A Cancer Biologist's Primer on Machine Learning Applications in High‐Dimensional Cytometry. Issue 8 (30th June 2020)

• Record Type: Journal Article
• Authors: Keyes, Timothy J.; Domizi, Pablo; Lo, Yu‐Chen; Nolan, Garry P.; Davis, Kara L.
• Is Part Of: Cytometry. Volume 97:Issue 8(2020)
• Page Start: 782
• Page End: 799
• Publication Date: 2020-06-30
• Subjects: machine learning -- mass cytometry -- cancer -- computational cytometry -- data science; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.24158
• Languages: English
• ISSNs: 1552-4922

Abstract

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. © 2020 International Society for Advancement of Cytometry Abstract : In this review, we introduce the reader to the machine learning algorithms most commonly applied to single‐cell data in cancer biology. Without assuming any in‐depth prior knowledge of bioinformatics, we discuss multiple state‐of‐the‐art algorithms for dimensionality reduction, clustering, and predictive modeling as they apply to the study of cancer. After reading this review, readers should be able to understand the main strengths and weaknesses of each of the algorithms that we discuss as well as how such analyses can be applied to their own clinical or experimental data sets.