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GSVA: gene set variation analysis for microarray and RNA-Seq data

BMC Bioinformatics · 2013 · Vol. 14(1) · pp. 7–7
Sonja HänzelmannRobert CasteloJustin Guinney

Abstract

GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

Bioinformatics and Genomic NetworksGene expression and cancer classificationRNA Research and SplicingBioconductorInterpretabilityMicroarray analysis techniquesData miningRobustness (evolution)Gene expression profilingDNA microarrayComputational biologyComputer scienceSignificance analysis of microarrays

MeSH terms

Analysis of VarianceFemaleHumansOvarian NeoplasmsSoftwareGenetic VariationLeukemia, Biphenotypic, AcuteSurvival AnalysisSequence Analysis, RNAStatistics, NonparametricOligonucleotide Array Sequence AnalysisGene Expression ProfilingPrecursor Cell Lymphoblastic Leukemia-Lymphoma

Funding

  • Instituto de Salud Carlos III
  • National Cancer Institute
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