Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference
Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference
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DOI：
10.1186/s13059-019-1865-2
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发表时间：
2019-12-13
期刊：
影响因子：
12.3
通讯作者：
Stegle, Oliver
中科院分区：
文献类型：
--
作者：
Huang, Yuanhua;McCarthy, Davis J.;Stegle, Oliver
利用混合对多个样本进行多路复用单细胞RNA - seq分析是一种很有前景的实验设计，它提高了通量，同时能够克服批次差异。为了重建每个细胞的样本身份，已有人提出将混合样本中样本间存在差异的基因变异作为细胞解复用的天然条形码。现有的解复用策略依赖于来自混合样本的完整基因型数据的可用性，这限制了此类方法的适用性，特别是当基因变异不是研究的主要对象时。为了解决这个问题，我们在此提出了Vireo，这是一种计算高效的贝叶斯模型，用于对来自混合实验设计的单细胞数据进行解复用。独特的是，我们的模型可应用于只有部分基因型信息可用或没有基因型信息可用的情况。利用基于合成混合物的混合样本以及真实数据的结果，我们证明了Vireo的稳健性，并说明了多路复用实验设计在常见表达分析中的实用性。
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.