Emerging deep learning methods for single-cell RNA-seq data analysis

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

References

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COMPLIANCE WITH ETHICS GUIDELINES

The authors Jie Zheng and Ke Wang declare that they have no conflict of interests.

This article is a review article and does not contain any studies with human or animal subjects performed by any of the authors.

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2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature