Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt

TL;DR

FedProt is a novel privacy-preserving federated learning tool for collaborative differential protein abundance analysis, achieving accuracy comparable to pooled data analysis while maintaining data privacy across multiple centers.

Contribution

Introduces FedProt, the first federated learning-based method for privacy-preserving multi-center proteomics analysis using secret sharing techniques.

Findings

FedProt achieves accuracy equivalent to centralized analysis.

Negligible differences in p-values compared to pooled data.

Meta-analysis methods diverge significantly from centralized results.

Abstract

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.