ArticleScience China. Life sciences2023
Privacy-preserving integration of multiple institutional data for single-cell type identification with scPrivacy.
Article in Science China. Life sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- scKSFD: federated distillation model with knowledge sharing for cell type classification of clinical transcriptome data.BMC bioinformatics · 2026Article
- scMFF: a machine learning framework with multiple feature fusion strategies for cell type identification.BMC bioinformatics · 2025Article
- Multi-slice spatial transcriptome domain analysis with SpaDo.Genome biology · 2024Article
- scFed: federated learning for cell type classification with scRNA-seq.Briefings in bioinformatics · 2023Article
- Single cell sequencing revealed the mechanism of CRYAB in glioma and its diagnostic and prognostic value.Frontiers in immunology · 2023Article
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Authors and funding
10 authors.
Funding
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Abstract
The rapid accumulation of large-scale single-cell RNA-seq datasets from multiple institutions presents remarkable opportunities for automatically cell annotations through integrative analyses. However, the privacy issue has existed but being ignored, since we are limited to access and utilize all the reference datasets distributed in different institutions globally due to the prohibited data transmission across institutions by data regulation laws. To this end, we present scPrivacy, which is the first and generalized automatically single-cell type identification prototype to facilitate single cell annotations in a data privacy-preserving collaboration manner. We evaluated scPrivacy on a comprehensive set of publicly available benchmark datasets for single-cell type identification to stimulate the scenario that the reference datasets are rapidly generated and distributed in multiple institutions, while they are prohibited to be integrated directly or exposed to each other due to the data privacy regulations, demonstrating its effectiveness, time efficiency and robustness for privacy-preserving integration of multiple institutional datasets in single cell annotations.
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