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

Abstract

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.

Objectives 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.

Methods 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.

Results 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.

Conclusion 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.