Berlin Institute of Health (BIH), Charitéplatz 1, 10117 Berlin, Germany.
Max Delbrück Center (MDC) for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany.
Anal Chem. 2020 Aug 4;92(15):10241-10245. doi: 10.1021/acs.analchem.0c00136. Epub 2020 Jul 16.
Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data are essential for confidence in analytical results and are particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory's routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire data set alongside standard analyses of quality control samples can be used to identify outliers and quantify systematic trends to improve downstream analysis. Finally, we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and other target data sets and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.
靶向定量质谱代谢物分析是代谢组学研究的主力。稳健和可重复的数据对于分析结果的可信度至关重要，特别是对于大规模研究而言。现在有商业试剂盒可供使用，这些试剂盒使用经过精心校准和验证的内部和外部标准，以提供这种可靠性。然而，在使用过程中它们仍然会受到处理和技术错误的影响，因此应根据实验室的常规质量保证和质量控制措施，以保持对结果的信心。我们讨论了使用这些试剂盒时的重要系统和随机测量误差，并提出了检测和量化这些误差的措施。我们展示了如何通过更广泛地分析整个数据集以及标准的质量控制样本分析，来识别异常值并量化系统趋势，从而改进下游分析。最后，我们介绍了 MeTaQuaC 软件，它实现了 Biocrates 试剂盒和其他目标数据集的上述概念和方法，并创建了一个包含丰富可视化和信息性评分和汇总统计数据的综合质量控制报告。还包括初步的无监督多元分析方法，以快速洞察研究变量和组。MeTaQuaC 作为一个开放源代码 R 包，根据宽松的 MIT 许可证提供，并包括详细的用户文档。
