De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters.

Autor:	Oubounyt M; Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.; Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany., Elkjaer ML; Department of Neurology, Odense University Hospital, Odense, Denmark.; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark.; Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark., Laske T; Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany., Grønning AGB; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark., Moeller MJ; Heisenberg Chair of Preventive and Translational Nephrology, Department of Nephrology, Rheumatology and Clinical Immunology, RWTH Aachen University, Aachen, Germany., Baumbach J; Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.; Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
Jazyk:	angličtina
Zdroj:	NAR genomics and bioinformatics [NAR Genom Bioinform] 2023 Mar 03; Vol. 5 (1), pp. lqad018. Date of Electronic Publication: 2023 Mar 03 (Print Publication: 2023).
DOI:	10.1093/nargab/lqad018
Abstrakt:	Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms de novo and report them as small, easily interpretable transcriptional regulatory network modules. We demonstrate that DiNiro is able to uncover novel, relevant, and deep mechanistic models that not just predict but explain differential cellular gene expression programs. DiNiro is available at https://exbio.wzw.tum.de/diniro/. (© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu