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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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CytoScan: Automated Detection of Technical Anomalies for Cytometry Quality Control.

Tim R Mocking^1,2, Felix Zwolle^1,2, Yejin Park^1,2

¹Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|July 7, 2026

Summary

This summary is machine-generated.

CytoScan is a new R package for cytometry data analysis. It helps identify technical errors and anomalies in large datasets, ensuring more reliable research findings.

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Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Cytometry techniques generate large datasets with multiple parameters, posing analytical challenges.
Technical variations and lack of standardization can introduce spurious effects, complicating multi-sample analyses.
Existing tools for identifying technical effects in large cytometry datasets are limited.

Purpose of the Study:

To introduce CytoScan, a novel R package for evaluating inter-measurement variation in cytometry datasets.
To provide a user-friendly tool for detecting anomalous measurements (outliers and novelties) post-data acquisition.
To enhance the quality control of large cytometry datasets for more reliable downstream analyses.

Main Methods:

CytoScan evaluates inter-measurement variation within cytometry datasets.
It identifies two types of anomalies: outliers (dissimilar files within a dataset) and novelties (dissimilar files compared to reference data).
The package was validated using simulated skewed marker distributions and real-life technical effects.

Main Results:

CytoScan accurately detects anomalies in cytometry datasets.
The package successfully identified both outlier and novelty files in simulations and real data.
Demonstrated effectiveness on large cytometry datasets using standard hardware.

Conclusions:

CytoScan offers an accessible solution for quality control in cytometry data analysis.
The R package facilitates the detection of technical effects, improving data reliability.
CytoScan supports more robust and accurate scientific conclusions from cytometry studies.