期刊
BRIEFINGS IN BIOINFORMATICS
卷 23, 期 1, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab473
关键词
single-cell RNA-sequencing; deep learning; bioinformatics
资金
- National Natural Science Foundation of China [61973240, 62072341]
This study summarizes the recent advances and applications of deep learning-based methods in the analysis of scRNA-seq data and specific tools, and investigates the future perspectives and challenges of deep learning techniques in the appropriate analysis and interpretation of scRNA-seq data.
The rapid development of single-cell RNA-sequencing (scRNA-seq) technology has raised significant computational and analytical challenges. The application of deep learning to scRNA-seq data analysis is rapidly evolving and can overcome the unique challenges in upstream (quality control and normalization) and downstream (cell-, gene- and pathway-level) analysis of scRNA-seq data. In the present study, recent advances and applications of deep learning-based methods, together with specific tools for scRNA-seq data analysis, were summarized. Moreover, the future perspectives and challenges of deep-learning techniques regarding the appropriate analysis and interpretation of scRNA-seq data were investigated. The present study aimed to provide evidence supporting the biomedical application of deep learning-based tools and may aid biologists and bioinformaticians in navigating this exciting and fast-moving area.
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