Heading Down the Wrong Pathway: on the Influence of Correlation within Gene Sets
1 Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
2 Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
3 Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
4 Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
5 Centers for Environmental Bioinformatics and Computational Toxicology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
BMC Genomics 2010, 11:574 doi:10.1186/1471-2164-11-574Published: 18 October 2010
Analysis 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.
We 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.
These 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.