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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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flowIO: Flow cytometry standard conformance testing, editing, and export tool.

Miroslav Koblížek1,2, Anastasia Lebedeva1, Karel Fišer1

  • 1Childhood Leukaemia Investigation Prague, 2nd Faculty of Medicine, Charles University, Prague, Czech Republic.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|August 16, 2018
PubMed
Summary

The Flow Cytometry Standard (FCS) format ensures data sharing but has implementation issues. A new R package, flowIO, tests FCS file conformance and aids data analysis, improving reproducibility.

Keywords:
FCSbioinformaticsdata standardfile formatflow cytometryreproducibility in science

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Data Science

Background:

  • The Flow Cytometry Standard (FCS) is crucial for sharing and re-analyzing Flow Cytometry (FCM) data.
  • Over time, FCS has evolved, but vendor-specific implementations can lead to suboptimal data formats.
  • These issues hinder data reproducibility, especially with advanced FCM analyses.

Purpose of the Study:

  • To address suboptimal FCS implementations and improve FCM data reproducibility.
  • To develop a tool for testing FCS file conformance to the ISAC standard.
  • To facilitate user-friendly access to FCS file validation and editing.

Main Methods:

  • Development of the 'flowIO' R package for automated FCS file conformance testing.
  • Creation of a web-based application for accessible testing and editing of FCS files.
  • Adherence to the International Society for Advancement of Cytometry (ISAC) normative standards.

Main Results:

  • The 'flowIO' R package effectively tests FCS file conformance.
  • The web application provides a user-friendly interface for validation and data manipulation.
  • The tools support advanced FCM analyses by ensuring reliable data storage.

Conclusions:

  • The 'flowIO' package and associated web application enhance the reliability of FCM data.
  • These tools help overcome challenges posed by diverse FCS implementations.
  • Improved data integrity supports robust and reproducible FCM research.