Dropouts distort gene expression and misclassify cell types in single-cell transcriptome. Although imputation may improve gene expression and downstream analysis to some degree, it also inevitably introduces false signals. We develop DISC, a novel deep learning network with semi-supervised learning to infer gene structure and expression obscured by dropouts. Compared with seven state-of-the-art imputation approaches on ten real-world datasets, we show that DISC consistently outperforms the other approaches. Its applicability, scalability, and reliability make DISC a promising approach to recover gene expression, enhance gene and cell structures, and improve cell type identification for sparse scRNA-seq data.

中文翻译：

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DISC：使用半监督深度学习对单细胞转录组的基因表达和结构进行高度可扩展且准确的推断


缺失会扭曲基因表达并错误分类单细胞转录组中的细胞类型。尽管插补可以在一定程度上改善基因表达和下游分析，但它也不可避免地引入错误信号。我们开发了 DISC，这是一种新颖的深度学习网络，具有半监督学习功能，可以推断被 dropout 掩盖的基因结构和表达。与十个真实世界数据集上的七种最先进的插补方法相比，我们表明 DISC 始终优于其他方法。其适用性、可扩展性和可靠性使 DISC 成为恢复基因表达、增强基因和细胞结构以及改善稀疏 scRNA-seq 数据的细胞类型识别的有前途的方法。