Multiple testing for gene sets from microarray experiments

BMC Bioinformatics

Methodology Article

Insuk Sohn¹  Sin-Ho Jung²  Kouros Owzar²  Stephen L George²  Stephanie Mackey Cushman³  Johan Lim⁴ 

[1] Biostatistics and Bioinformatics Center, Samsung Cancer Research Institute, Samsung Medical Center, 137-710, Seoul, Republic of Korea;Department of Biostatistics and Bioinformatics, Duke University Medical Center, 27710, NC, USA;Department of Medicine, Division of Medical Oncology, Duke University, 27710, NC, USA;Department of Statistics, Seoul National University, 151-747, Seoul, Republic of Korea;

关键词: False Discovery Rate;    Generalize Inverse;    Prognostic Gene;    Extension Package;    False Discovery Rate Level;   

DOI  :  10.1186/1471-2105-12-209

 received in 2010-12-13, accepted in 2011-05-26,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundA 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.ResultsIn 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.ConclusionsOur 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.

【 授权许可】

Unknown   
© Sohn et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.