
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发表时间：
2018-01-18
影响因子：
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通讯作者：
Irish, Jonathan M
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文献类型：
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作者：
Diggins, Kirsten E;Gandelman, Jocelyn S;Irish, Jonathan M
多重单细胞实验技术，如质量细胞术，测量40个或更多特征，并使众所周知的和新的细胞群体的深入表征成为可能。然而，传统的数据分析技术广泛依赖于人类专家或先验知识，而新颖的机器学习算法可能会产生意想不到的群体分组。标记富集化建模(MEM)基于相对于参考在种群中丰富的特征来创建定量身份标签。虽然MEM标签是为细胞类型分析而开发的，但可以为各种多维数据类型生成MEM标签，并且MEM通过专家分析和各种机器学习算法的输出有效地工作。MEM以R包的形式实现，包括三个步骤：(1)计算量化每个特征在种群中的相对丰富度的MEM值，(2)将MEM标签报告为热图或文本标签，以及(3)量化种群之间的MEM标签相似性。这里的方案显示了使用免疫学和肿瘤学的数据集进行的MEM分析。这些MEM的实施提供了一种在计算和专家分析的背景下描述人口特性和新颖性的方法。2018年John Wiley&Sons，Inc.
Multiplexed single-cell experimental techniques like mass cytometry measure 40 or more features and enable deep characterization of well-known and novel cell populations. However, traditional data analysis techniques rely extensively on human experts or prior knowledge, and novel machine learning algorithms may generate unexpected population groupings. Marker enrichment modeling (MEM) creates quantitative identity labels based on features enriched in a population relative to a reference. While developed for cell type analysis, MEM labels can be generated for a wide range of multidimensional data types, and MEM works effectively with output from expert analysis and diverse machine learning algorithms. MEM is implemented as an R package and includes three steps: (1) calculation of MEM values that quantify each feature's relative enrichment in the population, (2) reporting of MEM labels as a heatmap or as a text label, and (3) quantification of MEM label similarity between populations. The protocols here show MEM analysis using datasets from immunology and oncology. These MEM implementations provide a way to characterize population identity and novelty in the context of computational and expert analyses. © 2018 by John Wiley & Sons, Inc.