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

SUBMITTER: Sohn I 

PROVIDER: S-EPMC3131260 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature