GAGE: generally applicable gene set enrichment for pathway analysis |
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| Authors: | Weijun Luo Michael S Friedman Kerby Shedden Kurt D Hankenson and Peter J Woolf |
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| Institution: | (1) Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA;(2) Bioinformatics Shared Resource, Cold Spring Harbor Laboratory, Cold Spring Harbor, New Yark, 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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| 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. |
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