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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.15220 |
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| _version_ | 1866918088970076160 |
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| author | Burankova, Yuliya Abele, Miriam Bakhtiari, Mohammad von Törne, Christine Barth, Teresa Schweizer, Lisa Giesbertz, Pieter Schmidt, Johannes R. Kalkhof, Stefan Müller-Deile, Janina van Veelen, Peter A Mohammed, Yassene Hammer, Elke Arend, Lis Adamowicz, Klaudia Laske, Tanja Hartebrodt, Anne Frisch, Tobias Meng, Chen Matschinske, Julian Späth, Julian Röttger, Richard Schwämmle, Veit Hauck, Stefanie M. Lichtenthaler, Stefan Imhof, Axel Mann, Matthias Ludwig, Christina Kuster, Bernhard Baumbach, Jan Zolotareva, Olga |
| author_facet | Burankova, Yuliya Abele, Miriam Bakhtiari, Mohammad von Törne, Christine Barth, Teresa Schweizer, Lisa Giesbertz, Pieter Schmidt, Johannes R. Kalkhof, Stefan Müller-Deile, Janina van Veelen, Peter A Mohammed, Yassene Hammer, Elke Arend, Lis Adamowicz, Klaudia Laske, Tanja Hartebrodt, Anne Frisch, Tobias Meng, Chen Matschinske, Julian Späth, Julian Röttger, Richard Schwämmle, Veit Hauck, Stefanie M. Lichtenthaler, Stefan Imhof, Axel Mann, Matthias Ludwig, Christina Kuster, Bernhard Baumbach, Jan Zolotareva, Olga |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15220 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt Burankova, Yuliya Abele, Miriam Bakhtiari, Mohammad von Törne, Christine Barth, Teresa Schweizer, Lisa Giesbertz, Pieter Schmidt, Johannes R. Kalkhof, Stefan Müller-Deile, Janina van Veelen, Peter A Mohammed, Yassene Hammer, Elke Arend, Lis Adamowicz, Klaudia Laske, Tanja Hartebrodt, Anne Frisch, Tobias Meng, Chen Matschinske, Julian Späth, Julian Röttger, Richard Schwämmle, Veit Hauck, Stefanie M. Lichtenthaler, Stefan Imhof, Axel Mann, Matthias Ludwig, Christina Kuster, Bernhard Baumbach, Jan Zolotareva, Olga Quantitative Methods Machine Learning 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. |
| title | Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2407.15220 |