Deep Learning for Clustering Single-cell RNA-seq Data


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Abstract

The development of single-cell RNA sequencing (scRNA-seq) technology provides an excellent opportunity to explore cell heterogeneity and diversity. With the growing application of scRNA-seq data, many computational clustering methods have been developed to further uncover cell subgroups, and cell dynamics at the group level. Due to the characteristics of high dimension, high sparsity and high noise of the scRNA-seq data, it is challenging to use traditional clustering methods. Fortunately, deep learning technologies characterize the properties of scRNA-seq data well and provide a new perspective for data analysis. This work reviews the most popular computational clustering methods and tools based on deep learning technologies, involving comparison, data collection, code acquisition, results evaluation, and so on. In general, such a presentation points out some progress and limitations of the existing methods and discusses the challenges and directions for further research, which may give new insight to address a broader range of new challenges in dealing with single-cell sequencing data and downstream analysis.

About the authors

Yuan Zhu

School of Automation, China University of Geosciences

Email: info@benthamscience.net

Litai Bai

School of Automation, China University of Geosciences

Email: info@benthamscience.net

Zilin Ning

School of Mathematics and Physics, China University of Geosciences

Email: info@benthamscience.net

Wenfei Fu

School of Mathematics and Physics, China University of Geosciences

Email: info@benthamscience.net

Jie Liu

School of Mathematics and Physics, China University of Geosciences

Email: info@benthamscience.net

Linfeng Jiang

School of Automation, China University of Geosciences

Email: info@benthamscience.net

Shihuang Fei

School of Automation, China University of Geosciences

Email: info@benthamscience.net

Shiyun Gong

School of Automation, China University of Geosciences

Email: info@benthamscience.net

Lulu Lu

School of Mathematics and Physics, China University of Geosciences

Email: info@benthamscience.net

Minghua Deng

School of Mathematical Sciences, Peking University

Email: info@benthamscience.net

Ming Yi

School of Mathematics and Physics, China University of Geosciences

Author for correspondence.
Email: info@benthamscience.net

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