A cancer biologist’s primer on machine learning applications in high-dimensional cytometry

Abstract: The application of machine learning and artificial intelligence to high‐dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single‐cell data in a high‐throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data. We introduce the reader to several keystone machine learning‐based analytic approaches with an emphasis on defining key terms and introducing a conceptual framework for making translational or clinically relevant discoveries. The target audience consists of cancer cell biologists and physician‐scientists interested in applying these tools to their own data, but who may have limited training in bioinformatics

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