Journal
GENOME BIOLOGY
Volume 21, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13059-020-02083-3
Keywords
Single cell; Transcriptome; Deep learning; Semi-supervised learning; Imputation
Funding
- National Key R&D Program of China [2019YFA0904401, 2016YFC0901604]
- National Natural Science Foundation of China [31829002]
- China Postdoctoral Science Foundation [2019 M663220]
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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.
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