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

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.

Publication
Nature Communications
Xiaotao Shen
Xiaotao Shen
Nanyang Assistant Professor

Metabolomics, Multi-omics, Bioinformatics, Systems Biology.

Dr. Yan Hong
Dr. Yan Hong
Postdoc
Yale University
Dr. Chuchu Wang
Dr. Chuchu Wang
Postdoc
Stanford University
Prof. Peng Gao
Prof. Peng Gao
Assistant Professor
University of Pittsburgh
Prof. Caroline Johnson
Prof. Caroline Johnson
Associate Professor
Yale University
Prof. Michael Snyder
Prof. Michael Snyder
Professor
Stanford University