MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1TH, UK.
Population Health Science, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK.
Bioinformatics. 2022 Mar 28;38(7):1980-1987. doi: 10.1093/bioinformatics/btac059.
Metabolomics is an increasingly common part of health research and there is need for preanalytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. Whilst some preprocessing steps are common, there is currently a lack of standardization and reporting transparency for these procedures.
Here, we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics datasets. The package extracts data from preformed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available batch variables on the data are generated for the purpose of open disclosure. Where possible, we provide users flexibility in defining their own selection thresholds.
metaboprep is an open-source R package available at https://github.com/MRCIEU/metaboprep.
Supplementary data are available at Bioinformatics online.
代谢组学是健康研究中越来越常见的一部分，需要进行分析前的数据处理。研究人员通常需要对数据进行特征描述，并在预期分析的背景下排除误差。虽然有些预处理步骤是常见的，但目前这些过程缺乏标准化和报告透明度。
在这里，我们介绍了 metaboprep，这是一个标准化的数据处理工作流程，用于提取和描述高质量的代谢组学数据集。该软件包从预格式化的工作表中提取数据，提供汇总统计信息，并使用户能够根据一组质量指标选择用于分析的样本和代谢物。为了公开披露的目的，生成一份报告，总结质量指标以及可用批次变量对数据的影响。在可能的情况下，我们为用户提供了定义自己选择阈值的灵活性。
metaboprep 是一个开源的 R 包，可在 https://github.com/MRCIEU/metaboprep 上获得。
补充数据可在生物信息学在线获得。
Zotero 插件