GSAR: Bioconductor package for Gene Set analysis in R

Yasir Rahmatallah, Boris Zybailov, Frank Emmert-Streib, Galina Glazko

    Research output: Contribution to journalArticleScientificpeer-review

    15 Citations (Scopus)
    76 Downloads (Pure)

    Abstract

    Background: Gene set analysis (in a form of functionally related genes or pathways) has become the method of choice for analyzing omics data in general and gene expression data in particular. There are many statistical methods that either summarize gene-level statistics for a gene set or apply a multivariate statistic that accounts for intergene correlations. Most available methods detect complex departures from the null hypothesis but lack the ability to identify the specific alternative hypothesis that rejects the null. Results: GSAR (Gene Set Analysis in R) is an open-source R/Bioconductor software package for gene set analysis (GSA). It implements self-contained multivariate non-parametric statistical methods testing a complex null hypothesis against specific alternatives, such as differences in mean (shift), variance (scale), or net correlation structure. The package also provides a graphical visualization tool, based on the union of two minimum spanning trees, for correlation networks to examine the change in the correlation structures of a gene set between two conditions and highlight influential genes (hubs). Conclusions: Package GSAR provides a set of multivariate non-parametric statistical methods that test a complex null hypothesis against specific alternatives. The methods in package GSAR are applicable to any type of omics data that can be represented in a matrix format. The package, with detailed instructions and examples, is freely available under the GPL (> = 2) license from the Bioconductor web site.

    Original languageEnglish
    Article number61
    JournalBMC Bioinformatics
    Volume18
    Issue number1
    DOIs
    Publication statusPublished - 24 Jan 2017
    Publication typeA1 Journal article-refereed

    Keywords

    • Gene set analysis
    • Kolmogorov-Smirnov
    • Minimum spanning tree
    • Non-parametric
    • Pathways
    • Wald Wolfowitz

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Structural Biology
    • Biochemistry
    • Molecular Biology
    • Computer Science Applications
    • Applied Mathematics

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