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

Genome Biology

Cheng Wu¹  Hao Yuan¹  Yao He¹  Zhi Xie¹ 

[1] State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University;

关键词: Single cell;    Transcriptome;    Deep learning;    Semi-supervised learning;    Imputation;   

DOI  :  10.1186/s13059-020-02083-3

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown