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

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 ca...

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Veröffentlicht in:Nature communications Jg. 13; H. 1; S. 4365 - 12
Hauptverfasser: Shen, Xiaotao, Yan, Hong, Wang, Chuchu, Gao, Peng, Johnson, Caroline H., Snyder, Michael P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 28.07.2022
Nature Publishing Group
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Schlagworte:
101/58
631/114/794
631/1647/296
631/1647/48
631/45/320
Annotations
Chromatography, Liquid
Computer applications
Data analysis
Data processing
Data structures
Datasets
Design
Ecosystem
Humanities and Social Sciences
Information processing
Interoperability
Linux
Metabolomics
Modular structures
multidisciplinary
Object oriented programming
Operating systems
Reproducibility
Reproducibility of Results
Science
Science (multidisciplinary)
Software
Tandem Mass Spectrometry
Workflow
ISSN:2041-1723, 2041-1723
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Beschreibung
Zusammenfassung: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.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-32155-w