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<p>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 <i>E. coli</i> 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<sup>−12</sup>. By contrast, −log<sub>10</sub><i>P</i> computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25–26.</p>

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Privacy-preserving multicenter differential protein abundance analysis with FedProt

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月11日

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  • Yuliya Burankova,
  • Miriam Abele,
  • Mohammad Bakhtiari,
  • Christine von Toerne,
  • Teresa K. Barth,
  • Lisa Schweizer,
  • Pieter Giesbertz,
  • Johannes R. Schmidt,
  • Stefan Kalkhof,
  • Janina Müller-Deile,
  • Peter A. van Veelen,
  • Yassene Mohammed,
  • Elke Hammer,
  • Lis Arend,
  • Klaudia Adamowicz,
  • Tanja Laske,
  • Anne Hartebrodt,
  • Tobias Frisch,
  • Chen Meng,
  • Julian Matschinske,
  • Julian Späth,
  • Richard Röttger,
  • Veit Schwämmle,
  • Stefanie M. Hauck,
  • Stefan F. Lichtenthaler,
  • Axel Imhof,
  • Matthias Mann,
  • Christina Ludwig,
  • Bernhard Kuster,
  • Jan Baumbach,
  • Olga Zolotareva

摘要

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.