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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization

Deisy Morselli Gysi1,2, Andre Voigt3, Tiago de Miranda Fragoso4

  • 1Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, Haertelstrasse 16-18, Leipzig, 04109, Germany. deisy@bioinf.uni-leipzig.de.

BMC Bioinformatics
|October 26, 2018
PubMed
Summary

This study introduces an R package for weighted topological overlap (wTO) network analysis, incorporating gene expression sign and enabling consensus network construction for robust biological insights.

Keywords:
Co-expression networkCo-occurrence networkConsensus NetworkExpressionMeta analysisMetagenomicsNetworkR packageSoftwarewTO

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Network analysis

Background:

  • Network analysis is crucial for studying biological systems like gene co-expression and metabolic networks.
  • Existing tools often use absolute correlation values, neglecting the regulatory direction (positive/negative) of gene interactions.

Purpose of the Study:

  • To develop an R package for weighted topological overlap (wTO) network construction that accounts for the sign of correlations.
  • To enable the creation of consensus networks (CNs) from multiple datasets.
  • To provide integrated visualization tools for network exploration.

Main Methods:

  • Implementation of a novel R package for calculating signed wTO.
  • Inclusion of p-value calculation (raw and adjusted) for gene scores.
  • Development of a consensus network (CN) algorithm for combining independent networks.
  • Integration of a network visualization tool with interactive features.

Main Results:

  • The R package efficiently calculates signed wTO and CNs, handling large datasets (>20,000 genes) within two hours.
  • Demonstrated application on human pre-frontal cortex gene expression data and metagenomics time-series data.
  • The package allows for network analysis from time series data without replicates.

Conclusions:

  • A new R package for wTO and CN computation, including visualization, has been developed.
  • The software is available on CRAN under the GPL-2 Open Source License.
  • This tool enhances the analysis of gene regulatory networks by considering correlation directionality.