Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference

Common genetic variants have been used as natural barcodes to demultiplex cells from pooled RNA-seq experiments. Existing demultiplexing strategies rely on access to complete genotype data from the set of pooled samples, which greatly 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 data from pooled experimental designs. Our model can be applied to dataset with partial or without any genotype information of the pooled samples. Using simulations and results on real data, we demonstrate the robustness of our model and illustrate the utility of multi-sample experimental designs for common expression analyses.