Key Points High-dimensional single-cell analysis uncovers heterogeneity in flow cytometry data. Correct data transformation of flow cytometry data is crucial for interpretation. Extensively documented R code incorporates existing single-cell analysis tools. Visual Abstract The introduction of single-cell platforms inspired the development of high-dimensional single-cell analysis tools to comprehensively characterize the underlying cellular heterogeneity. Flow cytometry data are traditionally analyzed by (subjective) gating of subpopulations on two-dimensional plots. However, the increasing number of parameters measured by conventional and spectral flow cytometry reinforces the need to apply many of the recently developed tools for single-cell analysis on flow cytometry data, as well. However, the myriads of analysis options offered by the continuously released novel packages can be overwhelming to the immunologist with limited computational background. In this article, we explain the main concepts of such analyses and provide a detailed workflow to illustrate their implications and additional prerequisites when applied on flow cytometry data. Moreover, we provide readily applicable R code covering transformation, normalization, dimensionality reduction, clustering, and pseudotime analysis that can serve as a template for future analyses. We demonstrate the merit of our workflow by reanalyzing a public human dataset. Compared with standard gating, the results of our workflow provide new insights in cellular subsets, alternative classifications, and hypothetical trajectories. Taken together, we present a well-documented workflow, which utilizes existing high-dimensional single-cell analysis tools to reveal cellular heterogeneity and intercellular relationships in flow cytometry data.

中文翻译：

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将单细胞聚类和伪时间分析应用于流式细胞术数据的综合工作流程

要点 高维单细胞分析揭示了流式细胞术数据的异质性。流式细胞术数据的正确数据转换对于解释至关重要。广泛记录的 R 代码结合了现有的单细胞分析工具。视觉摘要单细胞平台的引入激发了高维单细胞分析工具的发展，以全面表征潜在的细胞异质性。流式细胞术数据传统上是通过对二维图上的亚群进行（主观）门控来分析的。然而，越来越多的常规和光谱流式细胞术测量的参数也加强了将许多最近开发的工具应用于流式细胞术数据的单细胞分析的必要性。然而，不断发布的新软件包提供的无数分析选项对于计算背景有限的免疫学家来说可能是压倒性的。在本文中，我们解释了此类分析的主要概念，并提供了详细的工作流程来说明它们在应用于流式细胞术数据时的影响和其他先决条件。此外，我们提供了易于应用的 R 代码，涵盖转换、归一化、降维、聚类和伪时间分析，可作为未来分析的模板。我们通过重新分析公共人类数据集来证明我们工作流程的优点。与标准门控相比，我们工作流程的结果为细胞子集、替代分类和假设轨迹提供了新的见解。综上所述，我们提出了一个有据可查的工作流程，