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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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Updated: May 24, 2025

Standardization of a Cytometric Bead Assay Based on Egg-Yolk Antibodies

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Visual Quality Control With CytoMDS , a Bioconductor Package for Low Dimensional Representation of Cytometry Sample

Philippe Hauchamps¹, Simon Delandre², Stéphane T Temmerman²

¹Computational Biology and Bioinformatics, de Duve Institute UCLouvain, Woluwe-Saint-Lambert, Belgium.

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

|March 4, 2025

Summary

This summary is machine-generated.

CytoMDS is a new Bioconductor package for cytometry data quality control. It helps identify batch effects and outlying samples by visualizing all study samples in a low-dimensional space.

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Area of Science:

Computational Biology
Bioinformatics
Data Science

Background:

Quality control (QC) is crucial for accurate cytometry data analysis.
Identifying batch effects and outlying samples is challenging but essential to avoid misinterpreting technical variations as biological signals.
Manual QC for large cytometry datasets is time-consuming and complex.

Purpose of the Study:

To introduce CytoMDS, a Bioconductor package for robust cytometry sample QC.
To provide a method for low-dimensional representation of cytometry samples for visual interpretation.
To facilitate the identification of batch effects, sample outliers, and biological signals.

Main Methods:

CytoMDS utilizes Earth Mover's Distance to quantify dissimilarities between high-dimensional single-cell marker expression distributions.
Multi-Dimensional Scaling (MDS) is employed for projecting these dissimilarities into a low-dimensional space, representing each sample as a single point.
The package includes visualization tools for assessing projection quality and interpreting sample coordinates.

Main Results:

CytoMDS effectively represents global sample relationships in cytometry studies.
Application to three biological datasets successfully identified low-quality samples and batch effects.
The method distinguished between technical variations and genuine biological signals between sample groups.

Conclusions:

CytoMDS offers an efficient and interpretable solution for cytometry data quality control.
The package aids researchers in ensuring data integrity and reliable downstream analysis.
CytoMDS is a valuable tool for managing and analyzing complex, large-scale cytometry datasets.