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Schmid, K.T.* ; Symeonidi, A. ; Hlushchenko, D.* ; Richter, M.L.* ; Tijhuis, A.E.* ; Foijer, F.* ; Colomé-Tatché, M.

Benchmarking scRNA-seq copy number variation callers.

Nat. Commun. 16:8777 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Copy number variations (CNVs), the gain or loss of genomic regions, are associated with disease, especially cancer. Single cell technologies offer new possibilities to capture within-sample heterogeneity of CNVs and identify subclones relevant for tumor progression and treatment outcome. Several computational tools have been developed to identify CNVs from scRNA-seq data. However, an independent benchmarking of them is lacking. Here, we evaluate six popular methods in their ability to correctly identify ground truth CNVs, euploid cells and subclonal structures in 21 scRNA-seq datasets. We discover dataset-specific factors influencing the performance, including dataset size, the number and type of CNVs in the sample and the choice of the reference dataset. Methods which include allelic information perform more robustly for large droplet-based datasets, but require higher runtime. Furthermore, the methods differ in their additional functionalities. We offer a benchmarking pipeline to identify the optimal method for new datasets, and improve methods' performance.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Read Alignment; Framework; Aberrations; Oncogene; Toolkit
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 16, Heft: 1, Seiten: , Artikelnummer: 8777 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen Dutch Cancer Society
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
Deutsche Forschungsgemeinschaft (German Research Foundation)