Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification

TL;DR

Vis-SPLIT is an interactive visual analytics tool that enables users to explore and classify individuals based on gene signatures in RNA sequencing data, aiding cancer research.

Contribution

The paper introduces Vis-SPLIT, a novel interactive visualization framework for hierarchical modeling and classification of mRNA expression data.

Findings

Effective classification of patients based on genetic signatures

Enhanced insights into RNA sequencing data through visualization

Positive usability feedback from domain experts

Abstract

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.