Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations.

ABSTRACT: The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We gene

SUBMITTER: Do KT 

PROVIDER: S-EPMC5608949 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature