Evidence map›Paper›PMID 31925557›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2020

Optimization of XCMS parameters for LC-MS metabolomics: an assessment of automated versus manual tuning and its effect on the final results.

Oihane E Albóniga, Oskar González, Rosa M Alonso, Yun Xu, Royston Goodacre

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
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  4. Review
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  6. Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study.Metabolomics : Official journal of the Metabolomic Society · 2023
    Article
  7. Osmoprotectants play a major role in theFrontiers in plant science · 2023
    Article
  8. Review
  9. Article
  10. Article
  11. An optimization method for untargeted MS-based isotopic tracing investigations of metabolism.Metabolomics : Official journal of the Metabolomic Society · 2022
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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.

Oihane E AlbónigaDepartment of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, Leioa, Spain. oihaneelena.alboniga@ehu.eus.
Oskar GonzálezDepartment of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, Leioa, Spain.
Rosa M AlonsoDepartment of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, Leioa, Spain.
Yun XuDepartment of Biochemistry, Institute of Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, Liverpool, L69 7ZB, UK.
Royston GoodacreDepartment of Biochemistry, Institute of Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, Liverpool, L69 7ZB, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSeveral software packages containing diverse algorithms are available for processing Liquid Chromatography-Mass Spectrometry (LC-MS) chromatographic data and within these deconvolution packages different parameters settings can lead to different outcomes. XCMS is the most widely used peak picking and deconvolution software for metabolomics, but the parameter selection can be hard for inexpert users. To solve this issue, the automatic optimization tools such as Isotopologue Parameters Optimization (IPO) can be extremely helpful.

objectivesTo evaluate the suitability of IPO as a tool for XCMS parameters optimization and compare the results with those manually obtained by an exhaustive examination of the LC-MS characteristics and performance.

methodsRaw HPLC-TOF-MS data from two types of biological samples (liver and plasma) analysed in both positive and negative electrospray ionization modes from three groups of piglets were processed with XCMS using parameters optimized following two different approaches: IPO and Manual. The outcomes were compared to determine the advantages and disadvantages of using each method.

resultsIPO processing produced the higher number of repeatable (%RSD < 20) and significant features for all data sets and allowed the different piglet groups to be distinguished. Nevertheless, on multivariate level, similar clustering results were obtained by Principal Component Analysis (PCA) when applied to IPO and manual matrices.

conclusionIPO is a useful optimization tool that helps in choosing the appropriate parameters. It works well on data with a good LC-MS performance but the lack of such adequate data can result in unrealistic parameter settings, which might require further investigation and manual tuning. On the contrary, manual selection criteria requires deeper knowledge on LC-MS, programming language and XCMS parameter interpretation, but allows a better fine-tuning of the parameters, and thus more robust deconvolution.

Indexed as

SoftwareAnimalsAutomationCalibrationChromatography, LiquidMass SpectrometryMetabolomicsSwineData treatmentIPOLC–MSMetabolomicsXCMS

Identifiers

PMID31925557

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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.