lmm2met

lmm2met applies linear mixed-effects modeling to adjust individual metabolite signals for technical and subject-specific variation, improving detection of subtle metabolic changes in clinical trials and biomarker discovery.

Key Features:

• Linear Mixed-Effects Modeling: Employs linear mixed-effects modeling (LMM) to separate technical variation and subject-specific variation from biological variables of interest and adjust individual metabolite signals.
• Reduction in Variation: Reduces within-metabolite variation after model fitting to increase signal clarity for downstream analyses.
• Improved Analytical Metrics: Demonstrates improvements in classification accuracy, precision, sensitivity, and specificity relative to other strategies.
• Utilization of Patient Metadata: Leverages patient metadata and subject characteristics to model and normalize metabolite abundances.
• Pre-adjustment for Multivariate Analysis: Adjusts metabolite abundances prior to multivariate analysis to enhance downstream biomarker detection.

Scientific Applications:

• Quantitative clinical metabolomics: Enhances quantitative metabolomics analyses in clinical trials by accounting for inter-individual variability.
• Early-stage disease biomarker discovery: Facilitates identification of candidate metabolic biomarkers in early-stage disease by improving signal-to-noise ratio.
• Precision medicine and treatment response profiling: Supports metabolic profiling of treatment responses and personalized-medicine applications by normalizing patient-specific effects.

Methodology:

Applies linear mixed-effects models to individual metabolite signals, modeling technical and subject-specific factors and using patient metadata to adjust abundances prior to multivariate analysis.