MSstatsQC-ML: A Supervised Machine Learning Approach to Monitor System Suitability and Quality Control in Mass...

Area of Science:

• Proteomics
• Analytical Chemistry
• Biotechnology

Background:

• Mass spectrometry (MS) is crucial for proteome analysis but susceptible to technological variability, impacting result reproducibility.
• Current quality control (QC) methods using statistical summaries of standard mixtures struggle with multivariate data and scalability.
• Reliable QC is essential for accurate interpretation of complex proteomic data.

Purpose of the Study:

• To introduce MSstatsQC-ML, a machine learning (ML) approach for enhanced quality control in mass spectrometry.
• To optimize decision-making in QC using standard mixtures with numerous analytes and metrics.
• To improve the detection and management of suboptimal mass spectrometry performance.

Main Methods:

• Developed MSstatsQC-ML, integrating ML classifiers with experimental design for simulating suboptimal MS runs.
• Incorporated informative features from QC metrics for training ML classifiers.
• Utilized longitudinal analysis of feature values to interpret root causes of suboptimal performance.

Main Results:

• MSstatsQC-ML demonstrated reduced error rates in detecting suboptimal mass spectrometry performance.
• The ML approach outperformed traditional QC methods in evaluations.
• Enabled interpretation of root causes and informed preventive actions for QC issues.

Conclusions:

• MSstatsQC-ML offers a scalable and effective machine learning solution for mass spectrometry quality control.
• The approach enhances the reliability and reproducibility of proteomic data analysis.
• MSstatsQC-ML is available as an open-source R/Bioconductor package (MSstatsQC).