Protein-level batch-effect correction enhances robustness in MS-based proteomics.

Area of Science:

• Proteomics
• Bioinformatics
• Data Science

Background:

• Mass spectrometry (MS)-based proteomics is susceptible to batch effects, which are technical variations impacting protein quantification.
• The optimal stage for applying batch-effect correction in proteomics workflows is not well-established.

Purpose of the Study:

• To benchmark batch-effect correction strategies at different data levels (precursor, peptide, protein) in MS-based proteomics.
• To evaluate the impact of quantification methods and correction algorithms on multi-batch data integration.

Main Methods:

• Utilized real-world (Quartet reference materials) and simulated multi-batch proteomics data.
• Compared seven batch-effect correction algorithms across three quantification methods (MaxLFQ, TopPep3, iBAQ).
• Assessed correction performance in balanced and confounded experimental designs.

Main Results:

• Protein-level batch-effect correction demonstrated the highest robustness across tested scenarios.
• Quantification methods significantly interact with the performance of batch-effect correction algorithms.
• The MaxLFQ-Ratio combination showed superior prediction performance in large-scale clinical trial data.

Conclusions:

• Batch-effect correction at the protein level is recommended for robust multi-batch proteomics data integration.
• The choice of quantification method influences the effectiveness of batch-effect correction strategies.
• Optimized batch-effect correction enhances the utility of large-scale proteomics cohorts in clinical research.