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Review

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

1
Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
2
Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
*
Author to whom correspondence should be addressed.
Vaccines 2020, 8(1), 138; https://doi.org/10.3390/vaccines8010138
Received: 27 February 2020 / Revised: 17 March 2020 / Accepted: 18 March 2020 / Published: 20 March 2020
Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented. View Full-Text
Keywords: vaccination; multiparametric flow cytometry; mass cytometry; computational data analysis; automated analysis; machine learning vaccination; multiparametric flow cytometry; mass cytometry; computational data analysis; automated analysis; machine learning
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MDPI and ACS Style

Lucchesi, S.; Furini, S.; Medaglini, D.; Ciabattini, A. From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies. Vaccines 2020, 8, 138. https://doi.org/10.3390/vaccines8010138

AMA Style

Lucchesi S, Furini S, Medaglini D, Ciabattini A. From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies. Vaccines. 2020; 8(1):138. https://doi.org/10.3390/vaccines8010138

Chicago/Turabian Style

Lucchesi, Simone, Simone Furini, Donata Medaglini, and Annalisa Ciabattini. 2020. "From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies" Vaccines 8, no. 1: 138. https://doi.org/10.3390/vaccines8010138

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