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

Abstract

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.