  Since its first publication in 2003, the Gene Set Enrichment Analysis (GSEA)
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 GSEA'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 GSEA'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.
