Publication Type
Journal Article
Version
acceptedVersion
Publication Date
2-2026
Abstract
Cell state discovery is crucial for understanding biological systems and enhancing medical outcomes. A key aspect of this process is identifying distinct biomarkers that define specific cell states. However, difficulties arise from the co-discovery process of cell states and biomarkers: biologists often use dimensionality reduction to visualize cells in a two-dimensional space. Then they usually interpret visually clustered cells as distinct states, from which they seek to identify unique biomarkers. However, this assumption is often this assumption often fails to hold due to internal inconsistencies in a cluster, making the process trial-and-error and highly uncertain. Therefore, biologists urgently need effective tools to help uncover the hidden association relationships between different cell populations and their potential biomarkers. To address this problem, we first designed a machine-learning algorithm based on the Mixture-of-Experts (MoE) technique to identify meaningful associations between cell populations and biomarkers. We further developed a visual analytics system-CellScout-in collaboration with biologists, to help them explore and refine these association relationships to advance cell state discovery. We validated our system through expert interviews, from which we further selected a representative case to demonstrate its effectiveness in discovering new cell states.
Keywords
Biomarkers, Biology, Association rule learning, Visual analytics, Machine learning, Gene expression, Data visualization, Collaboration, Biological system modeling, Bioinformatics, Biomedical, cell state discovery, gene expression data, machine learning, human-AI collaboration
Discipline
Cell and Developmental Biology | Databases and Information Systems | Graphics and Human Computer Interfaces
Publication
IEEE Transactions on Visualization and Computer Graphics
Volume
32
Issue
2
First Page
1497
Last Page
1512
ISSN
1077-2626
Identifier
10.1109/TVCG.2025.3636102
Publisher
Institute of Electrical and Electronics Engineers
Citation
SHENG, Rui; ZANG, Zelin; WANG, Jiachen; LUO, Yan; CHEN, Zixin; ZHOU, Yan; RUAN, Shaolun; and QU, Huamin.
CellScout: Visual analytics for mining biomarkers in cell state discovery. (2026). IEEE Transactions on Visualization and Computer Graphics. 32, (2), 1497-1512.
Available at: https://ink.library.smu.edu.sg/sis_research/11052
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TVCG.2025.3636102
Included in
Cell and Developmental Biology Commons, Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons