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GSAR: Bioconductor package for Gene Set analysis in R.

Yasir Rahmatallah1, Boris Zybailov2, Frank Emmert-Streib3

  • 1Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. yrahmatallah@uams.edu.

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Summary

The GSAR R package offers novel multivariate statistical methods for gene set analysis, identifying specific biological changes beyond just detecting differences. It aids in understanding omics data by testing shifts in mean, variance, or correlation structure.

Keywords:
Gene set analysisKolmogorov-SmirnovMinimum spanning treeNon-parametricPathwaysWald Wolfowitz

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene set analysis is crucial for interpreting omics data, particularly gene expression.
  • Existing methods often detect general deviations but struggle to pinpoint specific biological changes.
  • A need exists for tools that can identify the nature of the alternative hypothesis being tested.

Purpose of the Study:

  • To introduce GSAR (Gene Set Analysis in R), an open-source R/Bioconductor package.
  • To implement multivariate non-parametric statistical methods for gene set analysis.
  • To provide tools for visualizing changes in gene correlation networks.

Main Methods:

  • Utilizes self-contained multivariate non-parametric statistical methods.
  • Tests complex null hypotheses against specific alternatives (shift, scale, net correlation structure).
  • Employs a novel visualization tool based on minimum spanning trees for correlation networks.

Main Results:

  • GSAR enables testing for differences in mean, variance, and net correlation structure within gene sets.
  • The package facilitates the examination of changes in correlation structures between conditions.
  • Identifies influential genes (hubs) within correlation networks.

Conclusions:

  • GSAR offers a robust framework for gene set analysis applicable to various omics data types.
  • The package provides specific statistical tests for biological hypotheses.
  • GSAR is freely available with comprehensive documentation and examples from Bioconductor.