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Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification

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

TL;DR

The 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.

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.

Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification

TL;DR

The 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.

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.
Paper Structure (14 sections, 2 equations, 2 figures)

This paper contains 14 sections, 2 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Design Requirements
  4. Framework
  5. Hierarchical Overview
  6. Heatmap Overview
  7. Survival Analysis View
  8. PCA View
  9. Projection
  10. Heatmaps
  11. Feature Loadings
  12. Case Study
  13. Expert Feedback
  14. Conclusion

Figures (2)

  • Figure 1: An example of (A) identifying a gene partition based on (B) selected features in the Feature Loadings plot.
  • Figure 2: A comparison of classification from the resulting Heatmap Overview of the PAM50 (top) and Vis-SPLIT (bottom), and (A) some regions where the difference is visible.