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De Donno, C. ; Hediyeh-Zadeh, S. ; Moinfar, A.A. ; Wagenstetter, M. ; Zappia, L. ; Lotfollahi, M. ; Theis, F.J.

Population-level integration of single-cell datasets enables multi-scale analysis across samples.

Nat. Methods 20, 1683-1692 (2023)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
The increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Quellenangaben Band: 20, Heft: 11, Seiten: 1683-1692 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
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
G-503893-001
Förderungen Joachim Herz Stiftung
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
European Union (ERC)
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
Bavarian Ministry of Science and the Arts in the framework of the Bavarian Research Association 'ForInter'
DOI 10.1038/s41592-023-02035-2
WOS ID 001166704900020
Scopus ID 85173981247
PubMed ID 37813989
Erfassungsdatum 2023-11-28
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