QComics: Recommendations and Guidelines for Robust, Easily Implementable and Reportable Quality Control of...

Area of Science:

• Metabolomics
• Bioinformatics
• Data Science

Background:

• Quality control is essential for reproducible metabolomics data.
• Current protocols are often non-standardized and poorly documented.
• Lack of standardized quality control hinders data reliability.

Purpose of the Study:

• To develop a robust, implementable, and reportable method for metabolomics data quality control.
• To address limitations in existing quality control strategies.
• To establish best practices for metabolomics data quality.

Main Methods:

• Developed QComics, a sequential protocol for data quality monitoring and control.
• Includes steps for background noise correction, signal drift detection, and outlier removal.
• Addresses missing data and monitors quality markers for preanalytical factors.

Main Results:

• QComics provides a comprehensive approach to metabolomics data quality control.
• The method distinguishes between missing values and absent data, preserving biological information.
• It highlights the impact of preanalytical factors on metabolomics results.

Conclusions:

• QComics offers a standardized and reportable solution for metabolomics quality control.
• Implementation of QComics can lead to improved data accuracy and reproducibility.
• This method contributes to establishing gold standards in metabolomics research.