LC-MS-based metabolomics has been an important tool in research. However, analyzing metabolomics data is still challenging. To overcome the challenges, the community has developed numerous tools. However, some limitations exist. Commercial tools only work on the associated instrument platform, online/GUI tools cannot take advantage of the cluster computational power, open-source tools typically follow limited parts of the whole workflow and have no uniform format for data input.<br/>Therefore, we developed tidymass, which is a comprehensive computational framework (Figure 1). Tidymass was developed based on the following strategies.<br/>1) Cross-platform utility. It can be installed on all platforms, so can utilize the computational power of the cluster.<br/>2) Uniform, shareable, traceable, and reproducible. A uniform data format “mass_dataset” class is designed to store metabolomics data and processing information; all the packages in tidymass use the “mass_dataset” as the input data format (Figure 2). This uniform data structure makes it easier to share data. In addition, all the parameters are stored in this class, which makes it is possible to trace the processing parameters used. This provides a highly reproducible and robust analytical framework.<br/>3) Flexible and extensible. Tidymass is a collection of multiple R packages, in which the different packages correspond to different steps of the workflow (Figure 1), which makes it easy for the user to find appropriate functions, and for developers to debug and extend. This modular design makes tidymass a highly flexible tool.

TidyMass: An object-oriented reproducible analysis framework for LC-MS data

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ACS Fall 2022

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