Metabolomics aims to measure, identify and (semi-)quantify a large number of metabolites in a biological system. The methods of choice are generally Nuclear Magnetic Resonance (NMR) spectroscopy or Mass Spectrometry (MS). The latter can be used directly (e.g. direct infusion MS), but is normally coupled to a separation system such as Gas Chromatography (GC-MS), Liquid Chromatography (LC-MS) or Capillary Electrophoresis (CE-MS). In order to increase the separation power multidimensional separation systems are becoming common, such as comprehensive two-dimensional GC or LC (GC×GC, LC×LC) or LC combined with ion mobility spectrometry (LC-IMS) before MS detection. Other detection techniques include Raman spectroscopy, UV/VIS (ultraviolet/visible absorbance spectrophotometric detection- typically with a Diode Array Detector (DAD)) and fluorescence. NMR also benefits from separation techniques, such as LC-MS-NMR or LC-SPE-NMR. Additionally, there are a wide variety of pulse programs commonly used in 1D and even bigger set of 2D pulse programs used in metabolomics and for metabolite identification, for a comprehensive review on this see [1]. A general introduction to metabolomics can be found in textbooks like [2–4] or online courses like [5,6].
All of these analytical platforms and methodologies generate large amounts of high dimensional and complex experimental raw data when used in a metabolomics context. The amount of data, the need for reproducible research, and the complexities of the biological problem under investigation necessitates a high degree of automation and standard workflows in the data analysis. Beside vendor software, which is usually not open, open source projects offer the possibility to work in community-driven teams, perform reproducible data analysis and to work with different types of raw data. Many tools and methods have been developed to facilitate the processing and analysis of metabolomics data; most seek to solve a specific challenge in the multi-step data processing and analysis workflow.
This review provides an overview of the metabolomics-related tools that are made available as packages (and a limited number of non-trivial, non-packaged scripts) for the statistics environment and programming language R [7]. We have included packages even if they are not anymore part of current CRAN or Bioconductor, i.e. as archived versions only. We have not included packages described in the literature if no longer available for download at all. We did include packages that are currently available, but not yet published in the scientific literature. The package descriptions have been grouped in sections according to the typical steps in the metabolomics data analysis pipeline for different analytical technologies, following the typical workflow steps from MS, NMR and UV data analysis, metabolite annotation, statistical analysis, molecular structure, network and pathway analysis and finally covering packages embracing large parts of the workflow.