MPEA

MPEA performs pathway enrichment analysis of metabolite profiling data to identify metabolic pathways enriched in ranked compound lists derived from gas chromatography–mass spectrometry (GC-MS).

Key Features:

• Pathway enrichment testing: Implements a gene set enrichment analysis (GSEA)-inspired test that assesses whether metabolites from predefined pathways are overrepresented at the top or bottom of a ranked metabolite list.
• Many-to-many mapping handling: Resolves many-to-many relationships between query compounds and metabolic annotations, allowing compounds to map to multiple pathways and annotations to include multiple compounds.
• GC-MS support: Operates on metabolite profiling data generated by gas chromatography–mass spectrometry (GC-MS), which can quantify hundreds of small molecules per run.
• System-level interpretation: Provides system-level visualization and interpretation of metabolite data to contextualize pathway-level changes.
• Pathway sensitivity: Detects significant pathways even when individual metabolites do not reach statistical significance.
• Cross-omics concordance: Produces pathway-level results that can be compared with transcriptomics data and have shown concordant findings.
• Comparative performance: Has identified more pathways than competing metabolic pathway methods in demonstrated analyses.

Scientific Applications:

• Functional interpretation of metabolomes: Assigns biological meaning to GC-MS metabolite profiles by identifying enriched metabolic pathways.
• Phenotype-associated pathway discovery: Identifies pathway alterations associated with phenotypic variation, exemplified by analyses of body-weight discordant twin pairs.
• Integrative analysis with transcriptomics: Enables cross-omics comparison by aligning pathway-level metabolite results with transcriptomics datasets.
• Investigation of disease and metabolic processes: Supports studies of disease mechanisms and system-level metabolic process changes.

Methodology:

Applies a GSEA-inspired enrichment test on ranked metabolite lists by mapping query compounds to predefined pathways and explicitly handling many-to-many compound-to-annotation relationships.