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Quantitative Biology

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Quant. Biol.    2019, Vol. 7 Issue (4) : 247-254    https://doi.org/10.1007/s40484-019-0189-2
MINI REVIEW
Emerging deep learning methods for single-cell RNA-seq data analysis
Jie Zheng(), Ke Wang
School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
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Abstract

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.
Keywords single-cell      RNA-seq      deep learning      autoencoder     
Corresponding Author(s): Jie Zheng   
Just Accepted Date: 15 November 2019   Online First Date: 17 December 2019    Issue Date: 31 December 2019
 Cite this article:   
Jie Zheng,Ke Wang. Emerging deep learning methods for single-cell RNA-seq data analysis[J]. Quant. Biol., 2019, 7(4): 247-254.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-019-0189-2
https://academic.hep.com.cn/qb/EN/Y2019/V7/I4/247
Names References Years Methods Goals
Lin’s method Lin et al. [48] 2017 PCA-based dimensionality reduction with denoising autoencoders Dimensionality reduction, cell grouping, inference of cell type or state
AutoImpute Talwar et al. [46] 2018 ?Autoencoder-based sparse gene expression matrix imputation ?Deal with dropout events
scVI Lopez et al. [52] 2018 Hierarchical Bayesian model and variational autoencoder Tackle batch correction, library-size bias, dropout, imputation, and visualization, etc.
VASC Wang & Gu [50] 2018 Variational autoencoder ?Model the dropout events and find the nonlinear hierarchical feature representations of the original data
scvis Ding et al. [49] 2018 Variational autoencoder ?Model and visualize structures in scRNA-seq data
scScope Deng et al. [54] 2019 Autoencoder with recurrent structure Conduct batch effect removal, dropout imputation, cell subpopulation identification
DCA ?Eraslan et al. [47] 2019 ?Deep count autoencoder ?with a ZINB loss function ?Remove technical variation to improve downstream analyses
SAVER-X Wang et al. [53] 2019 ?Bayesian hierarchical model and deep autoencoder with transfer learning Leverage existing data to improve the quality of new scRNA-seq datasets
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