Weber Lukas M, Robinson Mark D
Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.
Cytometry A. 2016 Dec;89(12):1084-1096. doi: 10.1002/cyto.a.23030. Epub 2016 Dec 19.
Recent technological developments in high-dimensional flow cytometry and mass cytometry (CyTOF) have made it possible to detect expression levels of dozens of protein markers in thousands of cells per second, allowing cell populations to be characterized in unprecedented detail. Traditional data analysis by "manual gating" can be inefficient and unreliable in these high-dimensional settings, which has led to the development of a large number of automated analysis methods. Methods designed for unsupervised analysis use specialized clustering algorithms to detect and define cell populations for further downstream analysis. Here, we have performed an up-to-date, extensible performance comparison of clustering methods for high-dimensional flow and mass cytometry data. We evaluated methods using several publicly available data sets from experiments in immunology, containing both major and rare cell populations, with cell population identities from expert manual gating as the reference standard. Several methods performed well, including FlowSOM, X-shift, PhenoGraph, Rclusterpp, and flowMeans. Among these, FlowSOM had extremely fast runtimes, making this method well-suited for interactive, exploratory analysis of large, high-dimensional data sets on a standard laptop or desktop computer. These results extend previously published comparisons by focusing on high-dimensional data and including new methods developed for CyTOF data. R scripts to reproduce all analyses are available from GitHub (https://github.com/lmweber/cytometry-clustering-comparison), and pre-processed data files are available from FlowRepository (FR-FCM-ZZPH), allowing our comparisons to be extended to include new clustering methods and reference data sets. © 2016 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.
近期，高维流式细胞术和质谱流式细胞术（CyTOF）的技术发展使得每秒能够检测数千个细胞中数十种蛋白质标志物的表达水平，从而以前所未有的详细程度对细胞群体进行表征。在这些高维环境中，通过“手动设门”进行的传统数据分析可能效率低下且不可靠，这促使大量自动化分析方法得以发展。为无监督分析设计的方法使用专门的聚类算法来检测和定义细胞群体，以便进行进一步的下游分析。在此，我们对高维流式和质谱流式细胞术数据的聚类方法进行了最新的、可扩展的性能比较。我们使用了来自免疫学实验的几个公开可用数据集来评估这些方法，这些数据集中包含主要和稀有细胞群体，并将专家手动设门确定的细胞群体身份作为参考标准。有几种方法表现良好，包括FlowSOM、X-shift、PhenoGraph、Rclusterpp和flowMeans。其中，FlowSOM运行时间极快，使其非常适合在标准笔记本电脑或台式计算机上对大型高维数据集进行交互式探索性分析。这些结果通过专注于高维数据并纳入为CyTOF数据开发的新方法，扩展了先前发表的比较。用于重现所有分析的R脚本可从GitHub（https://github.com/lmweber/cytometry-clustering-comparison）获取，预处理数据文件可从FlowRepository（FR-FCM-ZZPH）获取，这使得我们的比较能够扩展到包括新的聚类方法和参考数据集。© 2016作者。《细胞分析A部分》由Wiley Periodicals, Inc.代表国际分析细胞学会出版。
