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Flow Cytometry

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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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Published on: January 16, 2019

cytoFlagR: A comprehensive framework to objectively assess high-parameter cytometry data for batch effects.

Shruti Eswar^1,2, Zachary T Koenig^3,4, Amanda R Tursi^2,5

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

Biorxiv : the Preprint Server for Biology

|June 12, 2025

Summary

This summary is machine-generated.

cytoFlagR is a new R tool that objectively identifies batch effects in high-parameter cytometry data. It flags problematic batches and markers, improving data quality control for longitudinal studies.

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Area of Science:

Immunology
Computational Biology
Data Science

Background:

High-parameter cytometry is crucial for longitudinal studies.
Technical variations across experimental batches can distort biological signals.
Objective tools for identifying batch-related issues in cytometry data are limited.

Purpose of the Study:

To introduce cytoFlagR, a novel tool for detecting and flagging batch effects in high-parameter cytometry.
To provide an objective method for quality control in cytometry data analysis.

Main Methods:

cytoFlagR utilizes robust statistical evaluations to assess batch and marker variations.
Methods include analyzing median signal intensities, positive cell frequencies, and Earth Mover's Distance (EMD).
Unsupervised clustering identifies cell-type-specific batch problems in mass and spectral cytometry data.

Main Results:

cytoFlagR effectively flags batch-related problems at marker and cell cluster levels.
The tool demonstrates utility with and without reference controls.
It objectively detects distinct types of batch issues, enhancing data reliability.

Conclusions:

cytoFlagR significantly improves quality control for high-parameter cytometry data.
Objective identification of technical variations by cytoFlagR prevents confounding of downstream analyses.
The tool is freely available as R scripts.