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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)
Publ. Version/Full Text Research data 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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Gene Selection; Single; Biomart; Atlas
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Journal Nature Methods
Quellenangaben Volume: 22, Issue: 4, Pages: 834-844 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute of Lung Health and Immunity (LHI)
Grants 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