ArticleBMC bioinformatics2011
Multiple testing for gene sets from microarray experiments.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The limitations of simple gene set enrichment analysis assuming gene independence.Statistical methods in medical research · 2016Article
- Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes.BMC systems biology · 2012Article
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Authors and funding
6 authors.
Funding
Abstract
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.
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