MetCohort: Precise Feature Detection and Correspondence for Untargeted Metabolomics in Large-Scale Cohort Studies.

Area of Science:

• Analytical Chemistry
• Computational Biology
• Biochemistry

Background:

• Untargeted metabolomics using LC-HRMS is crucial for large cohort studies.
• Current data processing methods are complex and present challenges.
• Accurate analysis is vital for understanding biological systems.

Purpose of the Study:

• To introduce MetCohort, a novel computational tool for LC-HRMS metabolomics data.
• To address challenges in large-scale sample analysis, feature detection, and quantification.
• To improve the accuracy and efficiency of metabolomic data processing.

Main Methods:

• Developed MetCohort, integrating chromatogram profile alignment and local anchor matching.
• Employed an outlier removal algorithm for retention time alignment.
• Utilized a 2D ROI-matrix and image processing techniques for feature detection and quantification.

Main Results:

• Achieved accurate retention time alignment across large sample sets.
• Enabled automatic correspondence for feature detection and quantification.
• Reduced false positives and improved detection of low-intensity compounds through holistic feature detection.

Conclusions:

• MetCohort significantly enhances the accuracy of metabolomic data processing.
• The tool improves the efficiency of analyzing large-scale LC-HRMS cohort data.
• MetCohort offers a robust solution for complex metabolomics data challenges.