State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
Genome Biol. 2020 Jul 10;21(1):170. doi: 10.1186/s13059-020-02083-3.
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.
辍学会扭曲单细胞转录组中的基因表达并错误分类细胞类型。尽管插补在某种程度上可以改善基因表达和下游分析，但它也不可避免地引入了错误信号。我们开发了 DISC，这是一种带有半监督学习的新型深度学习网络，可以推断出由辍学现象掩盖的基因结构和表达。在十个真实数据集上与七种最先进的插补方法进行比较，我们表明，DISC 始终优于其他方法。它的适用性、可扩展性和可靠性使其成为一种有前途的方法，可以恢复基因表达、增强基因和细胞结构，并改善稀疏 scRNA-seq 数据的细胞类型识别。
