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Open Access Highly Accessed Research article

GAGE: generally applicable gene set enrichment for pathway analysis

Weijun Luo12, Michael S Friedman3, Kerby Shedden4, Kurt D Hankenson5 and Peter J Woolf167*

Author Affiliations

1 Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA

2 Bioinformatics Shared Resource, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA

3 Thermogenesis Corporation, Rancho Cordova CA, 95742, USA

4 Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA

5 Department of Animal Biology, University of Pennsylvania, Philadelphia, PA 19104, USA

6 Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA

7 Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA

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BMC Bioinformatics 2009, 10:161  doi:10.1186/1471-2105-10-161

Published: 27 May 2009

Abstract

Background

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.

Results

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GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies.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 For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT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.

Conclusion

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 webcite.