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Flow cytometry data analysis: Recent tools and algorithms.

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This summary is machine-generated.

Flow cytometry (FCM) data analysis is complex due to high-dimensional data. This review highlights R-based bioinformatic tools to streamline the process for researchers.

Keywords:
automated gatingbioinformaticsclusteringdata analysisflow cytometry

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Computational Biology

Background:

  • Flow cytometry (FCM) enables high-throughput quantification of cellular characteristics.
  • Modern FCM instruments generate complex, high-dimensional datasets.
  • Analyzing FCM data is a significant bottleneck in its application.

Purpose of the Study:

  • To review the primary stages of flow cytometry data analysis.
  • To focus on contemporary bioinformatic tools within the R programming environment.
  • To provide a guide to available R packages for FCM data analysis.

Main Methods:

  • Literature review of bioinformatic tools for FCM data analysis.
  • Focus on R-based software packages and libraries.
  • Categorization of tools based on data analysis stages.

Main Results:

  • Identification of key R packages for various FCM data analysis steps.
  • Description of the functionality of selected bioinformatic tools.
  • Summary of the current landscape of computational approaches for FCM data.

Conclusions:

  • Advanced computational algorithms are essential for high-parameter FCM data.
  • R programming environment offers a robust ecosystem for FCM data analysis.
  • This review serves as a practical resource for researchers utilizing FCM.