Algorithmic Tools for Mining High-Dimensional Cytometry Data

Abstract

The advent of mass cytometry has led to an unprecedented increase in the number of analytes measured in individual cells, thereby increasing the complexity and information content of cytometric data. Although this technology is ideally suited to the detailed examination of the immune system, the applicability of the different methods for analyzing such complex data is less clear. Conventional data analysis by manual gating of cells in biaxial dot plots is often subjective, time consuming, and neglectful of much of the information contained in a highly dimensional cytometric dataset. Algorithmic data mining has the promise to eliminate these concerns, and several such tools have been applied recently to mass cytometry data. We review computational data mining tools that have been used to analyze mass cytometry data, outline their differences, and comment on their strengths and limitations. This review will help immunologists to identify suitable algorithmic tools for their particular projects.