Statistical Workflow for Feature Selection in Human Metabolomics Data
Year: 2019
Type: review
Abstract: High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.
Source: Metabolites
Institutions: Brigham and Women's Hospital, Harvard University, University of Florida, Cedars-Sinai Medical Center, Cedars-Sinai Smidt Heart Institute, University of California San Diego, University of Turku, Turku University Hospital, Finnish Institute for Health and Welfare, Framingham Heart Study
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,418
Funders: American Heart Association, Suomen Lääketieteen Säätiö, Emil Aaltosen Säätiö, Paavo Nurmen Säätiö, National Institutes of Health, University of California, San Diego, National Heart, Lung, and Blood Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Environmental Health Sciences
Awards: G3460339748, Mediators of Systemic Inflammation and Heart Failure Risk in the Community, Mapping the Plasma Exposome and its Association with Human Cardiovascular Disease, Reducing Inflammation for the Prevention of CVD: A Focus on Eicosanoid Pathways, Inflammatory Mediators, Cardiovascular Health, and Longevity in Women, Microbial contributions to the host metabolome, G8931882178, CHD Risk and Metabolomic Profiles of Discordant Lipids, Graduate Training in Biostatistics, Mentored Patient-Oriented Research for Preventing ASCVD Events, Circulating plasma metabolites, diet, and risk of type 2 diabetes
OpenAlex ID: W2959053591