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Stock, M. ; Losert, C. ; Zambon, M. ; Popp, N. ; Lubatti, G. ; Hörmanseder, E. ; Heinig, M. ; Scialdone, A.

Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.

Mol. Syst. Biol. 21, 214-230 (2025)
Verlagsversion Forschungsdaten DOI PMC
Creative Commons Lizenzvertrag
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Many studies have used single-cell RNA sequencing (scRNA-seq) to infer gene regulatory networks (GRNs), which are crucial for understanding complex cellular regulation. However, the inherent noise and sparsity of scRNA-seq data present significant challenges to accurate GRN inference. This review explores one promising approach that has been proposed to address these challenges: integrating prior knowledge into the inference process to enhance the reliability of the inferred networks. We categorize common types of prior knowledge, such as experimental data and curated databases, and discuss methods for representing priors, particularly through graph structures. In addition, we classify recent GRN inference algorithms based on their ability to incorporate these priors and assess their performance in different contexts. Finally, we propose a standardized benchmarking framework to evaluate algorithms more fairly, ensuring biologically meaningful comparisons. This review provides guidance for researchers selecting GRN inference methods and offers insights for developers looking to improve current approaches and foster innovation in the field.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Gene Regulatory Network Inference ; Graph Learning ; Prior Knowledge ; Single-cell Multiomics ; Single-cell Transcriptomics; Accessibility; Chromatin; Mouse
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 1744-4292
e-ISSN 1744-4292
Zeitschrift Molecular Systems Biology
Quellenangaben Band: 21, Heft: 3, Seiten: 214-230 Artikelnummer: , Supplement: ,
Verlag EMBO Press
Verlagsort Campus, 4 Crinan St, London, N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epigenetics and Stem Cells (IES)
Institute of Computational Biology (ICB)
Institute of Functional Epigenetics (IFE)
POF Topic(s) 30204 - Cell Programming and Repair
30205 - Bioengineering and Digital Health
30203 - Molecular Targets and Therapies
Forschungsfeld(er) Stem Cell and Neuroscience
Enabling and Novel Technologies
Helmholtz Diabetes Center
PSP-Element(e) G-506290-001
G-506200-001
G-503800-001
G-553500-001
G-502800-001
Förderungen DZHK partner site project
Helmholtz Association under the joint research school "Munich School for Data Science
Joachim Herz Stiftung Add-on Fellowship for Interdisciplinary Life Science
Helmholtz Association
Deutsche Forschungsgemeinschaft
Chan Zuckerberg Foundation
Helmholtz Association ((sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic))
DOI 10.1038/s44320-025-00088-3
WOS ID 001418883900001
Scopus ID 85217749208
PubMed ID 39939367
Erfassungsdatum 2025-04-08
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