Journal Article

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

Xie, Yanming, Duan, Shumeng, Chen, Haonan, Shi, Leming, Chen, Qiaochu, Liu, Yaqing, Li, Jiaqi, Cao, Zehui, Zhao, Yang, Yu, Ying, Zheng, Yuanting, Mai, Yuanbang, Zhang, Naixin

2025

Overview

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. Batch effects in MS-based proteomics pose important challenges in protein quantification, and the optimal stage for batch-effect correction remains elusive and crucial. Here, the authors, through benchmarking using reference standards and simulated data, demonstrate that protein-level batch correction is most robust in MS-based proteomics.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114/2401 / 631/114/2784 / 631/45/475 / 631/61/475 / 631/61/54 / 82/58 / Algorithms / Case studies / Clinical trials / Data integration / Datasets / Diabetes / Diabetes mellitus (non-insulin dependent) / Diabetes Mellitus, Type 2 - blood / Humanities and Social Sciences / Humans / Mass Spectrometry - methods / multidisciplinary / Peptides / Precursors / Proteins / Proteomics / Proteomics - methods / Reference materials / Regression analysis / Reproducibility / Robustness / Science / Science (multidisciplinary)