MetaFIND

MetaFIND performs post-feature-selection correlation analysis of metabolomics (metabonomics) datasets from NMR spectroscopy and Mass Spectrometry to identify correlated features and refine metabolite signatures for improved sample classification.

Key Features:

• Implementation: Java-based application for computational analysis of metabolomics feature sets.
• Post-feature-selection correlation analysis: Performs correlation analysis on feature sets selected by upstream feature selection methods.
• Real-time correlation analysis: Supports real-time, interactive correlation analysis of feature sets to explore relationships among peaks or features.
• Discovery of related features: Identifies additional features related to selected class-discriminating features to expand candidate metabolite sets.
• Associated metabolite analysis: Analyzes metabolites associated with correlated features to aid interpretation of metabolic signatures.
• Identification of overlooked features: Detects significant features and potential novel class-discriminating metabolites missed by primary feature selection.
• Higher-level correlation discovery: Reveals higher-level metabolite correlations that may be obscured by multi-collinearity.
• Robustness considerations: Addresses discrepancies arising from experimental noise, technique choice, threshold settings, high dimensionality, and multi-collinearity.

Scientific Applications:

• Metabolite signature elucidation: Elucidates metabolite signatures from selected features across diverse NMR and Mass Spectrometry datasets.
• Novel marker discovery: Facilitates discovery of novel class-discriminating metabolites overlooked by standard feature selection.
• Post-selection validation and extension: Validates and extends feature selection results by identifying correlated and functionally related features.
• Improved classification and interpretability: Supports improved sample classification accuracy and model interpretability by expanding and contextualizing discriminating features.
• Inference of metabolic correlations: Infers higher-level metabolite correlations relevant to understanding metabolic processes.

Methodology:

Performs correlation analysis of post-feature-selection feature sets to discover related features, analyze associated metabolites, and identify higher-level metabolite correlations in NMR spectroscopy and Mass Spectrometry metabolomics data.