Lack of standardized applications of bioinformatics and statistical approaches for pre- and postprocessing of global metabolomic profiling data sets collected using high-resolution mass spectrometry platforms remains an inadequately addressed issue in the field. Several publications now recognize that data analysis outcome variability is caused by different data treatment approaches. Yet, there is a lack of interlaboratory reproducibility studies that have looked at the contribution of data analysis techniques toward variability/overlap of results. The goal of our study was to identify the contribution of data pre- and postprocessing methods on metabolomics analysis results. We performed urinary metabolomics from samples obtained from mice exposed to 5 Gray of external beam gamma rays and those exposed to sham irradiation (control group). The data files were made available to study participants for comparative analysis using commonly used bioinformatics and/or biostatistics approaches in their laboratories. The participants were asked to report back the top 50 metabolites/features contributing significantly to the group differences. Herein we describe the outcome of this study which suggests that data preprocessing is critical in defining the outcome of untargeted metabolomic studies.

中文翻译：

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ABRF代谢组学研究小组2016探索性研究：针对非靶向代谢组学的数据分析方法的研究。

对于使用高分辨率质谱平台收集的全球代谢组学谱数据集的预处理和后处理，缺乏生物信息学和统计方法的标准化应用仍然是该领域中尚未充分解决的问题。现在，有几篇出版物认识到数据分析结果的可变性是由不同的数据处理方法引起的。但是，缺乏实验室间的可重复性研究，这些研究关注的是数据分析技术对结果的变异性/重叠性的贡献。我们研究的目的是确定数据预处理和后处理方法对代谢组学分析结果的贡献。我们从暴露于5 Gray外部射线γ射线的小鼠和暴露于假辐射的小鼠（对照组）的样品中进行尿代谢组学研究。数据文件可供研究参与者使用实验室中常用的生物信息学和/或生物统计学方法进行比较分析。要求参与者报告对小组差异有重大贡献的前50种代谢物/特征。在这里，我们描述了这项研究的结果，这表明数据预处理对于定义非靶向代谢组学研究的结果至关重要。