Data pre-processing of the LC-MS data is a critical step in untargeted metabolomics studies in order to achieve correct biological interpretations. Several tools have been developed for pre-processing, and these can be classified into either commercial or open source software. This case report aims to compare two specific methodologies, Agilent Profinder vs. R pipeline, for a metabolomic study with a large number of samples. Specifically, 369 plasma samples were analyzed by HPLC-ESI-QTOF-MS. The collected data were pre-processed by both methodologies and later evaluated by several parameters (number of peaks, degree of missingness, quality of the peaks, degree of misalignments, and robustness in multivariate models). The vendor software was characterized by ease of use, friendly interface and good quality of the graphs. The open source methodology could more effectively correct the drifts due to between and within batch effects. In addition, the evaluated statistical methods achieved better classification results with higher parsimony for the open source methodology, indicating higher data quality. Although both methodologies have strengths and weaknesses, the open source methodology seems to be more appropriate for studies with a large number of samples mainly due to its higher capacity and versatility that allows combining different packages, functions, and methods in a single environment.

中文翻译：

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从针对非目标LC-MS代谢组学的特定供应商到基于R的管道切换的案例报告。

为了实现正确的生物学解释，LC-MS数据的数据预处理是非目标代谢组学研究的关键步骤。已经开发了几种用于预处理的工具，这些工具可以分为商业软件或开源软件。本案例报告旨在比较两种特殊的方法，即Agilent Profinder vs. R管线，用于大量样品的代谢组学研究。具体而言，通过HPLC-ESI-QTOF-MS分析了369个血浆样品。两种方法都对收集到的数据进行了预处理，然后通过多个参数（峰数，缺失程度，峰的质量，错位程度和多变量模型的鲁棒性）进行了评估。供应商软件的特点是易于使用，友好的界面和良好的图形质量。开源方法可以更有效地纠正由于批次效应之间和之内的漂移。此外，对于开放源代码方法，所评估的统计方法获得了更好的分类结果和更高的简约性，表明数据质量更高。尽管两种方法都有其优点和缺点，但开放源代码方法似乎更适合于大量样本的研究，这主要是由于其较高的功能和多功能性，可以在一个环境中组合不同的程序包，功能和方法。