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Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects
View ORCID ProfileFlorian Buettner, Naruemon Pratanwanich, John C. Marioni, Oliver Stegle
doi: https://doi.org/10.1101/087775
Florian Buettner
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD Hinxton, Cambridge, UK
Naruemon Pratanwanich
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD Hinxton, Cambridge, UK
John C. Marioni
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD Hinxton, Cambridge, UK
2Cancer Research UK Cambridge Institute, Cambridge, United Kingdom
3Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
Oliver Stegle
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD Hinxton, Cambridge, UK
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Posted November 15, 2016.
Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects
Florian Buettner, Naruemon Pratanwanich, John C. Marioni, Oliver Stegle
bioRxiv 087775; doi: https://doi.org/10.1101/087775
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