IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets.

Area of Science:

• Metabolomics
• Computational Biology
• Analytical Chemistry

Background:

• Generating comprehensive metabolomics data matrices from LC/HRMS is challenging for large population studies (n > 200).
• Existing methods struggle with data quality and scalability for large-scale research.

Purpose of the Study:

• To present a novel data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package, for generating high-fidelity metabolomics data matrices.
• To specifically address the challenges of processing untargeted LC/HRMS data for organic compounds in large population studies.

Main Methods:

• The IDSL.IPA pipeline identifies 12C and 13C ion pairs, detects and characterizes chromatographic peaks with advanced mass correction and smoothing, and corrects retention times using dynamic markers.
• It annotates peaks using m/z and retention time reference databases and accelerates processing via parallel computation for large datasets.
• The pipeline was evaluated on studies ranging from 200 to 1600 samples.

Main Results:

• IDSL.IPA successfully generates high-quality metabolomics data matrices from large-scale LC/HRMS datasets.
• The pipeline effectively isolates reliable signals for carbon-containing compounds, improving data comprehensiveness and fidelity.
• Successful evaluation across studies with up to 1600 samples demonstrates scalability and robustness.

Conclusions:

• The IDSL.IPA R package provides a robust solution for generating high-fidelity metabolomics data matrices from large population studies.
• This tool facilitates new discoveries in population-scale metabolomics and exposomics research by improving data quality and enabling analysis of complex datasets.
• IDSL.IPA is available on the R CRAN repository, promoting wider adoption and advancement in the field.