GSAR: Bioconductor package for Gene Set analysis in R

BMC Bioinformatics. 2017 Jan 24;18(1):61. doi: 10.1186/s12859-017-1482-6.


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.

Keywords: Gene set analysis; Kolmogorov-Smirnov; Minimum spanning tree; Non-parametric; Pathways; Wald Wolfowitz.

MeSH terms

  • Cell Line, Tumor
  • Computational Biology / methods*
  • Databases, Genetic*
  • Gene Expression
  • Gene Expression Profiling*
  • Humans
  • Models, Theoretical
  • Multivariate Analysis
  • Phenotype
  • Sequence Analysis, RNA
  • Software*
  • Tumor Suppressor Protein p53 / genetics
  • Tumor Suppressor Protein p53 / metabolism


  • TP53 protein, human
  • Tumor Suppressor Protein p53