The Limitations of Simple Gene Set Enrichment Analysis Assuming Gene Independence

TL;DR

This paper critically evaluates a simplified gene set enrichment analysis method that ignores gene correlations, demonstrating that ignoring gene-gene correlations leads to inaccurate significance estimates and emphasizing the importance of accounting for gene dependencies.

Contribution

The paper provides empirical evidence that ignoring gene-gene correlations in GSEA leads to variance inflation and inaccurate significance, advocating for methods that incorporate gene dependencies.

Findings

Ignoring gene correlations inflates variance of enrichment scores

Simplified methods can produce misleading significance estimates

Gene dependencies are crucial for accurate gene set enrichment analysis

Abstract

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.