Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data.

ABSTRACT: Emerging technologies now allow for mass spectrometry-based profiling of thousands of small molecule metabolites ('metabolomics') in an increasing number of biosamples. While offering great promise for insight into the pathogenesis of human disease, standard approaches have not yet been established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes, including disease outcomes. To determine optimal approaches for analysis, we formally compare traditional and newer statistical learning methods across a range of metabolomics dataset types. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observe that with an increasing number of study subjects, univariate compared to

SUBMITTER: Henglin M 

PROVIDER: S-EPMC9227835 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature