GAGE: generally applicable gene set enrichment for pathway analysis.

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

SUBMITTER: Luo W 

PROVIDER: S-EPMC2696452 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature