PuSH - Publication Server of Helmholtz Zentrum München

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)
Publ. Version/Full Text Research data 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.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords 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
Journal Genome Biology
Quellenangaben Volume: 26, Issue: 1, Pages: 29, Article Number: 216 Supplement: ,
Publisher BioMed Central
Publishing Place Campus, 4 Crinan St, London N1 9xw, England
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants European Union's Horizon Europe Research and Innovation Programme