Integrated machine learning approaches for flow cytometric quantification of myeloid-derived suppressor cells in acute sepsis

Integrated machine learning approaches for flow cytometric quantification of myeloid-derived suppressor cells in acute sepsis

Highly heterogeneous cell populations require multiple flow cytometric markers for appropriate phenotypic characterization. This exponentially increases the complexity of 2D scatter plot analyses and exacerbates human errors due to variations in manual gating of flow data. We describe a semi-automat...

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Bibliographic Details
Main Authors: Anthony S. Bonavia, Abigail Samuelsen, Joshua Luthy, E. Scott Halstead
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-11-01
Series:Frontiers in Immunology
Subjects:
high parameter flow cytometry
sepsis - diagnostics
clustering analysis
clinical immunology
myeloid-derived suppressor cells (MDSC)
Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2022.1007016/full

Internet

https://www.frontiersin.org/articles/10.3389/fimmu.2022.1007016/full

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