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Stat Methods Med Res DOI:10.1177/0962280212460441

The limitations of simple gene set enrichment analysis assuming gene independence.

Publication TypeJournal Article
Year of Publication2016
AuthorsTamayo, P, Steinhardt, G, Liberzon, A, Mesirov, JP
JournalStat Methods Med Res
Date Published2016 Feb
KeywordsBiostatistics, Databases, Genetic, Epistasis, Genetic, Gene Expression Profiling, Genome, Human, Humans, Knowledge Bases, Models, Statistical, Oligonucleotide Array Sequence Analysis, Statistics, Nonparametric

Since its first publication in 2003, the Gene Set Enrichment Analysis method, based on the Kolmogorov-Smirnov statistic, has been heavily used, modified, and also questioned. Recently a simplified approach using a one-sample t-test score to assess enrichment and ignoring gene-gene correlations was proposed by Irizarry et al. 2009 as a serious contender. The argument criticizes Gene Set Enrichment Analysis's nonparametric nature and its use of an empirical null distribution as unnecessary and hard to compute. We refute these claims by careful consideration of the assumptions of the simplified method and its results, including a comparison with Gene Set Enrichment Analysis's on a large benchmark set of 50 datasets. Our results provide strong empirical evidence that gene-gene correlations cannot be ignored due to the significant variance inflation they produced on the enrichment scores and should be taken into account when estimating gene set enrichment significance. In addition, we discuss the challenges that the complex correlation structure and multi-modality of gene sets pose more generally for gene set enrichment methods.


Alternate JournalStat Methods Med Res
PubMed ID23070592
PubMed Central IDPMC3758419
Grant ListR01 GM074024 / GM / NIGMS NIH HHS / United States
U24 CA194107 / CA / NCI NIH HHS / United States
R01-CA121941 / CA / NCI NIH HHS / United States
R01 CA109467 / CA / NCI NIH HHS / United States
P30 CA023100 / CA / NCI NIH HHS / United States
U54 HD090255 / HD / NICHD NIH HHS / United States
R01 CA121941 / CA / NCI NIH HHS / United States