Gridded Visualization of Statistical Trees for High‐Dimensional Multipartite Data in Systems Genetics
Document Type
Article
Publication Date
7-2025
Original Citation
Adams JL,
Ball R,
Bubier JA,
Chesler E.
Gridded Visualization of Statistical Trees for High‐Dimensional Multipartite Data in Systems Genetics Computer Graphics Forum. 2025;44(3):e70113.
Keywords
JMG, JDS, SS1
JAX Source
Computer Graphics Forum. 2025;44(3):e70113.
ISSN
1467-8659
DOI
https://doi.org/10.1111/cgf.70113
Grant
This work was supported in part by The Jackson Laboratory, the National Science Foundation (awards #2209624 & #2209623), NIH NIDA P50 DA039841, R01 DA37927, and U01DA043809, and the Khoury College of Computer Sciences at Northeastern University. The authors also gratefully acknowledge members of the computational science service supported in part by The Jackson Laboratory Cancer Center Support Grant P30CA034196.
Abstract
In systems genetics and other multi-omics research, exploring high-dimensional relationships among molecular and physiological variables across individuals poses significant challenges. We present the Gridded Trees interface, a novel interactive visualization tool designed to facilitate the exploration of conditional inference trees, which are hierarchical models of relationships in these complex datasets. Traditional static tools struggle to reveal patterns in tree-structured data, but the Gridded Trees interface provides interactive, coordinated views, allowing users to navigate between overview and detail, filter data dynamically, and compare molecular-physiological relationships across subgroups. By combining filtering techniques, strip plots, Sankey diagrams, and small multiples, the Gridded Trees interface enhances exploratory data analysis and supports hypothesis generation. In our systems genetics research use case, this tool has revealed significant associations among microbial populations and addiction-related behavioral traits in genetically diverse mice. The Gridded Trees interface suggests broad potential for visualizing hierarchical and multipartite data across domains. A preprint of this paper as well as Supplemental Materials are available on OSF at https://osf.io/9emn5/.