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Bakhtiari, M.* ; Bonn, S.* ; Theis, F.J. ; Zolotareva, O.* ; Baumbach, J.*

FedscGen: Privacy-preserving federated batch effect correction of single-cell RNA sequencing data.

Genome Biol. 26, 29:216 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Single-cell RNA-seq data from clinical samples often suffer from batch effects, but data sharing is limited due to genomic privacy concerns. We present FedscGen, a privacy-preserving communication-efficient federated method built upon the scGen model, enhanced with secure multiparty computation. FedscGen supports federated training and batch effect correction workflows, including the integration of new studies. We benchmark FedscGen across diverse datasets, showing competitive performance-matching scGen on key metrics like NMI, GC, ILF1, ASW_C, kBET, and EBM on the Human Pancreas dataset. Published as a FeatureCloud app, FedscGen enables secure, real-world collaboration for scRNA-seq batch effect correction.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Batch Effect Correction ; Federated Learning ; Generative Models ; Privacy-preserving Computation ; Secure Multiparty Computation (smpc) ; Single-cell Rna-seq; Atlas; Map
ISSN (print) / ISBN 1474-760X
e-ISSN 1465-6906
Zeitschrift Genome Biology
Quellenangaben Band: 26, Heft: 1, Seiten: 29, Artikelnummer: 216 Supplement: ,
Verlag BioMed Central
Verlagsort Campus, 4 Crinan St, London N1 9xw, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen European Union's Horizon Europe Research and Innovation Programme