TidyMass an object-oriented reproducible analysis framework for LC–MS data - Shen, Xiaotao; Yan, Hong; Wang, Chuchu; Gao, Peng; Johnson, Caroline H.; Snyder, Michael P. (Nature communications, 2022)
Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project ( https://www.tidymass.org/ ), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline. Reproducibility, traceability, and transparency have been long-standing issues in metabolomics data analysis. Here, the authors present tidyMass, an R-based computational framework that allows designing traceable, shareable, and reproducible data processing and analysis workflows for untargeted metabolomics.