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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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Flow Sheet

Flow Sheet

Flowsheets are valuable tools in nursing documentation. They enable healthcare professionals to efficiently record and monitor various patient assessments and measurements in a consolidated format.
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Graphic Sheet Documentation:

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Related Experiment Video

Updated: Apr 28, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Analysis and visualization of expression patterns with fuzzy sets as FlowSets.

Felix Offensperger¹, Markus Joppich¹, Evi Sinn¹

¹Institute of Bioinformatics, Department of Informatics, Ludwig-Maximilians-Universität München, Amalienstr. 17, 80333 München, Germany.

NAR Genomics and Bioinformatics

|April 27, 2026

Summary

This summary is machine-generated.

FlowSets is a new framework for analyzing complex biological data. It identifies fuzzy patterns and feature flows across diverse datasets, offering a flexible and interpretable alternative to traditional methods.

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Last Updated: Apr 28, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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Published on: December 12, 2019

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Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

High-dimensional biological datasets require advanced analysis techniques.
Existing methods often lack interpretability and flexibility.
Need for tools to analyze patterns across diverse and heterogeneous data.

Purpose of the Study:

Introduce FlowSets, a novel framework for identifying and analyzing fuzzy patterns (flows).
Enable flexible and interpretable analysis of complex patterns across multiple datasets.
Provide an alternative to rigid clustering and hard thresholding.

Main Methods:

Constructing data views by grouping features into fuzzy, linguistically defined categories.
Tracking feature transitions between categories across different conditions or data types.
Utilizing fuzzy categorizations to uncover structural patterns.

Main Results:

FlowSets uncovers structural patterns missed by conventional methods.
Enables visualization of feature flows and quantification of pattern memberships.
Facilitates enrichment analysis for sets with gradual memberships.

Conclusions:

FlowSets offers a robust and customizable approach for multidimensional biological data.
Provides transparent and insightful interpretation of complex biological patterns.
Enhances the analysis of heterogeneous datasets through fuzzy pattern identification.