Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples

Area of Science:

• Metabolomics
• Analytical Chemistry
• Biotechnology

Background:

• Untargeted metabolomics is vital for biomarker discovery, drug development, and precision medicine.
• Instrumental drifts (retention time, signal intensity) pose challenges in large-scale mass spectrometry-based metabolomics.
• Ensuring data quality requires accounting for these variations during data processing.

Purpose of the Study:

• To recommend an optimal data processing workflow for untargeted metabolomics using intrastudy quality control (QC) samples.
• To identify and mitigate errors caused by instrumental drifts.
• To compare the performance of different batch-effect correction methods.

Main Methods:

• Developed a data processing workflow incorporating intrastudy QC samples.
• Evaluated three popular batch-effect correction methods.
• Utilized QC-based metrics and a machine learning approach on biological samples for performance evaluation.

Main Results:

• The TIGER method exhibited the best performance in batch-effect correction.
• TIGER significantly reduced the relative standard deviation of QCs and dispersion ratio.
• TIGER achieved the highest area under the receiver operating characteristic curve with multiple classifiers.

Conclusions:

• The recommended workflow enhances data quality for untargeted metabolomics.
• Effective batch-effect correction is crucial for reliable biomarker discovery and precision medicine.
• The TIGER method is a highly effective tool for improving metabolomics data integrity.