MWASTools

MWASTools performs metabolome-wide association analyses and interpretation of metabonomic data for large-scale epidemiological research.

Key Features:

• Quality Control Analysis: Provides robust quality control mechanisms to ensure integrity and reliability of metabonomic datasets prior to analysis.
• Metabolome-Wide Association Analysis (MWAS): Implements statistical models, including partial correlations and generalized linear models, to assess associations between metabolites and phenotypic traits or disease states.
• Model Validation: Applies non-parametric bootstrapping techniques to assess stability and reliability of statistical models used in MWAS.
• Visualization Tools: Produces visualizations to represent MWAS outcomes and aid interpretation of complex association patterns.
• Metabolite Identification: Uses statistical total correlation spectroscopy (STOCSY) to support metabolite assignment by leveraging correlations between NMR signals.
• Biological Interpretation: Facilitates translation of statistical associations into biological insights for downstream investigation.

Scientific Applications:

• Epidemiology: Enables analysis of metabolomic data in large cohorts to identify metabolic markers associated with diseases or health outcomes.
• Systems Biology: Supports investigation of complex interactions within biological systems using comprehensive analytical approaches.

Methodology:

Computational methods explicitly include partial correlations, generalized linear models, non-parametric bootstrapping, statistical total correlation spectroscopy (STOCSY) for NMR-based metabolite assignment, and implementation in R (version ≥ 3.4).