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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)
Verlagsversion DOI PMC
Open Access Hybrid
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1465-7392
e-ISSN 1476-4679
Zeitschrift Nature Cell Biology
Quellenangaben Band: 25, Heft: 2, Seiten: 337-350 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort Heidelberger Platz 3, Berlin, 14197, Germany
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen 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
DOI 10.1038/s41556-022-01072-x
WOS ID 000924642500001
Scopus ID 85147371442
PubMed ID 36732632
Erfassungsdatum 2023-02-11
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