Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification

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Braden Roper, James C. Mathews, Saad Nadeem, Ji Hwan Park

 Room: 104

2023-10-25T04:54:00ZGMT-0600Change your timezone on the schedule page
2023-10-25T04:54:00Z

Exemplar figure, described by caption below — An overview of the Vis-SPLIT tool. (A) The Hierarchical Overview is an abstract view of the current clusters. (B) The Heatmap Overview displays the patterns for the expression values of genes in each cluster. (C) The Survival Analysis View visualizes survival curves for each cluster. (D) The PCA View allows users to view or split the selected node in the Hierarchical Overview, and includes (D.1) the Projection depicting individuals in 2D, placed based on genetic expression, (D.2-D.3) axis-aligned heatmaps displaying the expression values of genes, and (D.4) the Feature Loadings Plot showing current gene contributions.

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Keywords

Human-centered computing—Visualization—Visualization application domains—Visual analytics

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.