Witting Michael, Rainer Johannes
Metabolomics and Proteomics Core, Helmholtz Munich, Munich, Germany.
Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.
Methods Mol Biol. 2025;2891:67-89. doi: 10.1007/978-1-0716-4334-1_4.
Metabolomics data analysis includes, next to the preprocessing, several additional repetitive tasks that can however be heavily dataset dependent or experiment setup specific due to the vast heterogeneity in instrumentation, protocols, or also compounds/samples that are being measured. To address this, various toolboxes and software packages in Python or R have been and are being developed providing researchers and analysts with bioinformatic/chemoinformatic tools to create their own workflows tailored toward their specific needs. This chapter presents tools and example workflows for common tasks focusing on the functionality provided by R packages developed as part of the RforMassSpectrometry initiative. These tasks include, among others, examples to work with chemical formulae, handle and process mass spectrometry data, or calculate similarities between fragment spectra.
代谢组学数据分析除预处理外，还包括其他几个重复性任务，然而，由于仪器、实验方案或所测量的化合物/样本存在巨大的异质性，这些任务在很大程度上可能依赖于数据集或特定的实验设置。为了解决这个问题，已经并正在开发各种Python或R语言的工具箱和软件包，为研究人员和分析师提供生物信息学/化学信息学工具，以创建针对其特定需求的工作流程。本章介绍了常见任务的工具和示例工作流程，重点关注作为RforMassSpectrometry计划一部分开发的R包所提供的功能。这些任务包括，例如，处理化学式、处理和分析质谱数据或计算碎片光谱之间的相似度等示例。
