Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning
Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning
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DOI：
10.1016/j.cels.2020.05.010
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发表时间：
2020-07-22
期刊：
影响因子：
9.3
通讯作者：
Kelley, David R.
中科院分区：
文献类型：
--
作者：
Bernstein, Nicholas J.;Fong, Nicole L.;Kelley, David R.
单细胞RNA测序（scRNA-seq）测量基因表达使前所未有的高分辨率视图进入细胞状态。然而，目前的方法经常导致两个或更多的细胞共享相同的细胞识别条形码；这些“双线”违反了单细胞技术的基本前提，并可能导致错误的推论。在这里，我们描述了Solo，这是一种半监督深度学习方法，可以比现有方法更准确地识别孪生体。Solo使用变分自编码器嵌入无监督单元，然后在编码器上附加前馈神经网络层，形成有监督分类器。我们训练这个分类器从观测数据中区分模拟的双态。Solo可与实验双元检测方法结合使用，进一步将scRNA-seq数据纯化为真正的单细胞。它可以从https://github.com/calico/solo免费获得。本文的透明同行评议过程记录包含在补充信息中。
Single-cell RNA sequencing (scRNA-seq) measurements of gene expression enable an unprecedented high-resolution view into cellular state. However, current methods often result in two or more cells that share the same cell-identifying barcode; these "doublets" violate the fundamental premise of single-cell technology and can lead to incorrect inferences. Here, we describe Solo, a semi-supervised deep learning approach that identifies doublets with greater accuracy than existing methods. Solo embeds cells unsupervised using a variational autoencoder and then appends a feed-forward neural network layer to the encoder to form a supervised classifier. We train this classifier to distinguish simulated doublets from the observed data. Solo can be applied in combination with experimental doublet detection methods to further purify scRNA-seq data to true single cells. It is freely available from https://github.com/calico/solo. A record of this paper's transparent peer review process is included in the Supplemental Information.