sampleDrift

sampleDrift assesses how pre-centrifugation delay time and temperature affect plasma metabolomic profiles and predicts and corrects resulting variability to improve biobanked sample data quality.

Key Features:

• Metabolomic Analysis via NMR: Uses nuclear magnetic resonance (NMR) spectroscopy data from plasma to detect changes in energy metabolism intermediates and lipid variability.
• Random Forest Modeling: Employs random forest algorithms to predict pre-centrifugation delay time and temperature from observed metabolomic alterations, with validation on independent sample sets.
• Cluster-Based Metabolomic Profiling: Applies a cluster-based approach to identify reproducible effects of delay time on metabolic profiles at 4°C and 22°C, with more pronounced changes at 22°C.
• Error Correction for Data Quality Improvement: Implements correction strategies to adjust for pre-analytical handling errors and enhance metabolomic data quality, particularly under conditions that induce greater variability (e.g., 22°C).
• Predictive Capability for Biobank Sample Management: Provides a predictive framework to estimate the impact of pre-centrifugation delay and temperature on biobanked plasma prior to downstream analyses.

Scientific Applications:

• Biobank sample integrity: Assessing and correcting pre-analytical variability in biobanked plasma across study centers and legacy collections to improve reproducibility.
• Metabolomics and biomarker studies: Informing investigations of metabolic diseases, pharmacokinetics, and precise biomarker quantification by modeling handling-induced metabolomic changes.

Methodology:

Uses NMR data collection from plasma samples under varied pre-centrifugation conditions; applies cluster-based profiling; builds random forest models to predict delay time and temperature; validates predictions on independent sample sets; and implements correction algorithms to adjust metabolomic measurements.