Heading down the wrong pathway: on the influence of correlation within gene sets.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
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.
Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Daniel M
DM
Gatti
William T
WT
Barry
Andrew B
AB
Nobel
Ivan
I
Rusyn
Fred A
FA
Wright
D000818:Animals; D030541:Databases, Genetic; D005786:Gene Expression Regulation; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011642:Publications