metaP

metaP automates analysis and standardizes biological interpretation of quantitative metabolomics data across sample phenotypes.

Key Features:

• Automated Data Analysis: Automates the analytical workflow from data acquisition to biological interpretation for quantitative metabolomics datasets.
• Data Quality and Reproducibility: Performs quality checks and estimates reproducibility, including assessment of batch effects.
• Statistical Testing: Conducts hypothesis testing for categorical phenotypes and correlation tests for metric phenotypes, with optional evaluation of all pairwise metabolite concentration ratios.
• Dimensionality Reduction: Implements principal component analysis (PCA) to reduce dimensionality and identify major variance patterns.
• Pathway Mapping: Maps metabolites onto colored KEGG pathway maps for pathway-level visualization.
• Visualization Outputs: Generates PCA and bar plots that can be colored by phenotype and associated with sample and metabolite identifiers.
• Database Cross-References: Cross-references metabolites to HMDB, LipidMaps, KEGG, PubChem, and CAS.

Scientific Applications:

• Drug Testing: Supports metabolome-wide association analyses in drug testing studies.
• Epidemiological Studies: Enables phenotype association and metabolome-wide analyses in epidemiological research.
• Kit-based Metabolomics: Standardizes analysis and interpretation of commercial kit–based metabolomics experiments.

Methodology:

Computational steps explicitly include data quality checks, reproducibility and batch-effect estimation, statistical hypothesis and correlation testing, principal component analysis (PCA), and mapping metabolites to KEGG pathways.