Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK.
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Genome Biol. 2019 Dec 13;20(1):273. doi: 10.1186/s13059-019-1865-2.
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.
使用池化进行多份样本的多重单细胞 RNA-seq 分析是一种很有前途的实验设计，它可以提高通量，同时克服批次变化。为了重建每个细胞的样本身份，已经提出了在池中的样本之间分离的遗传变异作为细胞去复用的天然条形码。现有的去复用策略依赖于池化样本中完整基因型数据的可用性，这限制了这些方法的适用性，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，我们在这里提出了 Vireo，这是一种计算效率高的贝叶斯模型，可以从池化实验设计中对单细胞数据进行去复用。独特的是，我们的模型可以应用于只有部分或没有基因型信息可用的情况下。使用基于合成混合物的池和真实数据的结果，我们证明了 Vireo 的稳健性，并说明了复用实验设计在常见表达分析中的实用性。
