Deep Learning for Clustering Single-cell RNA-seq Data - Zhu, Yuan; Bai, Litai; Ning, Zilin; Fu, Wenfei; Liu, Jie; Jiang, Linfeng; Fei, Shihuang; Gong, Shiyun; Lu, Lulu; Deng, Minghua; Yi, Ming (Current Bioinformatics, 2024)
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.
Keywords: cell heterogeneity