Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference
AuthorHuang, Y; McCarthy, DJ; Stegle, O
Source TitleGenome Biology
University of Melbourne Author/sMcCarthy, Davis
AffiliationSchool of Mathematics and Statistics
Document TypeJournal Article
CitationsHuang, Y., McCarthy, D. J. & Stegle, O. (2019). Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference. GENOME BIOLOGY, 20 (1), https://doi.org/10.1186/s13059-019-1865-2.
Access StatusOpen Access
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.
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