Statistical workflow for feature selection in human metabolomics data
Open accessAbstract
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.
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Some omics types are more likely to produce sparse data than others.
“Also, some omics are more prone to generating sparse data (e.g. in the case of metabolomics, some values might be below the limit of detection and hence assigned null value (Antonelli et al., 2019)) than others.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Antonelli et al., 2019
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- Authors
- Antonelli, J., Claggett, B. L., Henglin, M., Kim, A., Ovsak, G., Kim, N., Deng, K., Rao, K., Tyagi, O., Watrous, J. D., Lagerborg, K. A., Hushcha, P. V., Demler, O. V., Mora, S., Niiranen, T. J., Pereira, A. C., Jain, M., & Cheng, S.
- Affiliations
- Brigham and Women's Hospital (US)
- Cedars-Sinai Medical Center (US)
- University of California San Diego (US)
- University of Turku (FI)
- Harvard University (US)
- Corresponding author
- Jain, Mohit
- Cheng, Susan
- ORCID
- Venue
- Metabolites
- Publisher
- MDPI AG
- Published
- 2019-07-12
- Volume
- 9
- Issue
- 7
- Pages
- 143
- ISSN
- 2218-1989 (ISSN-L)
- PMC
- PMC6680705
- OpenAlex
- W2959053591
- Cited by
- 88
- References
- 64
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 10 · 2023: 19 · 2022: 13 · 2021: 11 · 2020: 11 · 2019: 3
- FWCI
- 0.84
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Metabolites (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UTUPub (University of Turku) (submittedVersion)
- UTUPub (University of Turku) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- STM:n Hallinnonalan avoin julkaisuarkisto (Julkari) (submittedVersion)
- WOS
- Funders
- National Institutes of Health (T32-ES007142, R01-HL134168, K01-HL135342, R01-HL134811)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- human metabolomics, feature selection, high-dimensional data, data management, analytical standardization
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