A Comprehensive Workflow for Applying Single-Cell Clustering and Pseudotime Analysis to Flow Cytometry Data

The Journal of Immunology, vol. 205, pp. 864–871

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.

Authors 5

1. Janine E. Melsen corresponding Aachen Department of Pediatrics, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Pediatrics, Leiden University Medical Center , 2333 ZA Leiden ,

   Department of Pediatrics, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands

2. Monique M. van Ostaijen-ten Dam Aachen Department of Pediatrics, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Pediatrics, Leiden University Medical Center , 2333 ZA Leiden ,

   Department of Pediatrics, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands

3. Arjan C. Lankester Aachen Department of Pediatrics, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Pediatrics, Leiden University Medical Center , 2333 ZA Leiden ,

   Department of Pediatrics, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands

4. Marco W. Schilham Aachen Department of Pediatrics, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Pediatrics, Leiden University Medical Center , 2333 ZA Leiden ,

   Department of Pediatrics, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands

5. Erik B. van den Akker Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University Medical Center , 2333 ZC Leiden ,

   Pattern Recognition and Bioinformatics Group, Delft University of Technology , 2628 XE Delft ,

   Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC Leiden, the Netherlands

   Pattern Recognition and Bioinformatics Group, Delft University of Technology, 2628 XE Delft, the Netherlands