Cytometry by time-of-flight (CyTOF) has emerged as a high-throughput single cell technology able to provide large samples of protein readouts. Already, there exists a large pool of advanced high-dimensional analysis algorithms that explore the observed heterogeneous distributions making intriguing biological inferences. A fact largely overlooked by these methods, however, is the effect of the established data preprocessing pipeline to the distributions of the measured quantities. In this article, we focus on randomization, a transformation used for improving data visualization, which can negatively affect multivariate data analysis methods such as dimensionality reduction, clustering, and network reconstruction algorithms. Our results indicate that randomization should be used only for visualization purposes, but not in conjunction with high-dimensional analytical tools. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

中文翻译：

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质量细胞计数数据的多元分析中的挑战：随机化的影响。

飞行时间细胞计数法（CyTOF）已成为一种高通量单细胞技术，能够提供大量蛋白质读数。已经存在大量高级的高维分析算法，这些算法可以探索观察到的异质分布，从而产生有趣的生物学推断。但是，这些方法在很大程度上忽略了一个事实，即已建立的数据预处理管道对测量数量分布的影响。在本文中，我们将重点放在随机化上，该转换是一种用于改善数据可视化的转换，它可能对多维数据分析方法（例如降维，聚类和网络重构算法）产生负面影响。我们的结果表明，随机化仅应用于可视化目的，但不能与高维分析工具结合使用。©2019作者。细胞计数法A部分，由Wiley Periodicals，Inc.代表国际细胞计数发展协会出版。