Simulated metabolic profiles reveal biases in pathway analysis methods

Abstract

Initially developed for transcriptomics data, pathway analysis (PA) methods can introduce biases when applied to metabolomics data, especially if input parameters are not chosen with care. This is particularly true for exometabolomics data, where exported metabolites may be far from internal disruptions in the organism. Experimentally evaluating PA methods is practically impossible when the samples "true" metabolic disruption is unknown. Using in silico metabolic modelling, we simulated metabolic profiles for entire pathway knockouts, providing both a known disruption site as well as a simulated metabolic profile for use in PA methods. PA should be able to detect the known disrupted pathway among the significantly enriched pathways for that profile. Through network-level statistics, visualisation, and graph-based metrics, we show that even when a given pathway is completely blocked, it may not be detected as significantly enriched with PA. This work highlights how some metabolomics data may not be suited to typical PA methods, and serves as a benchmark for analysing, improving and developing new PA tools.