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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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Related Experiment Video

Updated: Jun 17, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Automatic clustering of flow cytometry data with density-based merging.

Guenther Walther1, Noah Zimmerman, Wayne Moore

  • 1Department of Statistics, Stanford University, Stanford, CA 94305, USA.

Advances in Bioinformatics
|January 14, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces automated software for analyzing flow cytometry data, overcoming limitations in manual cell population identification. The new nonparametric method accurately identifies cell subsets, advancing clinical and laboratory research.

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Related Experiment Videos

Last Updated: Jun 17, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

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

Published on: January 16, 2019

Area of Science:

  • Biotechnology
  • Computational Biology
  • Immunology

Background:

  • Flow cytometry is essential for single-cell analysis in clinical and research settings.
  • Manual data analysis, particularly gating, is a bottleneck limiting flow cytometry's potential.
  • Current automated methods struggle with complex, non-convex cell populations.

Purpose of the Study:

  • To develop and present automated methodology and software for cell population identification in flow cytometry data.
  • To move beyond manual, sequential gating towards statistically-driven, automated gating.
  • To provide a nonparametric approach capable of identifying complex subpopulations.

Main Methods:

  • Developed a novel nonparametric statistical approach for automated data analysis.
  • Created software to implement the automated gating methodology.
  • Applied the method to analyze flow cytometry data from mouse spleen and peritoneal cavity cells.

Main Results:

  • Successfully automated the identification of cell populations in flow cytometry datasets.
  • The nonparametric method accurately reproduced known cell subpopulations, including non-convex ones.
  • Demonstrated the software's capability on a relevant biological sample.

Conclusions:

  • Automated analysis of flow cytometry data is feasible and improves upon manual gating.
  • The developed nonparametric methodology offers a robust solution for identifying complex cell populations.
  • This advancement has significant implications for clinical diagnostics and biological research using flow cytometry.