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Sorek, G.* ; Haim, Y.* ; Chalifa-Caspi, V.* ; Lazarescu, O.* ; Ziv-Agam, M.* ; Hagemann, T. ; Nono Nankam, P.A. ; Blüher, M. ; Liberty, I.F.* ; Dukhno, O.* ; Kukeev, I.* ; Yeger-Lotem, E.* ; Rudich, A.* ; Levin, L.*

sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human adipose tissues.

iScience 27:110368 (2024)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Deconvolution algorithms mostly rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to estimate tissues’ cell-type composition, with performance accuracy validated on deposited databases. Adipose tissues’ cellular composition is highly variable, and adipocytes can only be captured by single-nucleus RNA-sequencing (snRNA-seq). Here we report the development of sNucConv, a Scaden deep-learning-based deconvolution tool, trained using 5 hSAT and 7 hVAT snRNA-seq-based data corrected by (i) snRNA-seq/bulk RNA-seq highly correlated genes and (ii) individual cell-type regression models. Applying sNucConv on our bulk RNA-seq data resulted in cell-type proportion estimation of 15 and 13 cell types, with accuracy of R = 0.93 (range: 0.76–0.97) and R = 0.95 (range: 0.92–0.98) for hVAT and hSAT, respectively. This performance level was further validated on an independent set of samples (5 hSAT; 5 hVAT). The resulting model was depot specific, reflecting depot differences in gene expression patterns. Jointly, sNucConv provides proof-of-concept for producing validated deconvolution models for tissues un-amenable to scRNA-seq.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Biocomputational Method ; Classification Of Bioinformatical Subject ; Integrative Aspects Of Cell Biology ; Machine Learning ; Transcriptomics; Obesity; Inflammation; Genes
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 2589-0042
e-ISSN 2589-0042
Zeitschrift iScience
Quellenangaben Band: 27, Heft: 7, Seiten: , Artikelnummer: 110368 Supplement: ,
Verlag Elsevier
Verlagsort Amsterdam ; Bosten ; London ; New York ; Oxford ; Paris ; Philadelphia ; San Diego ; St. Louis
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Institute for Metabolism, Obesity and Vascular Research (HI-MAG)
POF Topic(s) 30201 - Metabolic Health
Forschungsfeld(er) Helmholtz Diabetes Center
PSP-Element(e) G-506501-001
Förderungen Israel Science Foundation
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
Chan Zuckerberg Initiative Foundation
DOI 10.1016/j.isci.2024.110368
WOS ID 001267014100001
Scopus ID 85197515001
PubMed ID 39071890
Erfassungsdatum 2024-07-17
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