Supplementary data for "GSAR: Bioconductor package for Gene Set analysis in R"

  • Yasir Rahmatallah (Creator)
  • Boris Zybailov (Creator)
  • Frank Emmert-Streib (Creator)
  • Galina V. Glazko (Creator)

    Tietoaineisto

    Description

    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.
    Supplementary data includes additional document presenting computational considerations and uniqueness of package GSAR.
    Koska saatavilla2017
    JulkaisijaTampere University of Technology

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