Evidence map›Paper›PMID 24957762›Full record

ArticleMetabolites2012

A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data.

Maria Vinaixa, Sara Samino, Isabel Saez, Jordi Duran, Joan J Guinovart, Oscar Yanes

2 registry-linked trialsAbstract read
In one paragraph

Article in Metabolites, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 132 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
132citing papers in PubMed, 3 pooled it
–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.

NCT02342340 nacompletednot on this mapstarted 2015, after this paper: background citation

A Blinded, Randomized, Controlled Study to Examine the Bioavailability of Compounds From Different Bean Varieties in Healthy Individuals.

TypeinterventionalSponsorDr. Peter ZahradkaRan2015 to 2016Enrolled8ConditionsHealthyArmsNavy Beans (cooked), Red Kidney Beans (cooked), Pinto Beans (cooked, Black Beans (cooked), Lentils (cooked)
NCT06449170 naunknown statusnot on this mapstarted 2023, after this paper: background citation

BIAMEX: Discovery of Biomarkers of Intake of Highly Consumed Foods in Mexico by Untargeted Metabolomics

TypeinterventionalSponsorInstituto Nacional de Ciencias Medicas y Nutricion Salvador ZubiranRan2023 to 2024Enrolled12ConditionsNutrition, Healthy, Diet Habit, Diet, Healthy, Dietary ExposureArmsMango Ataulfo, Avocado Hass, Nopal, 3 corn tortilla, Guava
3 · Its place in the literature

Who cites it

132 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  7. Metabolomics Effects of Folding Correction in Retinitis Pigmentosa Rhodopsin Mutant P23A.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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  8. Article
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  11. Review
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  18. Observational
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72 more citing papers are in PubMed but not listed here.

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

6 authors.

Maria VinaixaMetabolomics Platform, Campus Sescelades, Edifici N2, Rovira i Virgili University, Tarragona 43007, Spain. maria.vinaixa@urv.cat.
Sara SaminoMetabolomics Platform, Campus Sescelades, Edifici N2, Rovira i Virgili University, Tarragona 43007, Spain. sara.samino@urv.cat.
Isabel SaezInstitute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain. isabel.saez@irbbarcelona.org.
Jordi DuranSpanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Passeig Bonanova 69, Barcelona 08017, Spain. jordi.duran@irbbarcelona.org.
Joan J GuinovartSpanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Passeig Bonanova 69, Barcelona 08017, Spain. guinovart@irbbarcelona.org.
Oscar YanesMetabolomics Platform, Campus Sescelades, Edifici N2, Rovira i Virgili University, Tarragona 43007, Spain. oscar.yanes@urv.cat.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples.

Identifiers

PMID24957762
PMCPMC3901240

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

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.