Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7DQ, UK.
Oxford University Hospital NHS Trust, John Radcliffe Hospital, Oxford, OX3 7DQ, UK.
Nat Commun. 2018 Mar 22;9(1):1187. doi: 10.1038/s41467-018-03608-y.
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.
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强大工具。通过应用无监督聚类算法，可以鉴定出具有共同基因表达谱的细胞亚群。然而，技术差异是 scRNA-seq 的一个主要混杂因素，尤其是因为不可能在同一细胞上复制测量。在这里，我们提出了 BEARscc，这是一种使用 RNA Spike-in 对照来模拟特定实验技术重复的工具。BEARscc 可与多种现有的聚类算法配合使用，以评估聚类对技术变化的稳健性。我们证明该工具可改善细胞的无监督分类，并有助于对单细胞 RNA-seq 实验进行生物学解释。
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