Enrichment Analysis of Metabolic Pathways Using P-value Perturbation

Motivation: Assessing the enrichment of biological pathways is a critical problem in systems biology, which has received much attention in recent years. However, available methods often make simplifying assumptions about correlations among components of pathways, and/or rely on appropriate choices of input parameters and sizes of gene sets. In addition, current methods often focus on analysis of gene expression data, and are not suitable for analysis of other sources of omics data, in particular data from metabolomic and proteomic studies. Results: We propose a new, easy to implement methodology for assessing the enrichment of biological pathways, which generalizes the applicability of enrichment analysis to other sources of omics data. The proposed method is based on a perturbed version of regular p-values, which result in simultaneous reduction in false positive and false negative errors, under complex correlation structures. Two choices of enrichment scores are proposed, and extensive numerical studies in simulated and real data examples from a metabolomic profiling study in Bladder cancer indicate that the new method offers significant improvements over state-of-the-art methods of enrichment analysis. Availability and Implementation: The proposed methodology is

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