Heading Down the Wrong Pathway: on the Influence of Correlation within Gene Sets

BMC Genomics

Research Article

William T Barry¹  Fred A Wright²  Daniel M Gatti³  Ivan Rusyn⁴  Andrew B Nobel⁵ 

[1] Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA;Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;Centers for Environmental Bioinformatics and Computational Toxicology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;Centers for Environmental Bioinformatics and Computational Toxicology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;Centers for Environmental Bioinformatics and Computational Toxicology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;

关键词: Gene Ontology;    False Positive Rate;    Independence Assumption;    High False Positive Rate;   

DOI  :  10.1186/1471-2164-11-574

 received in 2010-04-19, accepted in 2010-10-18,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundAnalysis of microarray experiments often involves testing for the overrepresentation of pre-defined sets of genes among lists of genes deemed individually significant. Most popular gene set testing methods assume the independence of genes within each set, an assumption that is seriously violated, as extensive correlation between genes is a well-documented phenomenon.ResultsWe conducted a meta-analysis of over 200 datasets from the Gene Expression Omnibus in order to demonstrate the practical impact of strong gene correlation patterns that are highly consistent across experiments. We show that a common independence assumption-based gene set testing procedure produces very high false positive rates when applied to data sets for which treatment groups have been randomized, and that gene sets with high internal correlation are more likely to be declared significant. A reanalysis of the same datasets using an array resampling approach properly controls false positive rates, leading to more parsimonious and high-confidence gene set findings, which should facilitate pathway-based interpretation of the microarray data.ConclusionsThese findings call into question many of the gene set testing results in the literature and argue strongly for the adoption of resampling based gene set testing criteria in the peer reviewed biomedical literature.

【 授权许可】

Unknown   
© Gatti et al; licensee BioMed Central Ltd. 2010. 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.