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Related Experiment Video

Updated: Feb 23, 2026

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

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

Published on: January 16, 2019

Interactive visual exploration and refinement of cluster assignments.

Michael Kern^1,2, Alexander Lex³, Nils Gehlenborg⁴

¹Scientific Computing and Imaging Institute, University of Utah, 72 Sout Central Campus Drive, Salt Lake City, 84112, USA.

BMC Bioinformatics

|September 14, 2017

Summary

This summary is machine-generated.

Scientists can now better evaluate and refine data clustering with a new method that visualizes assignment quality. This approach improves the analysis of biological data, leading to more accurate genotype and phenotype differentiation.

Keywords:

Biology visualization Cluster analysis Omics data Visualization

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Last Updated: Feb 23, 2026

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

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

Published on: January 16, 2019

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

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Published on: February 10, 2017

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

Area of Science:

Bioinformatics
Data Science
Computational Biology

Background:

Biological research generates vast datasets requiring efficient analysis.
Cluster analysis and visualization aid in understanding large biological data volumes.
Existing clustering methods struggle with data ambiguity and lack comprehensive refinement tools.

Purpose of the Study:

To introduce a novel method for visualizing and refining cluster assignments.
To enable comparison and manual curation of clustering results.
To integrate clustering evaluation with source and contextual data.

Main Methods:

Development of a method to visualize cluster assignment quality.
Application to matrix data clustered using partitional, hierarchical, and fuzzy algorithms.
Integration with existing tools for exploring results in context of other data.

Main Results:

The method explicitly visualizes the quality of cluster assignments.
It allows for comparison and manual refinement of clustering results.
Enables exploration of clustering in the context of other data, such as phenotypes.

Conclusions:

The developed methods are integrated into Caleydo StratomeX.
The approach reveals ambiguities in cluster assignments.
Improved clusterings are produced, enhancing genotype and phenotype differentiation.