title: GSAR: Bioconductor package for Gene Set analysis in R.
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.
authors.affiliation: Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. yrahmatallah@uams.edu.
authors.forename: Yasir
authors.identifier: http://orcid.org/0000-0002-8176-6328
authors.initials: Y
authors.lastname: Rahmatallah
authors.affiliation: Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
authors.forename: Boris
authors.initials: B
authors.lastname: Zybailov
authors.affiliation: Computational Medicine and Statistical Learning Laboratory, Tampere University of Technology, Korkeakoulunkatu 1, Tampere, FI-33720, Finland.
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
authors.affiliation: Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
authors.forename: Galina
authors.initials: G
authors.lastname: Glazko
mesh_terms: D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D008962:Models, Theoretical; D015999:Multivariate Analysis; D010641:Phenotype; D017423:Sequence Analysis, RNA; D012984:Software; D016159:Tumor Suppressor Protein p53 / Q000235:genetics / Q000378:metabolism
chemical_list: C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53
keywords: Gene set analysis; Kolmogorov-Smirnov; Minimum spanning tree; Non-parametric; Pathways; Wald Wolfowitz