GAGE: generally applicable gene set enrichment for pathway analysis.

TitleGAGE: generally applicable gene set enrichment for pathway analysis.
Publication TypeJournal Article
Year of Publication2009
AuthorsLuo W, Friedman MS, Shedden K, Hankenson KD, Woolf PJ
JournalBMC Bioinformatics
Date Published2009 May 27
KeywordsAlgorithms, Bone Morphogenetic Protein 6, Computer Simulation, Gene Expression Profiling, Gene Regulatory Networks, Humans, Lung Neoplasms, Models, Statistical, Oligonucleotide Array Sequence Analysis, Reproducibility of Results, Sensitivity and Specificity, Signal Transduction, Software

<p><b>BACKGROUND: </b>Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.</p><p><b>RESULTS: </b>To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways-all of which are supported by the experimental literature.</p><p><b>CONCLUSION: </b>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</p>

Alternate JournalBMC Bioinformatics
PubMed ID19473525
PubMed Central IDPMC2696452
Grant ListR01 AR049682 / AR / NIAMS NIH HHS / United States
R01 AR054714 / AR / NIAMS NIH HHS / United States
R01 DE017471 / DE / NIDCR NIH HHS / United States
U54 DA021519 / DA / NIDA NIH HHS / United States
U54-DA-021519 / DA / NIDA NIH HHS / United States