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Hrovatin, K. ; Moinfar, A.A. ; Zappia, L. ; Parikh, S. ; Tejada Lapuerta, A. ; Lengerich, B.* ; Kellis, M.* ; Theis, F.J.

Integrating single-cell RNA-seq datasets with substantial batch effects.

BMC Genomics 26:974 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Integration of single-cell RNA-sequencing (scRNA-seq) datasets is standard in scRNA-seq analysis. Nevertheless, current computational methods struggle to harmonize datasets across systems such as species, organoids and primary tissue, or different scRNA-seq protocols, including single-cell and single-nuclei. Conditional variational autoencoders (cVAE) are a popular integration method, however, existing strategies for stronger batch correction have limitations. Increasing the Kullback-Leibler divergence regularization does not improve integration and adversarial learning removes biological signals. Here, we propose sysVI, a cVAE-based method employing VampPrior and cycle-consistency constraints. We show that sysVI integrates across systems and improves biological signals for downstream interpretation of cell states and conditions.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Adversarial Learning ; Benchmarking ; Data Integration ; Kl Regularization Strength ; Latent Cycle-consistency ; Single-cell Rna Sequencing (scrna-seq) ; Vampprior; Organoids; Retina; Atlas
ISSN (print) / ISBN 1471-2164
e-ISSN 1471-2164
Zeitschrift BMC Genomics
Quellenangaben Band: 26, Heft: 1, Seiten: , Artikelnummer: 974 Supplement: ,
Verlag Bmc
Verlagsort Campus, 4 Crinan St, London N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen Technische Universitt Mnchen (1025)