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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiaotao Shen & Hong Yan & Chuchu Wang & Peng Gao & Caroline H. Johnson & Michael P. Snyder, 2022. "TidyMass an object-oriented reproducible analysis framework for LC–MS data," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32155-w
    DOI: 10.1038/s41467-022-32155-w
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    References listed on IDEAS

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    1. Xiaotao Shen & Ruohong Wang & Xin Xiong & Yandong Yin & Yuping Cai & Zaijun Ma & Nan Liu & Zheng-Jiang Zhu, 2019. "Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
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