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VAN: an R package for identifying biologically perturbed networks via differential variability analysis.

Vivek Jayaswal1, Sarah-Jane Schramm, Graham J Mann

  • 1School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia. jean.yang@sydney.edu.au.

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|October 26, 2013
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Summary

The Variability Analysis in Networks (VAN) R package identifies disease-associated molecular network modules by analyzing gene expression and interaction data. It streamlines complex bioinformatics workflows for network-level disease analysis.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Complex diseases involve network-level perturbations, not just individual gene changes.
  • Existing methods for gene expression analysis are unsuitable for network-level studies.
  • Bioinformatics approaches integrating transcriptomics and interaction networks are crucial for identifying perturbed networks.

Purpose of the Study:

  • To present the Variability Analysis in Networks (VAN) R package.
  • To streamline the bioinformatics analysis of molecular interaction networks and transcriptomics data.
  • To facilitate the identification of disease-associated network modules.

Main Methods:

  • VAN identifies network hubs and extracts modules (hub and interaction partners).
  • It uses functions like identifySignificantHubs and summarizeHubData to detect dysregulated modules.
  • The package includes tools for ID mapping, microRNA-gene network generation, and data visualization in R and Cytoscape.

Main Results:

  • VAN identifies network-level perturbations across biological states.
  • It can convert protein identifiers to gene identifiers and generate microRNA-gene networks.
  • The software aids in identifying cancer-associated hubs and visualizing network module changes.

Conclusions:

  • VAN offers a user-friendly platform for integrative analysis of omics data.
  • It enables the identification of disease-associated network modules.
  • The approach is relevant for understanding phenotypic changes driven by network regulation shifts.