Making the most of high‐dimensional cytometry data
Felix Marsh‐Wakefield, Andrew J. Mitchell, Samuel E Norton, Thomas M. Ashhurst, Julia K. H. Leman, Joanna Roberts, Jessica E Harte, Helen M. McGuire, Roslyn A. Kemp
Abstract
High-dimensional cytometry represents an exciting new era of immunology research, enabling the discovery of new cells and prediction of patient responses to therapy. A plethora of analysis and visualization tools and programs are now available for both new and experienced users; however, the transition from low- to high-dimensional cytometry requires a change in the way users think about experimental design and data analysis. Data from high-dimensional cytometry experiments are often underutilized, because of both the size of the data and the number of possible combinations of markers, as well as to a lack of understanding of the processes required to generate meaningful data. In this article, we explain the concepts behind designing high-dimensional cytometry experiments and provide considerations for new and experienced users to design and carry out high-dimensional experiments to maximize quality data collection.