INTRODUCTION 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.

中文翻译：

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用于LC-MS代谢组学的XCMS参数优化：自动和手动调整的评估及其对最终结果的影响。

引言几种包含多种算法的软件包可用于处理液相色谱-质谱（LC-MS）色谱数据，并且在这些反卷积软件包中，不同的参数设置可能导致不同的结果。XCMS是用于代谢组学的最广泛使用的峰选择和反卷积软件，但是对于不熟练的用户而言，参数选择可能很困难。要解决此问题，自动优化工具（例如同位素同位素参数优化（IPO））可能会非常有用。目的评估IPO作为XCMS参数优化工具的适用性，并将结果与​​通过详尽检查LC-MS特性和性能而获得的结果进行比较。方法采用XCMS处理来自三组仔猪的正电和负电喷雾电离模式下的两种生物样品（肝脏和血浆）的原始HPLC-TOF-MS数据，并使用经过以下两种不同方法优化的参数XIPO处理：IPO和Manual。比较结果以确定使用每种方法的优缺点。结果IPO处理对所有数据集产生了较高的可重复性（％RSD <20）和重要特征，并可以区分不同的仔猪组。然而，在多变量水平上，当将主成分分析（PCA）应用于IPO和手动矩阵时，可获得相似的聚类结果。结论IPO是一种有用的优化工具，有助于选择合适的参数。它在具有良好LC-MS性能的数据上效果很好，但是缺少足够的数据可能会导致参数设置不切实际，这可能需要进一步研究和手动调整。相反，手动选择标准需要对LC-MS，编程语言和XCMS参数解释有更深入的了解，但可以对参数进行更好的微调，从而实现更强大的反卷积。