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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
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  • ORCID record for Florian Buettner
Naruemon Pratanwanich
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD Hinxton, Cambridge, UK
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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
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Oliver Stegle
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, CB10 1SD Hinxton, Cambridge, UK
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Abstract

Single-cell RNA-sequencing (scRNA-seq) allows heterogeneity in gene expression levels to be studied in large populations of cells. Such heterogeneity can arise from both technical and biological factors, thus making decomposing sources of variation extremely difficult. We here describe a computationally efficient model that uses prior pathway annotation to guide inference of the biological drivers underpinning the heterogeneity. Moreover, we jointly update and improve gene set annotation and infer factors explaining variability that fall outside the existing annotation. We validate our method using simulations, which demonstrate both its accuracy and its ability to scale to large datasets with up to 100,000 cells. Moreover, through applications to real data we show that our model can robustly decompose scRNA-seq datasets into interpretable components and facilitate the identification of novel sub-populations.

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Posted November 15, 2016.
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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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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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