Deep learning-based advances and applications for single-cell RNA-sequencing data analysis

Abstract 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...

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Bibliographic Details
Published in:Briefings in bioinformatics Vol. 23; no. 1
Main Authors: Bao, Siqi, Li, Ke, Yan, Congcong, Zhang, Zicheng, Qu, Jia, Zhou, Meng
Format: Journal Article
Language:English
Published: England Oxford University Press 17.01.2022
Oxford Publishing Limited (England)
Subjects:
Biomedical materials
Computer applications
Data Analysis
Deep Learning
Gene Expression Profiling - methods
Gene sequencing
Quality control
Ribonucleic acid
RNA
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
single-cell RNA-sequencing
deep learning
bioinformatics
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary:Abstract 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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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbab473