TidyMass an object-oriented reproducible analysis framework for LC–MS data

Xiaotao Shen, Hong Yan, Chuchu Wang, Peng Gao, Caroline H. Johnson () and Michael P. Snyder ()
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Xiaotao Shen: Stanford University School of Medicine
Hong Yan: Yale School of Public Health
Chuchu Wang: Stanford University
Peng Gao: Stanford University School of Medicine
Caroline H. Johnson: Yale School of Public Health
Michael P. Snyder: Stanford University School of Medicine

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-32155-w

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