|Title||Multiple testing for gene sets from microarray experiments.|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Sohn, Insuk, Kouros Owzar, Johan Lim, Stephen L. George, Stephanie Mackey Cushman, and Sin-Ho Jung|
|Date Published||2011 May 26|
|Keywords||Gene 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 Journal||BMC Bioinformatics|
|Original Publication||Multiple testing for gene sets from microarray experiments.|
|PubMed Central ID||PMC3131260|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
CA142538 / CA / NCI NIH HHS / United States
Multiple testing for gene sets from microarray experiments.