Evidence map›Paper›PMID 38941008›Full record

ReviewMetabolomics : Official journal of the Metabolomic Society2024

Designing optimal experiments in metabolomics.

Mathies Brinks Sørensen, Jan Kloppenborg Møller, Mikael Lenz Strube, Charlotte Held Gotfredsen

Abstract readReview
PubMed Publisher
In one paragraph

Review in Metabolomics : Official journal of the Metabolomic Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mathies Brinks SørensenDepartment of Chemistry, Technical University of Denmark, Kemitorvet, 2800, Kongens Lyngby, Hovedstaden, Denmark.
Jan Kloppenborg MøllerDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Asmussens Allé, 2800, Kongens Lyngby, Hovedstaden, Denmark.
Mikael Lenz StrubeDepartment of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, 2800, Kongens Lyngby, Hovedstaden, Denmark.
Charlotte Held GotfredsenDepartment of Chemistry, Technical University of Denmark, Kemitorvet, 2800, Kongens Lyngby, Hovedstaden, Denmark. chg@kemi.dtu.dk.

Funding

Danmarks Grundforskningsfond DNRF137
6 · The paper itself

Abstract

backgroundMetabolomics data is often complex due to the high number of metabolites, chemical diversity, and dependence on sample preparation. This makes it challenging to detect significant differences between factor levels and to obtain accurate and reliable data. To address these challenges, the use of Design of Experiments (DoE) techniques in the setup of metabolomic experiments is crucial. DoE techniques can be used to optimize the experimental design space, ensuring that the maximum amount of information is obtained from a limited sample space. AIM OF REVIEW: This review aims at providing a baseline workflow for applying DoE when generating metabolomics data. KEY SCIENTIFIC CONCEPTS OF REVIEW: The review provides insights into the theory of DoE. The review showcases the theory being put into practice by highlighting different examples DoE being applied in metabolomics throughout the literature, considering both targeted and untargeted metabolomic studies in which the data was acquired using both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry techniques. In addition, the review presents DoE concepts not currently being applied in metabolomics, highlighting these as potential future prospects.

Indexed as

Magnetic Resonance SpectroscopyMass SpectrometryMetabolomicsResearch DesignHumansDesign of ExperimentsMass spectrometryMetabolomicsNuclear magnetic resonance

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.