多重单细胞实验技术,如质量细胞计数可测量40个或更多的特征,并能够对众所周知的新细胞群体进行深入表征。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,和新的机器学习算法可能会产生意想不到的群体分组。标记富集建模(MEM)基于相对于参考在群体中富集的特征创建定量身份标签。虽然开发用于细胞类型分析,可以为各种多维数据类型生成MEM标签,MEM可以有效地与专家分析和各种机器学习算法的输出一起工作。MEM作为R包实现,包括三个步骤:(1)计算MEM值,以量化每个特征在总体中的相对丰富度,(2)将MEM标签报告为热图或文本标签,和(3)群体间MEM标记相似性的量化。此处的方案显示使用来自免疫学和肿瘤学的数据集的MEM分析。这些MEM实现提供了一种在计算和专家分析的背景下表征群体身份和新颖性的方法。©2018由JohnWiley&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.