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 c...

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Bibliographic Details
Published in:Frontiers in immunology 2022-01, Vol.12, p.787574-787574
Main Authors: Hu, Zicheng, Bhattacharya, Sanchita, Butte, Atul J
Format: Article
Language:English
Subjects:
Animals
cyTOF
cytometry
flow cytometry
Flow Cytometry - methods
Humans
Immunology
Machine Learning
predictive modeling
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Summary: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.
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2021.787574