  High-throughput metabolomics investigations, when conducted in large human
cohorts, represent a potentially powerful tool for elucidating the biochemical
diversity and mechanisms underlying human health and disease. Large-scale
metabolomics data, generated using targeted or nontargeted platforms, are
increasingly more common. Appropriate statistical analysis of these complex
high-dimensional data is critical for extracting meaningful results from such
large-scale human metabolomics studies. Herein, we consider the main
statistical analytical approaches that have been employed in human metabolomics
studies. Based on the lessons learned and collective experience to date in the
field, we propose a step-by-step framework for pursuing statistical analyses of
human metabolomics data. We discuss the range of options and potential
approaches that may be employed at each stage of data management, analysis, and
interpretation, and offer guidance on analytical considerations that are
important for implementing an analysis workflow. Certain pervasive analytical
challenges facing human metabolomics warrant ongoing research. Addressing these
challenges will allow for more standardization in the field and lead to
analytical advances in metabolomics investigations with the potential to
elucidate novel mechanisms underlying human health and disease.
