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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
Published on: January 16, 2019
A scalable, multi-resolution consensus clustering approach for prioritizing robust signals from high-throughput
Shaine Chenxin Bao1, Kathleen I Pishas2, Karla J Cowley3
1Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, Brisbane, QLD, 4072, Australia.
Briefings in Bioinformatics
|May 24, 2026
Summary
Untangled, an unsupervised clustering tool, helps analyze complex biological data by finding stable, meaningful patterns. It improves the discovery of biological mechanisms and phenotypic responses from high-dimensional screening datasets.
Area of Science:
- Computational biology
- Bioinformatics
- Data science
Background:
- Modern biology generates high-dimensional datasets from large-scale screening.
- Analyzing these complex datasets is challenging due to hierarchical structures, unknown group numbers, and high-dimensional noise.
Purpose of the Study:
- To present Untangled, an unsupervised consensus clustering tool.
- To address challenges in analyzing high-dimensional biological screening data.
Main Methods:
- Untangled aggregates clustering solutions across granularities.
- It constructs a stability-based representation.
- Cluster number optimization and robustness evaluation are performed.
Main Results:
- Untangled reliably recovers underlying biological relationships.
- It resolves stable, meaningful substructure within datasets.
- It effectively prioritizes robust clusters with shared biological mechanisms and conserved phenotypic responses.
Conclusions:
- Untangled is a scalable framework for cluster discovery.
- It guides efficient follow-up investigations from high-dimensional biological datasets.
- It outperforms alternative clustering approaches in benchmarking studies.
