ArticleNature communications2025
Protein-level batch-effect correction enhances robustness in MS-based proteomics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Protein Variability Patterns in Ovarian Serous Carcinoma.International journal of molecular sciences · 2026Article
- Plasma Proteomic Profiling Was Used to Discover a Biochemical Recurrence Prediction Model for Prostate Cancer.Molecular & cellular proteomics : MCP · 2026Article
- Artificial intelligence virtual extracellular vesicles (AIVEVs).Bioactive materials · 2026Review
- Small extracellular vesicles in tumor metabolism and immune escape: biomarkers and therapeutic opportunities.Molecular biology reports · 2026Review
- Plasma EV Proteomics Identifies ECM Remodeling and Inflammatory Proteins LUM and C7 as Candidate Biomarkers in FSHD.Annals of clinical and translational neurology · 2026Article
- Protein-level batch-effect correction enhances robustness in MS-based proteomics.Nature communications · 2025Article
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Authors and funding
13 authors.
Funding
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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.
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Registered trials
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