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ANALYSIS: software for graphical analysis of multidimensional flow cytometric list mode data

T C Bakker Schut1, R M Doornbos, B G de Grooth

  • 1Department of Applied Physics, University of Twente, The Netherlands.

Computers and Biomedical Research, an International Journal
|April 1, 1994
PubMed
Summary
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This study introduces a novel computer program for analyzing multidimensional flow cytometry data. The software enables detailed graphical analysis and subpopulation identification, enhancing research capabilities.

Area of Science:

  • Biotechnology
  • Computational Biology
  • Data Science

Background:

  • Multidimensional flow cytometry generates large datasets.
  • Analyzing these datasets requires specialized software for effective interpretation.
  • Existing tools may have limitations in data handling and visualization.

Purpose of the Study:

  • To describe a new computer program for graphical analysis of multidimensional flow cytometric list mode data.
  • To provide researchers with a tool for detailed data inspection and subpopulation identification.
  • To offer advanced data manipulation features for enhanced analysis.

Main Methods:

  • Development of a computer program using Turbo Pascal.
  • Implementation of one-, two-, and three-dimensional data inspection.

Related Experiment Videos

  • Inclusion of two-dimensional AND gates for subpopulation identification.
  • Features for creating new parameters via zooming, logarithmic transformation, and parameter division.
  • Compatibility with MS-DOS PCs with EGA/VGA resolution.

    • Main Results:

    • The program allows for data analysis limited only by disk space.
    • Subpopulations can be effectively identified using AND gates.
    • Time-dependent processes can be evaluated using the order of measurement as a parameter.
    • Novel parameters can be generated for more in-depth analysis.
    • The software is accessible on standard PC hardware.

    Conclusions:

    • The developed program offers a powerful and flexible solution for multidimensional flow cytometry data analysis.
    • Its features facilitate detailed subpopulation identification and the exploration of complex datasets.
    • The program enhances the capabilities of researchers working with flow cytometry data.