Multiple testing for gene sets from microarray experiments.

TitleMultiple testing for gene sets from microarray experiments.
Publication TypeJournal Article
Year of Publication2011
AuthorsSohn, Insuk, Kouros Owzar, Johan Lim, Stephen L. George, Stephanie Mackey Cushman, and Sin-Ho Jung
JournalBMC Bioinformatics
Date Published2011 May 26
KeywordsGene Expression Profiling, Genetic Association Studies, Humans, Lung Neoplasms, Oligonucleotide Array Sequence Analysis

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.

Alternate JournalBMC Bioinformatics
Original PublicationMultiple testing for gene sets from microarray experiments.
PubMed ID21615889
PubMed Central IDPMC3131260
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
CA142538 / CA / NCI NIH HHS / United States