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Zappia, L. ; Richter, S. ; Ramirez Suastegui, C. ; Kfuri-Rubens, R. ; Vornholz, L. ; Wang, W. ; Dietrich, O. ; Frishberg, A. ; Luecken, M. ; Theis, F.J.

Feature selection methods affect the performance of scRNA-seq data integration and querying.

Nat. Methods 22, 834-844 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
The availability of single-cell transcriptomics has allowed the construction of reference cell atlases, but their usefulness depends on the quality of dataset integration and the ability to map new samples. Previous benchmarks have compared integration methods and suggest that feature selection improves performance but have not explored how best to select features. Here, we benchmark feature selection methods for single-cell RNA sequencing integration using metrics beyond batch correction and preservation of biological variation to assess query mapping, label transfer and the detection of unseen populations. We reinforce common practice by showing that highly variable feature selection is effective for producing high-quality integrations and provide further guidance on the effect of the number of features selected, batch-aware feature selection, lineage-specific feature selection and integration and the interaction between feature selection and integration models. These results are informative for analysts working on large-scale tissue atlases, using atlases or integrating their own data to tackle specific biological questions.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Gene Selection; Single; Biomart; Atlas
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Quellenangaben Band: 22, Heft: 4, Seiten: 834-844 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Lung Health and Immunity (LHI)
Förderungen Helmholtz Information and Data Science Academy to enable a short-term research stay at Helmholtz Munich
Helmholtz Association under the joint research school Munich School for Data Science
Chan Zuckerberg Initiative
Arts in the framework of the Bavarian Research Association 'ForInter' (Interaction of human brain cells)
Bavarian Ministry of Science