Cell Systems ( IF 9.0 ) Pub Date : 2019-04-03 , DOI: 10.1016/j.cels.2018.11.005
Samuel L Wolock 1 , Romain Lopez 2 , Allon M Klein 1
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Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets. Scrublet avoids the need for expert knowledge or cell clustering by simulating multiplets from the data and building a nearest neighbor classifier. To demonstrate the utility of this approach, we test Scrublet on several datasets that include independent knowledge of cell multiplets. Scrublet is freely available for download at github.com/AllonKleinLab/scrublet.
中文翻译:
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Scrublet:单细胞转录组数据中细胞双峰的计算鉴定。
单细胞RNA测序已成为研究细胞群体的一种广泛使用的强大方法。但是,这些方法通常会产生多重伪影,其中两个或多个细胞接收相同的条形码,从而导致了混合转录组。在大多数实验中,多重态占转录组的百分之几,并且可能混淆下游数据分析。在这里,我们介绍双峰单细胞去除剂(Scrublet),该框架用于在给定的分析中预测多重峰的影响并识别有问题的多重峰。Scrublet通过模拟数据中的多重峰并建立最近的邻居分类器,避免了对专业知识或单元聚类的需求。为了演示此方法的实用性,我们在几个包含细胞多态性独立知识的数据集上测试了Scrublet。