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Updated: Sep 11, 2025

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

scGHSOM: A Hierarchical Framework for Single-Cell Data Clustering and Visualization.

Shang-Jung Wen, Jia-Ming Chang, David Jing-Wei Chen

IEEE Transactions on Computational Biology and Bioinformatics

|August 14, 2025

Summary

This summary is machine-generated.

scGHSOM offers advanced hierarchical clustering and visualization for complex single-cell data. This framework effectively identifies key biological features and improves data interpretation for Mass Cytometry and RNA sequencing.

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Area of Science:

Computational biology
Bioinformatics
Data science

Background:

High-dimensional single-cell data exhibit complexity and heterogeneity, challenging biological pattern discovery.
Existing methods struggle to effectively cluster and visualize intricate cellular states.

Purpose of the Study:

To introduce scGHSOM, an enhanced Growing Hierarchical Self-Organizing Map (GHSOM) framework.
To enable robust hierarchical clustering and visualization of high-dimensional single-cell datasets.
To develop novel algorithms for identifying significant biological attributes and enhancing data interpretability.

Main Methods:

scGHSOM employs hierarchical data organization with adaptive cluster expansion based on variation thresholds.
A Significant Attributes Identification algorithm is integrated to pinpoint features minimizing intra-cluster and maximizing inter-cluster variation.
Two visualization tools, Cluster Feature Map and Cluster Distribution Map, are introduced for enhanced interpretability.

Main Results:

scGHSOM demonstrates compatibility with state-of-the-art methods in performance evaluations.
The framework achieved the best Calinski-Harabasz (CH) index on two out of three Mass Cytometry by Time-Of-Flight (CyTOF) datasets.
Visualization tools significantly improve the clarity and efficiency of interpreting clustering patterns and biological features.

Conclusions:

scGHSOM provides an effective solution for hierarchical clustering and visualization of complex single-cell data.
The integrated attribute identification and visualization tools enhance biological insight discovery.
scGHSOM offers a valuable, freely available resource for the scientific community.