GAGE: generally applicable gene set enrichment for pathway analysis

Luo, W., Friedman, M. S., Shedden, K., Hankenson, K. D., Woolf, P. J. (May 2009) GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics, 10. p. 161.

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URL: http://www.ncbi.nlm.nih.gov/pubmed/19473525
DOI: 10.1186/1471-2105-10-161

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mechanisms inferred.GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies.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 For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT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

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > analysis and processing
bioinformatics > genomics and proteomics > annotation
bioinformatics
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification
bioinformatics > genomics and proteomics > annotation > gene expression profiling annotation
bioinformatics > genomics and proteomics > genetics & nucleic acid processing
bioinformatics > genomics and proteomics
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function
CSHL Authors:
Communities: CSHL labs > Lowe lab
Depositing User: Matt Covey
Date: 27 May 2009
Date Deposited: 21 Feb 2013 14:24
Last Modified: 21 Feb 2013 14:24
PMCID: PMC2696452
Related URLs:
URI: http://repository.cshl.edu/id/eprint/27382

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