We propose an interactive visual analytics tool, Vis-SPLIT, for partitioning a population of individuals into groups with similar gene signatures. Vis-SPLIT allows users to interactively explore a dataset and exploit visual separations to build a classification model for specific cancers. The visualization components reveal gene expression and correlation to assist specific partitioning decisions, while also providing overviews for the decision model and clustered genetic signatures. We demonstrate the effectiveness of our framework through a case study and evaluate its usability with domain experts. Our results show that Vis-SPLIT can classify patients based on their genetic signatures to effectively gain insights into RNA sequencing data, as compared to an existing classification system.
@article{arxiv.2309.04423,
title = {Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification},
author = {Braden Roper and James C. Mathews and Saad Nadeem and Ji Hwan Park},
journal= {arXiv preprint arXiv:2309.04423},
year = {2024}
}
Comments
To be published in IEEE Visualization and Visual Analytics (VIS), 2023