MetaboAnalystR

MetaboAnalystR streamlines LC-MS metabolomics data analysis through an end-to-end pipeline encompassing raw spectra processing, compound identification, statistical analysis, and functional interpretation.

Key Features:

• Auto-optimized feature detection and quantification: An algorithm for LC-MS1 spectra processing that automatically optimizes feature detection and quantification.
• MS2 spectra deconvolution and compound identification: Efficient deconvolution and compound identification for both data-dependent and data-independent acquisition methods.
• Integrated spectral annotation: More accurate functional interpretation through integrated spectral annotation.
• Statistical analysis utilities: Existing statistical analysis capabilities integrated within the workflow.

Scientific Applications:

• Untargeted global metabolomics: Supports LC-MS based global metabolomics research in an open-source R environment.
• Peak picking: Performance validated for peak picking tasks using standards mixtures, dilution series, and clinical metabolomics samples.
• Spectral deconvolution: Validated for spectral deconvolution across diverse sample types.
• Compound identification: Validated for compound identification with good computing efficiency.

Methodology:

The workflow covers raw spectra processing, compound identification, statistical analysis, and functional interpretation. Comprehensive validation studies were performed using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series, and clinical metabolomics samples.