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

Qiaochu Chen, Zehui Cao, Yaqing Liu, Naixin Zhang, Yanming Xie, Haonan Chen, Yuanbang Mai, Shumeng Duan, Jiaqi Li, Ying Yu, Yang Zhao, Leming Shi () and Yuanting Zheng ()
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Qiaochu Chen: Fudan University
Zehui Cao: Fudan University
Yaqing Liu: Fudan University
Naixin Zhang: Fudan University
Yanming Xie: Fudan University
Haonan Chen: Fudan University
Yuanbang Mai: Fudan University
Shumeng Duan: Fudan University
Jiaqi Li: Fudan University
Ying Yu: Fudan University
Yang Zhao: National Institute of Metrology
Leming Shi: Fudan University
Yuanting Zheng: Fudan University

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Batch effects, defined as unwanted technical variations caused by differences in labs, pipelines, or batches, are notorious in MS-based proteomics data, wherein protein quantities are inferred from precursor- and peptide-level intensities. However, the optimal stage for batch-effect correction remains elusive and crucial. Leveraging real-world multi-batch data from the Quartet protein reference materials and simulated data, we benchmark batch-effect correction at precursor, peptide, and protein levels combined across two designed scenarios (balanced and confounded), three quantification methods (MaxLFQ, TopPep3, and iBAQ), and seven batch-effect correction algorithms (Combat, Median centering, Ratio, RUV-III-C, Harmony, WaveICA2.0, and NormAE). Our findings reveal that protein-level correction is the most robust strategy, and the quantification process interacts with batch-effect correction algorithms. Furthermore, we extend our analysis to large-scale data from 1431 plasma samples of type 2 diabetes patients in Phase 3 clinical trials, demonstrating the superior prediction performance of the MaxLFQ-Ratio combination. These findings support that batch-effect correction at the protein level enhances multi-batch data integration in large proteomics cohort studies.

Date: 2025
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DOI: 10.1038/s41467-025-64718-y

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