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.

中文翻译：

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Vireo：无基因型参考的合并单细胞 RNA-seq 数据的贝叶斯解复用

使用合并对多个样本进行多重单细胞 RNA-seq 分析是一种很有前景的实验设计，可提供更高的通量，同时允许克服批次差异。为了重建每个细胞的样本识别，在池中的样本之间分离的遗传变异已被提议作为细胞解复用的天然条形码。现有的多路分解策略依赖于从汇集样本中获得完整基因型数据的可用性，这限制了此类方法的适用性，特别是当遗传变异不是主要研究对象时。为了解决这个问题，我们在此介绍 Vireo，这是一种计算效率高的贝叶斯模型，用于从汇集的实验设计中分离单细胞数据。独特的是，当只有部分或没有基因型信息可用时，我们的模型可以应用于设置。