SCIM: universal single-cell matching with unpaired feature sets

Stark, Stefan G.; Ficek, Joanna; Locatello, Francesco; Bonilla, Ximena; Chevrier, Stéphane; Singer, Franziska; Tumor Profiler Consortium,; Rätsch, Gunnar; Lehmann, Kjong-Van

doi:10.1093/bioinformatics/btaa843

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Journal Article

SCIM: universal single-cell matching with unpaired feature sets

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Locatello, Francesco
External Organizations;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://doi.org/10.1093/bioinformatics/btaa843
(Publisher version)

https://academic.oup.com/bioinformatics/article/36/Supplement_2/i919/6055906#supplementary-data
(Supplementary material)

https://github.com/ratschlab/scim
(Code)

https://doi.org/10.1101/2020.06.11.146845
(Preprint)

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Citation

Stark, S. G., Ficek, J., Locatello, F., Bonilla, X., Chevrier, S., Singer, F., et al. (2020). SCIM: universal single-cell matching with unpaired feature sets. Bioinformatics, 36(Supplement_2), i919-i927. doi:10.1093/bioinformatics/btaa843.

Cite as: https://hdl.handle.net/21.11116/0000-000D-02EC-7

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