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

Simone Lucchesi1, Simone Furini2, Donata Medaglini1

  • 1Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy.

Vaccines
|April 5, 2020
PubMed

Insights

Automated tools enhance multiparametric flow cytometry (MFC) data analysis for immune response studies. These computational methods overcome manual gating limitations, improving cell population characterization in complex datasets.

Area of Science:

  • Immunology
  • Computational Biology
  • Biotechnology

Background:

  • Multiparametric flow cytometry (MFC) enables detailed analysis of immune cell phenotypes and functions.
  • Analyzing high-dimensional MFC data manually is challenging due to the vast number of parameters.
  • Automated tools are increasingly developed for MFC data analysis, particularly for vaccination studies.

Purpose of the Study:

  • To provide an overview of automated analysis pipelines for multiparametric flow cytometry data.
  • To highlight the benefits of computational tools in overcoming manual gating limitations.
  • To showcase applications of automated tools in analyzing cell populations from vaccination studies.

Main Methods:

  • Review of automated analysis pipelines, from pre-processing to population analysis.
  • Discussion of algorithms for unbiased, data-driven examination of complex datasets.
  • Presentation of case studies from vaccination research.

Main Results:

  • Automated tools offer improved data quality and unbiased analysis of complex datasets.
  • Computational approaches reduce subjectivity and operator bias inherent in manual gating.
  • Successful applications demonstrate enhanced characterization of cell populations in vaccination studies.

Conclusions:

  • Bridging the gap between algorithm developers and biomedical users is crucial for adopting automated MFC analysis.
  • Automated analysis tools significantly improve the efficiency and accuracy of characterizing immune cell populations.
  • Wider adoption of these tools will advance the understanding of immune responses, especially post-vaccination.

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