Ensuring Full Spectrum Flow Cytometry Data Quality for High‐Dimensional Data Analysis

Abstract

AbstractFull spectrum flow cytometry (FSFC) allows for the analysis of more than 40 parameters at the single‐cell level. Compared to the practice of manual gating, high‐dimensional data analysis can be used to fully explore single‐cell datasets and reduce analysis time. As panel size and complexity increases so too does the detail and time required to prepare and validate the quality of the resulting data for use in downstream high‐dimensional data analyses. To ensure data analysis algorithms can be used efficiently and to avoid artifacts, some important steps should be considered. These include data cleaning (such as eliminating variable signal change over time, removing cell doublets, and antibody aggregates), proper unmixing of full spectrum data, ensuring correct scale transformation, and correcting for batch effects. We have developed a methodical step‐by‐step protocol to prepare full spectrum high‐dimensional data for use with high‐dimensional data analyses, with a focus on visualizing the impact of each step of data preparation using dimensionality reduction algorithms. Application of our workflow will aid FSFC users in their efforts to apply quality control methods to their datasets for use in high‐dimensional analysis, and help them to obtain valid and reproducible results. © 2023 Wiley Periodicals LLC.Basic Protocol 1: Data cleaningBasic Protocol 2: Validating the quality of unmixingBasic Protocol 3: Data scalingBasic Protocol 4: Batch‐to‐batch normalization