Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in...

Area of Science:

• Metabolomics
• Bioinformatics
• Analytical Chemistry

Background:

• Untargeted metabolomics relies on computational tools for data processing.
• Poor chromatographic peak shape leads to quantitative variation in automated data extraction.
• This computational variation is an underinvestigated source of error in omics studies.

Purpose of the Study:

• To investigate factors contributing to computational variation in untargeted metabolomics.
• To compare peak height (PH) and peak area (PA) based quantification methods.
• To develop a workflow to minimize computational variation for more reliable results.

Main Methods:

• Experimental factors (sample concentration, LC columns, software) were analyzed for their impact on computational variation.
• Peak height (PH) and peak area (PA) based quantification were systematically compared.
• A quality control (QC) sample-based correction workflow was developed and applied.

Main Results:

• Computational variation was found to be consistent at specific concentrations.
• The MS-DIAL software demonstrated more precise PH-based quantification.
• The proposed QC workflow corrected 652 out of 915 features, with 31% showing altered statistical significance.

Conclusions:

• Computational variation is a significant factor affecting quantitative metabolomics.
• The developed QC workflow effectively minimizes computational variation.
• This approach enhances statistical confidence in comparative metabolomics studies.