Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference.
Authors
Huang, Yuanhua
McCarthy, Davis J
Stegle, Oliver
Publication Date
2019-12-13Journal Title
Genome Biol
ISSN
1474-7596
Publisher
Springer Science and Business Media LLC
Volume
20
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Huang, Y., McCarthy, D. J., & Stegle, O. (2019). Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference.. Genome Biol, 20 (1) https://doi.org/10.1186/s13059-019-1865-2
Abstract
Multiplexed single-cell RNA-seq analysis of multiple samples using pooling is a promising experimental design, offering increased throughput while allowing to overcome batch variation. To reconstruct the sample identify of each cell, genetic variants that segregate between the samples in the pool have been proposed as natural barcode for cell demultiplexing. Existing demultiplexing strategies rely on availability of complete genotype data from the pooled samples, which limits the applicability of such methods, in particular when genetic variation is not the primary object of study. To address this, we here present Vireo, a computationally efficient Bayesian model to demultiplex single-cell data from pooled experimental designs. Uniquely, our model can be applied in settings when only partial or no genotype information is available. Using pools based on synthetic mixtures and results on real data, we demonstrate the robustness of Vireo and illustrate the utility of multiplexed experimental designs for common expression analyses.
Keywords
Method, Multiplexing, Single-cell RNA-seq, Genetic variation, Variational Bayes
Identifiers
s13059-019-1865-2, 1865
External DOI: https://doi.org/10.1186/s13059-019-1865-2
This record's URL: https://www.repository.cam.ac.uk/handle/1810/315043
Rights
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/
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