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GAGE: generally applicable gene set enrichment for pathway analysis

BMC Bioinformatics · 2009 · Vol. 10(1) · pp. 161–161

Weijun Luo✉(Cold Spring Harbor Laboratory)Michael S. Friedman(ThermoGenesis (United States))Kerby Shedden(University of Michigan–Ann Arbor)Kurt D. Hankenson(University of Pennsylvania)Peter Woolf(University of Michigan–Ann Arbor)

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Abstract

GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php.

Bioinformatics and Genomic Networks Gene expression and cancer classification RNA Research and SplicingComputational biologyDNA microarrayBiologyGene expression profilingWnt signaling pathwayComputer scienceOutlierRobustness (evolution)BioinformaticsBiological pathway

MeSH terms

AlgorithmsComputer SimulationHumansLung NeoplasmsSensitivity and SpecificitySoftwareReproducibility of ResultsModels, StatisticalSignal TransductionOligonucleotide Array Sequence AnalysisGene Expression ProfilingGene Regulatory NetworksBone Morphogenetic Protein 6