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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: May 10, 2026

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells
10:20

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

GenePattern flow cytometry suite.

Josef Spidlen1, Aaron Barsky, Karin Breuer

  • 1Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada. rbrinkman@bccrc.ca.

Source Code for Biology and Medicine
|July 5, 2013
PubMed
Summary
This summary is machine-generated.

The GenePattern Flow Cytometry Suite offers advanced analysis tools for flow cytometry data, making complex methods accessible to researchers without programming skills. This enables easier cell population identification and data normalization.

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Related Experiment Videos

Last Updated: May 10, 2026

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Published on: December 26, 2014

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Immunology

Background:

  • Traditional flow cytometry data analysis is time-consuming and relies on 2D representations of high-dimensional data.
  • Increasing data output from flow cytometry has outpaced analysis method development.
  • Advanced analysis tools are often programmatic and inaccessible to experimentalists.

Purpose of the Study:

  • To develop user-friendly flow cytometry data analysis tools for experimentalists.
  • To integrate advanced algorithms into an accessible platform.
  • To facilitate reproducible research through automated analysis pipelines.

Main Methods:

  • Development of the GenePattern Flow Cytometry Suite with 34 open-source modules.
  • Leveraging R/BioConductor functionality within GenePattern modules.
  • Utilizing a web-based interface for pipeline creation and execution.

Main Results:

  • The suite covers basic FCS file processing to advanced automated cell identification, normalization, and quality assessment.
  • Modules are integrated via the GenePattern web interface to build analytical workflows.
  • Functionality previously requiring bioinformatics expertise is now accessible.

Conclusions:

  • The GenePattern Flow Cytometry Suite democratizes advanced flow cytometry data analysis.
  • Users with minimal computational skills can now perform complex analyses via a web browser.
  • This enhances accessibility and reproducibility in flow cytometry research.