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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Updated: Jun 21, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Scalable analysis of flow cytometry data using R/Bioconductor.

David J Klinke1, Kathleen M Brundage

  • 1Department of Chemical Engineering, West Virginia University, Morgantown, West Virginia 25606, USA. david.klinke@mail.wvu.edu

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|July 8, 2009
PubMed
Summary
This summary is machine-generated.

This study presents an R/Bioconductor tutorial for analyzing complex flow cytometry data. It simplifies multidimensional data analysis, making advanced techniques accessible to more researchers.

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Characterization of Aquatic Biofilms with Flow Cytometry
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Characterization of Aquatic Biofilms with Flow Cytometry

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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Published on: January 16, 2019

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Published on: June 6, 2018

Area of Science:

  • Immunology
  • Computational Biology
  • Data Science

Background:

  • Flow cytometry is a crucial tool for analyzing cell populations at single-cell resolution.
  • Analyzing complex, multidimensional flow cytometry data presents a significant challenge for researchers.
  • Existing open-source platforms like R/Bioconductor offer powerful tools for data analysis.

Purpose of the Study:

  • To provide a tutorial for analyzing flow cytometry data using R/Bioconductor.
  • To demonstrate the application of R/Bioconductor for dissecting complex cellular behavior.
  • To facilitate wider adoption of R/Bioconductor for polychromatic flow cytometry analysis.

Main Methods:

  • Utilized R/Bioconductor, an open-source platform, for graphical and data analysis.
  • Applied a common workflow for analyzing flow cytometry data.
  • Included advanced analyses such as density function estimation and principal component analysis.

Main Results:

  • Successfully analyzed a dataset of CD4(+)CD62L(+) T cells from Balb/c splenocytes.
  • Demonstrated a clear workflow for flow cytometry data analysis in R/Bioconductor.
  • Illustrated the utility of density function estimation and principal component analysis for complex datasets.

Conclusions:

  • R/Bioconductor provides a robust and accessible platform for flow cytometry data analysis.
  • The presented tutorial simplifies complex data analysis, aiding researchers in exploring their own data.
  • This work aims to lower the barrier for using advanced analytical methods in flow cytometry research.