Single-cell messenger RNA sequencing (scRNA-seq) has emerged as a powerful tool to study cellular heterogeneity within complex tissues. Subpopulations of cells with common gene expression profiles can be identified by applying unsupervised clustering algorithms. However, technical variance is a major confounding factor in scRNA-seq, not least because it is not possible to replicate measurements on the same cell. Here, we present BEARscc, a tool that uses RNA spike-in controls to simulate experiment-specific technical replicates. BEARscc works with a wide range of existing clustering algorithms to assess the robustness of clusters to technical variation. We demonstrate that the tool improves the unsupervised classification of cells and facilitates the biological interpretation of single-cell RNA-seq experiments.

中文翻译：

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BEARscc使用模拟技术复制来确定单细胞集群的鲁棒性。

单细胞信使RNA测序（scRNA-seq）已成为研究复杂组织内细胞异质性的有力工具。具有常见基因表达谱的细胞亚群可以通过应用无监督聚类算法来鉴定。但是，技术差异是scRNA-seq中的主要混杂因素，尤其是因为不可能在同一细胞上重复进行测量。在这里，我们介绍BEARscc，这是一种使用RNA插入控件模拟特定于实验的技术复制品的工具。BEARscc与广泛的现有聚类算法一起使用，以评估聚类对技术变化的鲁棒性。我们证明该工具改善了细胞的无监督分类，并促进了单细胞RNA-seq实验的生物学解释。