Evidence map›Paper›PMID 30830321›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2017

Critical review of reporting of the data analysis step in metabolomics.

E C Considine, G Thomas, A L Boulesteix, A S Khashan, L C Kenny

Abstract read
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In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing 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.

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

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

  1. Pooled it
  2. Pooled it
  3. Pooled it
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  20. 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

5 authors.

E C ConsidineThe Irish Centre for Fetal and Neonatal Translational Research (INFANT), Department of Obstetrics and Gynaecology, University College Cork, Cork, Ireland. lizconsidine@gmail.com.
G ThomasSQU4RE, Sint-Alfonsusstraat 17, 8800, Roeselare, Belgium.
A L BoulesteixDepartment of Medical Informatics, Biometry and Epidemiology, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
A S KhashanThe Irish Centre for Fetal and Neonatal Translational Research (INFANT), Department of Obstetrics and Gynaecology, University College Cork, Cork, Ireland.
L C KennyThe Irish Centre for Fetal and Neonatal Translational Research (INFANT), Department of Obstetrics and Gynaecology, University College Cork, Cork, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionWe present the first study to critically appraise the quality of reporting of the data analysis step in metabolomics studies since the publication of minimum reporting guidelines in 2007.

objectivesThe aim of this study was to assess the standard of reporting of the data analysis step in metabolomics biomarker discovery studies and to investigate whether the level of detail supplied allows basic understanding of the steps employed and/or reuse of the protocol. For the purposes of this review we define the data analysis step to include the data pretreatment step and the actual data analysis step, which covers algorithm selection, univariate analysis and multivariate analysis.

methodWe reviewed the literature to identify metabolomic studies of biomarker discovery that were published between January 2008 and December 2014. Studies were examined for completeness in reporting the various steps of the data pretreatment phase and data analysis phase and also for clarity of the workflow of these sections.

resultsWe analysed 27 papers, published anytime in 2008 until the end of 2014 in the area or biomarker discovery in serum metabolomics. The results of this review showed that the data analysis step in metabolomics biomarker discovery studies is plagued by unclear and incomplete reporting. Major omissions and lack of logical flow render the data analysis' workflows in these studies impossible to follow and therefore replicate or even imitate.

conclusionsWhile we await the holy grail of computational reproducibility in data analysis to become standard, we propose that, at a minimum, the data analysis section of metabolomics studies should be readable and interpretable without omissions such that a data analysis workflow diagram could be extrapolated from the study and therefore the data analysis protocol could be reused by the reader. That inconsistent and patchy reporting obfuscates reproducibility is a given. However even basic understanding and reuses of protocols are hampered by the low level of detail supplied in the data analysis sections of the studies that we reviewed.

Indexed as

Data AnalysisAlgorithmsBiomarkersHumansMetabolomicsMultivariate AnalysisReproducibility of ResultsWorkflowBiomarkersBiomarker discoveryData analysisGuidelinesMetabolomicsMinimum standardsReporting

Identifiers

PMID30830321

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.