article Open AccessTop 1% cited
GSVA: gene set variation analysis for microarray and RNA-Seq data
BMC Bioinformatics · 2013 · Vol. 14(1) · pp. 7–7
Sonja Hänzelmann(Hospital Del Mar)Robert Castelo(Pompeu Fabra University)Justin Guinney✉(Sage Bionetworks)
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
Citations
15,970
FWCI
16.59
field-weighted impact
References
69
Percentile
100%
vs. same field & year
Citations per year
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