tidyMass

tidyMass provides an R-based framework to process and analyze LC-MS-based untargeted metabolomics data, enabling reproducible, traceable, and transparent metabolomic data processing and interpretation.

Key Features:

• Reproducibility and Traceability: Enables creation of traceable, shareable, and reproducible workflows for LC-MS-based untargeted metabolomics analyses.
• Object-Oriented Design: Adopts object-oriented data structures for structured data management and manipulation.
• Modular Architecture: Composed of multiple R packages that share a unified design philosophy, grammar, and data structure.
• Comprehensive Ecosystem: Provides functionalities spanning raw data preprocessing through advanced statistical analyses for metabolomics datasets.
• Metabolite Identification and Quantification: Supports identification and quantification of a broad spectrum of metabolites from untargeted LC-MS data.

Scientific Applications:

• Untargeted Metabolite Profiling: Supports identification and quantification of metabolites from LC-MS untargeted metabolomics datasets.
• Biomarker Discovery: Facilitates detection of candidate biomarkers through robust data processing and statistical analysis.
• Metabolic Pathway Analysis: Enables investigation of metabolic pathways and exploration of complex biological systems.

Methodology:

Computational methods explicitly include object-oriented data structures, a modular set of R packages with a unified grammar and data structure, raw LC-MS data preprocessing, advanced statistical analyses, and generation of traceable, shareable, and reproducible workflows with the ability to integrate additional R packages.