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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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CytoBatchFlagR: A Comprehensive Framework to Objectively Assess High-Parameter Cytometry Data for Batch Effects.

Shruti Eswar1,2, Zachary T Koenig3,4, Amanda R Tursi2,5

  • 1Department of Pharmacology, Physiology & Neurobiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|April 12, 2026
PubMed
Summary
This summary is machine-generated.

CytoBatchFlagR objectively identifies batch and marker issues in high-parameter cytometry data. This R package improves quality control for mass and flow cytometry, aiding downstream analysis decisions.

Keywords:
batch effectsbioinformaticsflow cytometryhigh‐parameterimmunologymass cytometryquality controlsingle‐cell

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Area of Science:

  • Immunology
  • Computational Biology
  • Data Science

Background:

  • High-parameter cytometry generates complex single-cell data, crucial for immunology.
  • Technical variations across experimental batches can distort biological signals in longitudinal studies.
  • Existing tools for identifying batch effects and problematic markers are limited.

Purpose of the Study:

  • Introduce CytoBatchFlagR, a novel R package for flagging batch-related issues in cytometry data.
  • Provide an objective and interpretable method for quality control in high-dimensional single-cell analysis.
  • Enhance the reliability of downstream analyses by identifying technical variations.

Main Methods:

  • CytoBatchFlagR employs statistical evaluations, including median signal intensities and Earth Mover's Distance (EMD).
  • It assesses variations at both marker and cell cluster levels, utilizing unsupervised clustering for cell type-specific issues.
  • The tool is applicable to both mass and flow cytometry datasets, with or without reference controls.

Main Results:

  • CytoBatchFlagR successfully identified distinct types of batch problems in three diverse cytometry datasets.
  • The tool demonstrated effectiveness in datasets with and without reference controls.
  • Results are presented through interpretable visualizations, facilitating informed decision-making.

Conclusions:

  • CytoBatchFlagR significantly improves quality control for high-parameter cytometry data.
  • It enables objective identification of technical variations, allowing users to decide on batch correction or data exclusion.
  • The freely available R package, with documentation and a tutorial, supports researchers in managing batch effects.