MetaboDiff

MetaboDiff performs differential metabolomic analysis and constructs data-derived metabolic correlation networks to identify differential metabolites and sample-trait associations from raw metabolite measurement tables.

Key Features:

• Raw input support: Accepts raw tables of metabolite measurements as the starting data for analysis.
• Differential metabolomic analysis: Identifies differential metabolite profiles across sample groups.
• Data-derived metabolic correlation networks: Constructs metabolic correlation networks from the metabolite data to represent relationships among metabolites.
• Sample-trait integration: Explores sample traits within the metabolic correlation network to reveal associations between metabolites and phenotypes.
• R implementation: Implemented as an R package for computational analysis.

Scientific Applications:

• Biomarker Discovery: Identification of metabolites associated with specific diseases or conditions.
• Pathway Analysis: Investigation of altered metabolic pathways via correlation network structure.
• Comparative Studies: Comparative analysis of metabolite profiles across sample groups to study disease progression, treatment effects, or genetic variation.

Methodology:

Starts from raw metabolite measurement tables, employs a data-driven approach to construct metabolic correlation networks, and performs differential metabolomic analysis to explore sample-trait associations.