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cytoFlagR: 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, OH, USA.
Biorxiv : the Preprint Server for Biology
|June 12, 2025
Summary
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.
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.
