Unknown

Dataset Information

0

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


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.

SUBMITTER: He Y 

PROVIDER: S-EPMC7353747 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

He Yao Y   Yuan Hao H   Wu Cheng C   Xie Zhi Z  

Genome biology 20200710 1


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  ...[more]

Similar Datasets

2019-11-13 | GSE140262 | GEO
| S-EPMC8563931 | biostudies-literature
| S-EPMC4074792 | biostudies-literature
| PRJNA589061 | ENA
| S-EPMC9307817 | biostudies-literature
| S-EPMC6550282 | biostudies-literature
| S-EPMC7551840 | biostudies-literature
| S-EPMC6550175 | biostudies-literature
| S-EPMC3956069 | biostudies-literature
| S-EPMC7351101 | biostudies-literature