Metabolite-Investigator

Metabolite-Investigator performs integrated analysis of quantitative metabolomics data across multiple studies to identify factor–metabolite associations, network relationships, and covariate models that support biomarker discovery and investigation of metabolic disease mechanisms.

Key Features:

• Data integration and pre-processing: Integration, cleaning, and transformation of metabolomics datasets to harmonize data across studies.
• Factor–metabolite association analysis: Uni- and multivariable statistical analysis of associations between factors (e.g., demographic, lifestyle) and metabolites.
• Network analysis: Construction and analysis of metabolite networks to explore relationships among metabolic features.
• Factor prioritization: Ranking and prioritization of factors influencing metabolite levels across cohorts.
• Cohort interaction identification: Detection of interactions between study cohorts and factors that affect metabolite levels.
• Covariate model inference: Inference of common covariate models across studies to identify consistent patterns and associations.

Scientific Applications:

• Biomarker discovery: Identification of metabolites and metabolite patterns as candidate biomarkers across multiple cohorts.
• Disease mechanism investigation: Elucidation of metabolic pathways and factor influences underlying diseases with metabolic components.
• Cross-cohort reproducibility assessment: Evaluation of consistency and robustness of metabolite–factor associations across diverse study populations.

Methodology:

Data integration, cleaning, and transformation followed by uni- and multivariable factor–metabolite association testing, network analysis, factor prioritization, and inference of common covariate models within a scalable workflow from initial integration to advanced statistical modeling.