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Hetzel, L. ; Fischer, D.S. ; Günnemann, S.* ; Theis, F.J.

Graph representation learning for single cell biology.

Curr. Opin. Syst. Biol. 28:100347 (2021)
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Open Access Hybrid
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Single cell RNA sequencing measures gene expression at an unprecedented resolution and scale and allows the analysis of cellular phenotypes which was not possible before. In this context, graphs occur as a natural representation of the system - both as gene-centric and cell-centric. However, many advances in machine learning on graphs are not yet harnessed in models on single-cell data. Taking the inference of cell types or gene interactions as examples, graph representation learning has a wide applicability to both cell and gene graphs. Recent advances in spatial molecular profiling additionally put graph-learning in the focus of attention due the innate resemblance of spatial information to spatial graphs. We argue that graph embedding techniques have great potential for various applications across single cell biology. Here, we discuss how graph representation learning maps to current models and concepts used in single cell biology and formalise overlaps to developments in graph-based deep learning.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 2452-3100
e-ISSN 2452-3100
Zeitschrift Current Opinion in Systems Biology
Quellenangaben Band: 28, Heft: , Seiten: , Artikelnummer: 100347 Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam
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 Bundesministerium für Bildung und Forschung
Helmholtz Association's Initiative and Networking Fund
Helmholtz AI
DOI 10.1016/j.coisb.2021.05.008
Erfassungsdatum 2022-08-31
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