Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt
<|reference_start|>Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt: 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 significant 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 LFQ E.coli experiments and one at three centers from TMT human serum. Evaluations using these datasets confirm that FedProt achieves accuracy equivalent to DEqMS applied to pooled data, with completely negligible absolute differences no greater than $\text{$4 \times 10^{-12}$}$. In contrast, -log10(p-values) computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25-27. FedProt is available as a web tool with detailed documentation as a FeatureCloud App.<|reference_end|>
@article{burankova2024privacy-preserving,
    title={Privacy-Preserving Multi-Center Differential Protein Abundance Analysis
  with FedProt},
    author={Yuliya Burankova, Miriam Abele, Mohammad Bakhtiari, Christine von
  T"orne, Teresa Barth, Lisa Schweizer, Pieter Giesbertz, Johannes R. Schmidt,
  Stefan Kalkhof, Janina M"uller-Deile, Peter A van Veelen, Yassene Mohammed,
  Elke Hammer, Lis Arend, Klaudia Adamowicz, Tanja Laske, Anne Hartebrodt,
  Tobias Frisch, Chen Meng, Julian Matschinske, Julian Sp"ath, Richard
  R"ottger, Veit Schw"ammle, Stefanie M. Hauck, Stefan Lichtenthaler, Axel
  Imhof, Matthias Mann, Christina Ludwig, Bernhard Kuster, Jan Baumbach, Olga
  Zolotareva},
    journal={arXiv preprint arXiv:2407.15220},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15220},
    primaryClass={q-bio.QM cs.LG}
}