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FedscGen: privacy-preserving federated batch effect correction of single-cell RNA sequencing data
Mohammad Bakhtiari1, Stefan Bonn2,3,4,5, Fabian Theis6,7,8
1Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany. mohammad.bakhtiari@uni-hamburg.de.
Genome Biology
|July 22, 2025
Summary
FedscGen offers a privacy-preserving method for correcting batch effects in single-cell RNA sequencing data. This federated approach ensures secure data sharing and collaboration for improved analysis.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Single-cell RNA sequencing (scRNA-seq) data from clinical samples frequently exhibit batch effects, complicating data integration and analysis.
- Genomic privacy concerns significantly limit the sharing of valuable clinical scRNA-seq datasets.
- Existing methods for batch effect correction may not adequately address privacy requirements for sensitive clinical data.
Purpose of the Study:
- To develop a privacy-preserving, communication-efficient federated method for batch effect correction in scRNA-seq data.
- To enable secure collaboration and data sharing for scRNA-seq analysis while mitigating batch effects.
- To integrate new studies into existing federated learning frameworks for robust batch effect correction.
Main Methods:
- FedscGen, a federated learning framework built upon the scGen model, incorporating secure multiparty computation for enhanced privacy.
- Implementation of federated training and batch effect correction workflows within the FedscGen framework.
- Benchmarking FedscGen against existing methods using diverse scRNA-seq datasets, including the Human Pancreas dataset.
Main Results:
- FedscGen demonstrates competitive performance in batch effect correction, matching the scGen model on key metrics such as NMI, GC, ILF1, ASW_C, kBET, and EBM.
- The method successfully supports federated training and the integration of new datasets, proving its versatility.
- Validation across diverse datasets confirms the efficacy and robustness of FedscGen.
Conclusions:
- FedscGen provides a secure and effective solution for addressing batch effects in clinical scRNA-seq data.
- The privacy-preserving nature of FedscGen facilitates real-world collaboration and data sharing, overcoming genomic privacy barriers.
- As a FeatureCloud app, FedscGen is readily accessible for secure, collaborative scRNA-seq batch effect correction.
