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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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The paraboloid of revolution is an axially symmetric surface generated by rotating a parabola around its axis. This shape has several applications in mechanical engineering due to its advantageous structural properties, such as strength against stress concentration points and rotational symmetry.
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A hyperbola is a conic section produced when a double-napped cone is intersected by a plane at an angle steeper than the slope of the cone, such that it cuts through both nappes. This intersection yields two separate, mirror-image curves known as branches, which open away from each other along the transverse axis. The nearest points on each branch to the hyperbola’s center are termed vertices, and the distance from the center to a vertex is denoted by a. Perpendicular to the transverse...
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hubViz: A Novel Tool for Hub-centric Visualization.

Jin Hyun Nam1,2, Jonghyun Yun3, Ick Hoon Jin4

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina 29425, United States of America.

Chemometrics and Intelligent Laboratory Systems : an International Journal Sponsored by the Chemometrics Society
|August 6, 2020
PubMed
Summary

HubViz is a new tool for visualizing binary genomic data, addressing limitations of existing methods. It centers on "hub" observations, improving clarity for complex datasets like gene expression and disease relationships.

Keywords:
Bayesian modelingNetwork modelingVisualization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Visualization

Background:

  • Existing visualization algorithms like MDS, t-SNE, and UMAP are not optimized for binary data.
  • These methods do not account for the phenomenon of 'hubness' in observations, potentially distorting data representation.

Purpose of the Study:

  • To introduce hubViz, a novel tool designed for hub-centric visualization of binary genomic data.
  • To overcome the limitations of current algorithms in representing binary datasets and hubness.

Main Methods:

  • Development of the hubViz algorithm for hub-centric visualization.
  • Application of hubViz to diverse datasets: rat cocaine exposure gene expression, human peripheral blood mononuclear cell single-cell RNA-seq, and disease relationship literature mining.
  • Evaluation through simulation studies to assess performance.

Main Results:

  • hubViz effectively visualizes binary data by positioning central 'hub' observations and contrasting elements symmetrically.
  • Demonstrated utility across multiple biological datasets, including gene expression and single-cell data.
  • Simulation studies confirmed the tool's efficacy in data inspection.

Conclusions:

  • hubViz offers an improved approach for visualizing binary genomic data, enhancing interpretability.
  • The tool effectively highlights central patterns and relationships within complex datasets.
  • The hubViz R package is publicly available, facilitating broader adoption in genomic research.