DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning | Genome Biology | Full Text

DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning | Genome Biology | Full Text

DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning

• Yao He, 
• Hao Yuan, 
• Cheng Wu & 
• Zhi Xie 

Genome Biology volume 21, Article number: 170 (2020) Cite this article

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Abstract

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.