Open AccessEmerging deep learning methods for single‐cell RNA‐seq data analysis
Author(s) -
Zheng Jie,
Wang Ke
Publication year - 2019
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-019-0189-2
Subject(s) - deep learning , generative grammar , artificial intelligence , computer science , generative adversarial network , adversarial system , machine learning , data science , rna seq , biology , gene , biochemistry , gene expression , transcriptome
Deep learning is making major breakthrough in several areas of bioinformatics. Anticipating that this will occur soon for the single‐cell RNA‐seq data analysis, we review newly published deep learning methods that help tackle computational challenges. Autoencoders are found to be the dominant approach. However, methods based on deep generative models such as generative adversarial networks (GANs) are also emerging in this area.