A Beginner's Guide to Analyzing and Visualizing Mass Cytometry Data

Area of Science:

• Immunology
• Computational Biology
• Biotechnology

Background:

• Mass cytometry offers high-dimensional single-cell analysis, expanding phenotypic and functional characterization.
• This platform necessitates novel data analysis approaches beyond traditional flow cytometry.
• Beginners face challenges due to numerous algorithms and lack of consensus on best practices.

Purpose of the Study:

• To compare five cytometry by time-of-flight (CyTOF) analysis platforms: viSNE, SPADE, X-shift, PhenoGraph, and Citrus.
• To identify considerations and challenges for users of these high-dimensional data analysis methods.
• To provide a practical guide for analyzing complex CyTOF datasets.

Main Methods:

• Analysis of a single mass cytometry dataset using five distinct computational algorithms.
• Comparative evaluation of algorithm performance and biological insights generated.
• Development of annotated workflows and figures for practical guidance.

Main Results:

• Each algorithm revealed common and unique biological insights from the high-dimensional data.
• Identified key considerations and challenges associated with using different CyTOF analysis platforms.
• Demonstrated the value of integrating multiple algorithms for comprehensive data interpretation.

Conclusions:

• Integrating multiple CyTOF analysis algorithms is beneficial for gaining complementary insights.
• A practical guide is provided to aid investigators in analyzing high-dimensional datasets.
• Emphasizes the need for careful algorithm selection and integration in mass cytometry studies.