Visualization, Quantification, and Alignment of Spectral Drift in Population Scale Untargeted Metabolomics Data

Area of Science:

• Metabolomics
• Biomarker Discovery
• Analytical Chemistry

Background:

• Untargeted liquid-chromatography-mass spectrometry (LC-MS) is crucial for understanding human health and disease.
• Analyzing large-scale metabolomics data is challenging due to signal drifts and batch effects.
• Existing methods lack efficient tools for drift correction and feature filtering in population studies.

Purpose of the Study:

• To develop and validate R-based scripts for preprocessing and postprocessing LC-MS metabolomics data.
• To enable accurate nonlinear retention time correction and visualization of signal drift.
• To automate the filtering of unstable spectral features for large cohort analysis.

Main Methods:

• Development of a suite of R scripts for raw LC-MS data preprocessing.
• Implementation of bulk nonlinear retention time correction at the raw data level.
• Postprocessing tools for peak alignment accuracy visualization, quantification, and hierarchical clustering of signal profiles.

Main Results:

• Demonstrated substantial improvement in peak alignment accuracy using the developed tools.
• Achieved automated data filtering and enhanced statistical power for detecting metabolite-disease correlations.
• Successfully applied the methods to a metabolomics dataset from approximately 3000 human plasma samples.

Conclusions:

• The developed R-based scripts provide efficient solutions for common challenges in LC-MS metabolomics data analysis.
• These tools facilitate robust analysis of population-scale cohorts, enabling deeper insights into health and disease.
• The approach enhances the reliability and statistical power of metabolomics studies for biomarker discovery.