How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

Hannah den Braanker, Margot Bongenaar, Erik Lubberts*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)
287 Downloads (Pure)

Abstract

Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed, as opposed to the common practice of manual gating in conventional flow cytometry. However, preparing spectral flow cytometry data for high-dimensional analysis can be challenging, because of several technical aspects. In this article, we will give insight into the pitfalls of handling spectral flow cytometry datasets. Moreover, we will describe a workflow to properly prepare spectral flow cytometry data for high dimensional analysis and tools for integrating new data at later time points. Using healthy control data as example, we will go through the concepts of quality control, data cleaning, transformation, correcting for batch effects, subsampling, clustering and data integration. This methods article provides an R-based pipeline based on previously published packages, that are readily available to use. Application of our workflow will aid spectral flow cytometry users to obtain valid and reproducible results.

Original languageEnglish
Article number768113
JournalFrontiers in Immunology
Volume12
DOIs
Publication statusPublished - 19 Nov 2021

Bibliographical note

Funding Information:
The authors would like to thank Mohammed Charrout (Delft University of Technology) for sharing his expertise and critical review of the article.

Publisher Copyright:
Copyright © 2021 den Braanker, Bongenaar and Lubberts.

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