Functional genomics meta-analysis to identify gene set enrichment networks in cardiac hypertrophy

In order to take advantage of the continuously increasing number of transcriptome studies, it is important to develop strategies that integrate multiple expression datasets addressing the same biological question to allow a robust analysis. Here, we propose a meta-analysis framework that integrates enriched pathways identified through the Gene Set Enrichment Analysis (GSEA) approach and calculates for each meta-pathway an empirical p-value. Validation of our approach on benchmark datasets showed comparable or even better performance than existing methods and an increase in robustness with increasing number of integrated datasets. We then applied the meta-analysis framework to 15 functional genomics datasets of physiological and pathological cardiac hypertrophy. Within these datasets we grouped expression sets measured at time points that represent the same hallmarks of heart tissue remodeling ('aggregated time points') and performed meta-analysis on the expression sets assigned to each aggregated time point. To facilitate biological interpretation, results were visualized as gene set enrichment networks. Here, our meta-analysis framework identified well-known biological mechanisms associated with pathological cardiac hypertrophy (e.g., cardiomyocyte apoptosis, cardiac contractile dysfunction, and alteration in energy metabolism). In addition, results highlighted novel, potentially cardioprotective mechanisms in physiological cardiac hypertrophy involving the down-regulation of immune cell response, which are worth further investigation.