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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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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: Oct 12, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows.

Scott White1,2,3, John Quinn4, Jennifer Enzor5,6

  • 1Duke Center for AIDS Research, Duke University, Durham, NC, United States.

Frontiers in Immunology
|November 22, 2021
PubMed
Summary
This summary is machine-generated.

FlowKit is a Python package enabling collaboration between cytometry experts by integrating traditional tools like FlowJo with advanced data science algorithms. This facilitates automated analysis workflows and enhances data interpretation.

Keywords:
FlowJoGatingMLflow cytometrypython (programming language)single cell data sciencesoftwaresystems immunology

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Computational Biology

Background:

  • Cytometry data analysis faces challenges in collaboration between domain and quantitative experts.
  • Increasing data complexity necessitates automated workflows in cytometry.
  • Traditional tools like FlowJo limit access to advanced Single Cell Data Science algorithms.

Purpose of the Study:

  • To develop a solution for seamless collaboration in cytometry data analysis.
  • To bridge the gap between domain experts and advanced computational tools.
  • To facilitate the integration of FlowJo with modern data science algorithms.

Main Methods:

  • Developed FlowKit, a Gating-ML 2.0-compliant Python package.
  • Implemented functionality to read and write FCS files and FlowJo workspaces.
  • Demonstrated workflow construction for reporting and analysis.

Main Results:

  • FlowKit enables bidirectional data transfer between FlowJo and Python environments.
  • Facilitated joint analysis by domain and quantitative experts.
  • Showcased the construction of automated analysis and reporting workflows.

Conclusions:

  • FlowKit enhances collaboration in cytometry data analysis.
  • The package integrates traditional cytometry software with advanced data science.
  • Enables domain experts to leverage a wider range of analytical tools for improved results validation and interpretation.