IPO: a tool for automated optimization of XCMS parameters

Abstract

BackgroundUntargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing.ResultsWe implemented the software package IPO (‘Isotopologue Parameter Optimization’) which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments.IPO optimizes XCMS peak picking parameters by using natural, stable 13C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third.ConclusionsIPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data.The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO. The training sets and test sets can be downloaded from https://health.joanneum.at/IPO.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0562-8) contains supplementary material, which is available to authorized users.

Bioactivity in Rhododendron: A Systemic Analysis of Antimicrobial and Cytotoxic Activities and Their Phylogenetic and Phytochemical Origins

“…For determination of the involved parameter the toolbox IPO has been used (Libiseller et al, 2015). In the course of peak annotation, the intensity threshold was set to 10 3 and 13 C isotope peaks were removed using the Bioconductor package CAMERA (Kuhl et al, 2012).…”

A roadmap for the XCMS family of software solutions in metabolomics

“…The modular nature of the original XCMS software has made it interoperable with new generations of programs for untargeted metabolomics and enabled multiple research laboratories to improve upon the original XCMS algorithms. [14,15,41,42]…”

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

“…The isotope coverage ranges from 0 to 1, where 0 means that no isotope clusters have been detected and 1 means that all peaks are part of isotope clusters. A higher isotope coverage indicates a higher peak picking quality as exploited in [19]. The PPS was proposed in [19] for the quantification of the peak picking quality and implemented in the R package IPO .…”

“…A higher isotope coverage indicates a higher peak picking quality as exploited in [19]. The PPS was proposed in [19] for the quantification of the peak picking quality and implemented in the R package IPO . The PPS is defined as the ratio between the number of reliable peaks squared and the number of non–reliable peaks.…”

“…There are approaches in metabolomics and proteomics which use isotope clusters to improve peak picking [16,17,18]. In addition, isotope clusters have been used as a valuable source for the assessment of the data quality [19] and for database searches with high precision [20]. …”