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Interactive Visualization of Hierarchically Structured Data.

Kris Sankaran1, Susan Holmes2

  • 1Department of Statistics, Stanford University.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|November 13, 2018
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Summary
This summary is machine-generated.

We present new methods for visualizing tree-structured data, particularly time series, using focus-plus-context and linking principles. An R package aids analysis of bacterial evolutionary trees and other hierarchical data.

Keywords:
D3Rfocus-plus-contextlinkingtime-seriestree-structured

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Area of Science:

  • Data Visualization
  • Bioinformatics
  • Computer Science

Background:

  • Hierarchical data, such as evolutionary trees, is common in scientific research.
  • Analyzing complex tree-structured data, especially time series, presents unique visualization challenges.
  • Existing methods may not adequately address the specific questions arising from hierarchical datasets.

Purpose of the Study:

  • To introduce novel visualization methods for tree-structured data, focusing on hierarchical time series.
  • To adapt established visualization principles (focus-plus-context, linking) for tree data analysis.
  • To provide practical tools and demonstrate applications in biological and general data analysis.

Main Methods:

  • Development of visualization techniques based on focus-plus-context and linking.
  • Creation of an R package to implement and facilitate the use of these methods.
  • Application to bacterial evolutionary time series and other hierarchical datasets.

Main Results:

  • Demonstrated utility of focus-plus-context and linking for tree-structured data visualization.
  • Successful application in analyzing bacterial time series with a priori evolutionary trees.
  • Showcased adaptability for datasets where trees can be constructed from data.

Conclusions:

  • The proposed visualization methods effectively address common questions in hierarchical data analysis.
  • The R package offers a practical solution for investigating tree-structured time series.
  • These techniques enhance the interpretation and utility of complex hierarchical datasets across various domains.