Application of Machine Learning for Cytometry Data

Modern cytometry technologies present opportunities to profile the immune system at a single-cell resolution with more than 50 protein markers, and have been widely used in both research and clinical settings. The number of publicly available cytometry datasets is growing. However, the analysis of cytometry data remains a bottleneck due to its high dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometry data and have been used in multiple facets of cytometry data analysis, including dimensionality reduction, cell population identification, and sample classification. Here, we review the existing machine learning applications for analyzing cytometry data and highlight the importance of publicly available cytometry data that enable researchers to develop and validate machine learning methods.

Automated summary

Modern cytometry technologies allow profiling of the immune system at a single-cell resolution with over 50 protein markers, and the number of publicly available cytometry datasets is increasing. Analyzing cytometry data remains challenging due to its high dimensionality, large cell numbers, and dataset heterogeneity, but machine learning techniques are well suited for addressing these challenges and have been employed in various aspects of cytometry data analysis.