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Application of Machine Learning for Cytometry Data
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DOI：10.3389/fimmu.2021.787574.png)
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摘要
En 中文
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.
Keyword:
cytometry
cyTOF
machine learning
predictive modeling
flow cytometry
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期刊
IF:
5.9
论文数:
5.0W
被引数:
22.7W
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NATURE BIOTECHNOLOGY
OAAI
NATURE BIOTECHNOLOGYIF41.7
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