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CorDiffViz: an R package for visualizing multi-omics differential correlation networks.

Shiqing Yu1, Mathias Drton2, Daniel E L Promislow3

  • 1Department of Statistics, University of Washington, NE Stevens Way, Seattle, WA, 98195, USA. syu.phd@gmail.com.

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|October 10, 2021
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Summary

This study introduces CorDiffViz, an R package for visualizing differential correlation networks. It helps researchers understand biomolecular interaction changes across conditions, aiding disease mechanism discovery.

Keywords:
Correlation networksData integrationDifferential correlationsOmicsUndirected graphsVisualization

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Differential correlation networks are vital for understanding biomolecular interaction changes.
  • These networks help elucidate disease mechanisms and progression.
  • Analyzing omics data under different conditions is crucial in biological research.

Purpose of the Study:

  • To present CorDiffViz, a novel R package for estimating and visualizing differential correlation networks.
  • To provide a flexible tool for analyzing changes in biomolecular interactions.

Main Methods:

  • The CorDiffViz package is implemented in R, HTML, and Javascript.
  • It supports multiple correlation measures and inference methods.
  • Interactive visualization is a key feature, tested on Chrome and Firefox.

Main Results:

  • CorDiffViz facilitates the estimation and visualization of differential correlation networks.
  • The package allows users to interact with visualizations and select different methods.
  • It enables easy comparison of correlation networks between two conditions.

Conclusions:

  • CorDiffViz offers flexibility in analyzing differential correlation networks.
  • The software supports integrative analysis of cross-correlation networks between omics datasets.
  • It provides a user-friendly interface for exploring complex biological interactions.