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Burankova, Y.* ; Abele, M.* ; Bakhtiari, M.* ; von Toerne, C. ; Barth, T.K.* ; Schweizer, L.* ; Giesbertz, P.* ; Schmidt, J.R.* ; Kalkhof, S.* ; Müller-Deile, J.* ; van Veelen, P.A.* ; Mohammed, Y.* ; Hammer, E.* ; Arend, L.* ; Adamowicz, K.* ; Laske, T.* ; Hartebrodt, A.* ; Frisch, T.* ; Meng, C.* ; Matschinske, J.* ; Späth, J.* ; Röttger, R.* ; Schwämmle, V.* ; Hauck, S.M. ; Lichtenthaler, S.F.* ; Imhof, A.* ; Mann, M.* ; Ludwig, C.* ; Kuster, B.* ; Baumbach, J.* ; Zolotareva, O.*

Privacy-preserving multicenter differential protein abundance analysis with FedProt.

Nat. Comput. Sci., DOI: 10.1038/s43588-025-00832-7 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises serious privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data, which utilizes federated learning and additive secret sharing. In the absence of a multicenter patient-derived dataset for evaluation, we created two: one at five centers from E. coli experiments and one at three centers from human serum. Evaluations using these datasets confirm that FedProt achieves accuracy equivalent to the DEqMS method applied to pooled data, with completely negligible absolute differences no greater than 4 × 10-12. By contrast, -log10P computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25-26.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Mass-spectrometry; Genes; Metaanalysis; Powerful; Package
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 2662-8457
e-ISSN 2662-8457
Journal Nature Computational Science
Publisher Springer
Publishing Place Campus, 4 Crinan St, London, N1 9xw, England
Reviewing status Peer reviewed
Institute(s) CF Metabolomics & Proteomics (CF-MPC)
POF-Topic(s) 30203 - Molecular Targets and Therapies
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-505700-001
Grants Technische Universitat Munchen
Project SyMBoD
German Federal Ministry of Education and Research (BMBF)
European Union's Horizon Research and Innovation program
CVDLINK project
Lander
Funds of the Excellence Strategy of the Federal Government
Universitat Hamburg
Federal Ministry of Education and Research (BMBF)
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
European Union's Horizon2020 research and innovation program
DOI 10.1038/s43588-025-00832-7
WOS ID 001527747000001
Scopus ID 105010612149
PubMed ID 40646319
Erfassungsdatum 2025-07-21
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