Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data
Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data
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DOI：
10.1016/j.cels.2018.11.005
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发表时间：
2019-04-24
期刊：
影响因子：
9.3
通讯作者：
Klein, Allon M.
中科院分区：
文献类型：
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作者：
Wolock, Samuel L.;Lopez, Romain;Klein, Allon M.
单细胞RNA测序已成为研究细胞群体的广泛使用的强大方法。然而，这些方法通常产生多重峰伪影，其中两个或更多个细胞接收相同的条形码，导致杂合转录组。在大多数实验中，多重体占转录组的百分之几，并且可以混淆下游数据分析。在这里，我们提出了单细胞去除双重（Scrublet），一个框架，用于预测多重峰在给定的分析和识别有问题的多重峰的影响。Scrublet通过从数据中模拟多联体并构建最近邻分类器来避免对专家知识或细胞聚类的需要。为了证明这种方法的实用性，我们在几个数据集上测试了Scrublet，这些数据集包括细胞多重态的独立知识。Scrublet可在github.com/AllonKleinLab/scrublet免费下载。
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.