A mixture of attention experts-embedded flow-based generative model to create synthetic cells in single-cell RNA-Seq datasets

Turgut Ögme, S.S., Aydin, N. orcid.org/0000-0003-0022-2247 and Kurt, Z. orcid.org/0000-0003-3186-8091 (2025) A mixture of attention experts-embedded flow-based generative model to create synthetic cells in single-cell RNA-Seq datasets. PLOS Computational Biology, 21 (10). e1013525. ISSN: 1553-734X

Abstract

Metadata

Item Type: Article
Authors/Creators:
Editors:
  • Osmanbeyoglu, H.U.
Copyright, Publisher and Additional Information:

© 2025 Turgut Ögme et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/4.0/

Keywords: Gene expression; Marker genes; Preprocessing; Biomacromolecule-ligand interactions; Principal component analysis; Cell communication; Quality control; Statistical data
Dates:
  • Submitted: 8 January 2025
  • Accepted: 15 September 2025
  • Published (online): 6 October 2025
  • Published: 6 October 2025
Institution: The University of Sheffield
Academic Units: ?? Sheffield.IJC ??
The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Date Deposited: 07 Oct 2025 15:07
Last Modified: 07 Oct 2025 15:07
Status: Published
Publisher: Public Library of Science (PLoS)
Refereed: Yes
Identification Number: 10.1371/journal.pcbi.1013525
Open Archives Initiative ID (OAI ID):

Export

Statistics