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

Xiaotao Shen

doi:10.1038/s41467-022-32155-w

2022-07-28

Caroline H. Johnson *

M Snyder *

DOI：10.1038/s41467-022-32155-w

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摘要

En 中文

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.

Keyword:

MISSING VALUE IMPUTATION

MASS-SPECTROMETRY DATA

METABOLOMICS

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期刊

Nature Communications

IF:

15.7

论文数:

9.4W

被引数:

91.2W

机构

Yale University

学者数:

6.5W

论文数: 6.0W

被引数: 10.0W

Stanford University

学者数:

9.6W

论文数: 8.2W

被引数: 17.0W

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