  Background. Emerging technologies now allow for mass spectrometry based
profiling of up to thousands of small molecule metabolites (metabolomics) in an
increasing number of biosamples. While offering great promise for revealing
insight into the pathogenesis of human disease, standard approaches have yet to
be established for statistically analyzing increasingly complex,
high-dimensional human metabolomics data in relation to clinical phenotypes
including disease outcomes. To determine optimal statistical approaches for
metabolomics analysis, we sought to formally compare traditional statistical as
well as newer statistical learning methods across a range of metabolomics
dataset types. Results. In simulated and experimental metabolomics data derived
from large population-based human cohorts, we observed that with an increasing
number of study subjects, univariate compared to multivariate methods resulted
in a higher false discovery rate due to substantial correlations among
metabolites. In scenarios wherein the number of assayed metabolites increases,
as in the application of nontargeted versus targeted metabolomics measures,
multivariate methods performed especially favorably across a range of
statistical operating characteristics. In nontargeted metabolomics datasets
that included thousands of metabolite measures, sparse multivariate models
demonstrated greater selectivity and lower potential for spurious
relationships. Conclusion. When the number of metabolites was similar to or
exceeded the number of study subjects, as is common with nontargeted
metabolomics analysis of relatively small sized cohorts, sparse multivariate
models exhibited the most robust statistical power with more consistent
results. These findings have important implications for the analysis of
metabolomics studies of human disease.
