Multiple testing for gene sets from microarray experiments.

Published online

Journal Article

BACKGROUND: A key objective in many microarray association studies is the identification of individual genes associated with clinical outcome. It is often of additional interest to identify sets of genes, known a priori to have similar biologic function, associated with the outcome. RESULTS: In this paper, we propose a general permutation-based framework for gene set testing that controls the false discovery rate (FDR) while accounting for the dependency among the genes within and across each gene set. The application of the proposed method is demonstrated using three public microarray data sets. The performance of our proposed method is contrasted to two other existing Gene Set Enrichment Analysis (GSEA) and Gene Set Analysis (GSA) methods. CONCLUSIONS: Our simulations show that the proposed method controls the FDR at the desired level. Through simulations and case studies, we observe that our method performs better than GSEA and GSA, especially when the number of prognostic gene sets is large.

Full Text

Duke Authors

Cited Authors

  • Sohn, I; Owzar, K; Lim, J; George, SL; Mackey Cushman, S; Jung, S-H

Published Date

  • May 26, 2011

Published In

Volume / Issue

  • 12 /

Start / End Page

  • 209 -

PubMed ID

  • 21615889

Pubmed Central ID

  • 21615889

Electronic International Standard Serial Number (EISSN)

  • 1471-2105

Digital Object Identifier (DOI)

  • 10.1186/1471-2105-12-209

Language

  • eng

Conference Location

  • England