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：按基因型对单细胞 RNA-seq 数据进行稳健聚类，无需参考基因型。


对于含有混合基因型的样本（无论是天然的还是实验组合的），需要采用解卷积单细胞 RNA 测序 (scRNA-seq) 数据的方法。供体之间的多重检测是一种流行的实验设计，可以避免批次效应、降低成本并改善双峰检测。通过使用 scRNA-seq 读取中检测到的变异，可以将细胞分配给其来源供体，并识别可能具有高度相似转录谱的跨基因型双联体，从而排除转录谱检测。更微妙的跨基因型变异污染可用于估计环境 RNA 的量。环境 RNA 是由液滴分配前的细胞裂解引起的，是 scRNA-seq 分析的重要混杂因素。在这里，我们开发了 souporcell，这是一种使用 scRNA-seq 读数中检测到的遗传变异对细胞进行聚类的方法。我们证明它在基因型聚类、双联体检测和环境 RNA 估计方面实现了高精度，正如在一系列具有挑战性的场景中所证明的那样。