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Lotfollahi, M. ; Rybakov, S. ; Hrovatin, K. ; Hediyeh-Zadeh, S. ; Talavera Lopez, C.N. ; Misharin, A.V.* ; Theis, F.J.

Biologically informed deep learning to query gene programs in single-cell atlases.

Nat. Cell Biol. 25, 337-350 (2023)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known ‘gene programs’. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
ISSN (print) / ISBN 1465-7392
e-ISSN 1476-4679
Journal Nature Cell Biology
Quellenangaben Volume: 25, Issue: 2, Pages: 337-350 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place Heidelberger Platz 3, Berlin, 14197, Germany
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants Helmholtz Association Initiative and Networking Fund through sparse2big
Helmholtz Association Initiative and Networking Fund through Helmholtz AI
European Union's Horizon 2020 research and innovation program
BMBF
Helmholtz Association under the joint research school 'Munich School for Data Science'
Joachim Herz Stiftung via Add-on Fellowships for Interdisciplinary Life Science