Statistical Methods and Workflow for Analyzing Human Metabolomics Data

TL;DR

This paper reviews statistical methods for analyzing large-scale human metabolomics data, proposing a step-by-step workflow to improve data interpretation and address analytical challenges in the field.

Contribution

It introduces a comprehensive, standardized framework for statistical analysis of human metabolomics data, integrating current approaches and guiding future research.

Findings

Proposes a detailed workflow for metabolomics data analysis.

Highlights key analytical challenges and potential solutions.

Emphasizes the need for standardization in the field.

Abstract

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.