Critical review of reporting of the data analysis step in metabolomics

Considine, Elizabeth C.; Thomas, Grace; Boulesteix, Anne‐Laure; Khashan, Ali S.; Kenny, Louise C.

doi:10.1007/s11306-017-1299-3

Cited by 58 publications

(54 citation statements)

References 65 publications

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“…The metabolomic workflow for both targeted and untargeted metabolomics analysis usually comprises the sequential steps of experimental design, sample collection and preparation, data acquisition, data analysis, metabolite identification (for untargeted metabolomics), and biological interpretation ( Figure ) . Many reviews have previously summarized the advances in metabolomics technologies, including sample collection and preparation and data acquisition and analysis . Herein, we aim to brief the key development of the mass spectrometry (MS)‐based metabolomics technologies that were frequently used in clinical pharmacology studies in the past 5 years.…”

Section: Metabolomics Analytical Technologiesmentioning

confidence: 99%

“…19,20 Many reviews have previously summarized the advances in metabolomics technologies, including sample collection 21 and preparation 22,23 and data acquisition 2,5 and analysis. 24 Herein, we aim to brief the key development of the mass spectrometry (MS)-based metabolomics technologies that were frequently used in clinical pharmacology studies in the past 5 years.…”

Section: Metabolomics Analytical Technologiesmentioning

confidence: 99%

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Emerging Applications of Metabolomics in Clinical Pharmacology

Pang

Wang

2019

Clin Pharma and Therapeutics

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Metabolic disturbances have been associated with many human diseases, including cancer, diabetes, and cardiovascular disease. Metabolomics, a rapidly growing member of the –omics family, investigates cellular metabolism by quantifying metabolites on a large scale and provides a link between metabolic pathways and the upstream genome that governs them. With the advances in analytical technologies, metabolomics is becoming a powerful tool for identifying diagnostic biomarkers of diseases, elucidating the pathological mechanisms, discovering novel drug targets, predicting drug responses, interpreting the mechanisms of drug action, as well as enabling precision treatment of patients. In this review, we highlight the recent advances of technologies and methodologies in metabolomics and their applications to the field of clinical pharmacology. Recent publications from 2013 to 2018 are covered in the review, and current challenges and potential future directions in the field are also discussed.

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Section: Metabolomics Analytical Technologiesmentioning

confidence: 99%

Section: Metabolomics Analytical Technologiesmentioning

confidence: 99%

Emerging Applications of Metabolomics in Clinical Pharmacology

Pang

Wang

2019

Clin Pharma and Therapeutics

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“…[92] Moreover, guidelines and reporting standards [93] from the metabolomics research community-such as the MSI [94] -keep improving amid the call for newer and updated guidelines. [95] Significant progress has been made toward open and sharable data practices that are available for responsible researchers, including newer data standards for sharing quantitative MS-based metabolomics data (i.e., mzTab-M [96] ), ISA-Tab formats for sharing metadata, [97] and concerted calls to share .fid raw file formats generated from NMR-based metabolomics experiments. [98] These practices serve as examples to follow for the rest of the community as a standard FAIR (Findable, Accessible, Interoperable, Re-usable principles (The FAIR principles as published by FORCE11)) [99] practice.…”

Section: New Approaches and Technologies For Future Directionsmentioning

confidence: 99%

Challenges and Opportunities in Cancer Metabolomics

Kumar

Misra

2019

Proteomics

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Challenges in metabolomics for a given spectrum of disease are more or less comparable, ranging from the accurate measurement of metabolite abundance, compound annotation, identification of unknown constituents, and interpretation of untargeted and analysis of high throughput targeted metabolomics data leading to the identification of biomarkers. However, metabolomics approaches in cancer studies specifically suffer from several additional challenges and require robust ways to sample the cells and tissues in order to tackle the constantly evolving cancer landscape. These constraints include, but are not limited to, discriminating the signals from given cell types and those that are cancer specific, discerning signals that are systemic and confounded, cell culture‐based challenges associated with cell line identities and media standardizations, the need to look beyond Warburg effects, citrate cycle, lactate metabolism, and identifying and developing technologies to precisely and effectively sample and profile the heterogeneous tumor environment. This review article discusses some of the current and pertinent hurdles in cancer metabolomics studies. In addition, it addresses some of the most recent and exciting developments in metabolomics that may address some of these issues. The aim of this article is to update the oncometabolomics research community about the challenges and potential solutions to these issues.

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“…However, as noted in [4], an excessive number of published studies are unclearly or incompletely reported, so that it is impossible to follow or replicate statistical analyses.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we oer a reproducible workow for analysis of metabolomics data in relation to one covariate of interest. The workow is demonstrated by re-using well-documented data, to be found on the repository MetaboLights [9], as suggested in [4] as instrument to improve clarity and transparency in metabolomics analysis.…”

Section: Introductionmentioning

confidence: 99%

A new workflow combining R packages for statistical analysis of metabolites

Ferrario

2019

Preprint

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In metabolomics, the investigation of an association between many metabolites and one trait (such as age in humans or cultivar in foods) is a central research question. On this topic, we present a complete statistical analysis, combining selected R packages in a new workow, which we are sharing completely, according to modern standards and research reproducibility requirements. We demonstrate the workow using a large-scale study with public data, available on repositories. Hence, the workow can directly be re-used on quite dierent metabolomics data, when searching for association with one covariate of interest.

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Critical review of reporting of the data analysis step in metabolomics

Cited by 58 publications

References 65 publications

Emerging Applications of Metabolomics in Clinical Pharmacology

Emerging Applications of Metabolomics in Clinical Pharmacology

Challenges and Opportunities in Cancer Metabolomics

A new workflow combining R packages for statistical analysis of metabolites

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