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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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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Current trends in flow cytometry automated data analysis software.

Melissa Cheung1, Jonathan J Campbell2, Liam Whitby3

  • 1Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|February 19, 2021
PubMed
Summary
This summary is machine-generated.

Automated flow cytometry (FC) data analysis tools face low adoption despite potential. Literature and surveys show varied usage, with clinical settings preferring targeted analysis and academia favoring exploration tools.

Keywords:
automationcell therapydata analysisflow cytometrygatingsoftware

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Area of Science:

  • Computational Biology
  • Biotechnology
  • Clinical Diagnostics

Background:

  • Automated flow cytometry (FC) data analysis tools are vital for cell identification and characterization in various labs.
  • Despite development to improve reproducibility and efficiency over manual gating, tool adoption remains low.
  • This study surveys computational tools for automated FC analysis and their real-world usage trends.

Purpose of the Study:

  • To conduct a comprehensive literature survey of state-of-the-art computational tools for automated FC data analysis.
  • To identify popular tools based on literature citation counts and analyze usage trends in clinical laboratories.
  • To understand factors influencing the adoption and selection of automated FC analysis software.

Main Methods:

  • Literature survey of computational tools for automated FC data analysis, ranking by citation counts.
  • Analysis of dimensionality reduction methods (e.g., t-SNE, viSNE) and clustering algorithms (e.g., K-Means, hierarchical).
  • Survey of UK NEQAS Leucocyte Immunophenotyping participants on software usage, adoption rates, and key decision factors.

Main Results:

  • Dimensionality reduction methods (t-SNE, viSNE) and GUI-based software (PhenoGraph, SPADE1, FlowSOM, Citrus) are popular in literature.
  • Unsupervised learning methods are more prevalent than supervised methods in published tools.
  • 53% of surveyed clinical labs have not adopted automated methods; Infinicyt is the most used among adopters.

Conclusions:

  • Adoption of automated FC analysis tools varies, with 53% of clinical labs not yet utilizing them.
  • Data output quality, speed, and technical support are key factors for automated FC software adoption.
  • Academic institutions favor discovery and visualization tools, while clinical settings prefer supervised learning for targeted analysis.