Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios.

中文翻译：

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Souporcell：按基因型无参考基因型的单细胞RNA-seq数据的强大聚类。

对于包含基因型混合物的样品，无论是天然的还是实验性组合，都需要对单细胞RNA测序（scRNA-seq）数据进行反卷积的方法。跨供体的多路复用是一种流行的实验设计，可以避免批量效应，降低成本并提高双线检测。通过使用在scRNA-seq读数中检测到的变体，可以将细胞分配给其原始供体，并鉴定可能具有高度相似的转录谱的交叉基因型双峰，从而排除了通过转录谱进行检测的可能性。可以使用更细微的交叉基因型变异污染来估算环境RNA的量。环境RNA是在液滴分配之前由细胞裂解引起的，并且是scRNA-seq分析的重要混杂因素。在这里，我们开发southorcell，一种使用在scRNA-seq读数中检测到的遗传变异将细胞聚类的方法。我们证明了它在基因型聚类，双峰检测和环境RNA估计上均具有很高的准确性，这在一系列具有挑战性的场景中得到了证明。