DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors
DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors
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DOI：
10.1016/j.cels.2019.03.003
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发表时间：
2019-04-24
期刊：
影响因子：
9.3
通讯作者：
Gartner, Zev J.
中科院分区：
文献类型：
--
作者：
McGinnis, Christopher S.;Murrow, Lyndsay M.;Gartner, Zev J.
单细胞RNA测序（scRNA-seq）数据通常受到被称为“双链”的技术产物的影响，这限制了细胞通量并导致虚假的生物学结论。在这里，我们提出了一种计算双偶检测工具- doubletfinder -仅使用基因表达数据识别双偶。DoubletFinder根据每个真实细胞在基因表达空间中的接近程度来预测双体，这种接近度是通过平均随机选择的细胞对的转录谱而产生的人工双体。我们首先使用已知双链身份的scRNA-seq数据集来证明DoubletFinder可以识别由转录不同的细胞形成的双链。当这些双链被移除时，对差异表达基因的识别就会增强。其次，我们提供了一种估计DoubletFinder输入参数的方法，允许其在具有不同细胞类型分布的scRNA-seq数据集上应用。最后，我们提出了DoubletFinder应用程序的“最佳实践”，并说明了DoubletFinder对实验验证的具有“杂交”表达特征的肾细胞类型不敏感。
Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as "doublets,'' which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool-DoubletFinder- that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell's proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present "best practices'' for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with "hybrid'' expression features.