title: Multiple testing for gene sets from microarray experiments.
abstract: 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.
authors: Biostatistics and Bioinformatics Center, Samsung Cancer Research Institute, Samsung Medical Center, Seoul 137-710, Republic of Korea.
authors: Insuk
authors: I
authors: Sohn
authors: Kouros
authors: K
authors: Owzar
authors: Johan
authors: J
authors: Lim
authors: Stephen L
authors: SL
authors: George
authors: Stephanie
authors: S
authors: Mackey Cushman
authors: Sin-Ho
authors: SH
authors: Jung
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