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



Several 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.


To 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.


Raw 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.


IPO 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.


IPO 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.

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Data availability

The datasets generated and analysed during the current study are available from the corresponding author on request.


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Authors thank the Experimental Neonatal Physiology Unit of the BioCruces Health Research Institute (Cruces University Hospital, Basque Country, Spain) for collecting and providing the samples. O.E.A. thanks the Ministry of Economy and Competitiveness for her predoctoral contract. Authors thank for technical and human support provided by SGIker of UPV/EHU and European funding (ERDF and ESF).


This research was funded by UPV/EHU (Project GIU16/04), the Spanish Ministry of Economy and Competitiveness (Project CTQ2013-46179-R) and the UK Wellcome Trust for MetaboFlow (Grant 202952/Z/16/Z).

Author information

The following authors contributed to conception and design of the study: O.E.A., O.G., R.M.A., Y.X. and R.G.; funding acquisition: R.M.A; experimental performance and acquisition of data: O.E.A. and O.G.; data analysis: O.E.A. and Y.X.; writing – original draft preparation; O.E.A. and O.G., and supervision: O.G., R.M.A., Y.X. and R.G. All authors revised the article critically for important intellectual content and approved the final version.

Correspondence to Oihane E. Albóniga.

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The authors declare that there is no conflict of interest.

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The study was approved by the Ethical Committee for Animal Welfare following the European and Spanish regulations for protection of experimental animals (86/609/EFC and RD 1201/2005).

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Albóniga, O.E., González, O., Alonso, R.M. et al. Optimization of XCMS parameters for LC–MS metabolomics: an assessment of automated versus manual tuning and its effect on the final results. Metabolomics 16, 14 (2020).

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  • IPO
  • XCMS
  • LC–MS
  • Metabolomics
  • Data treatment