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

Abstract

The joint analysis of multiple samples using single-cell RNA-seq is a promising experimental design, offering both increased throughput while allowing to account for batch variation. To achieve multi-sample designs, genetic variants that segregate between the samples in the pool have been proposed as natural barcodes for cell demultiplexing. Existing demultiplexing strategies rely on access to complete genotype data from the 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 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 simulations based on synthetic mixtures 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.