Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review

Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discusspotential directions for improvements in deep learning algorithms for scRNA-seq data analysis.

Automated summary

Single-cell RNA sequencing (scRNA-seq) is widely used to quantify the gene expression profile of thousands of single cells simultaneously. Deep learning techniques have emerged as a promising tool for scRNA-seq data analysis, allowing for the extraction of informative and compact features from noisy and high-dimensional data. This review surveys recent developments in deep learning for scRNA-seq data analysis, highlights key advancements made by deep learning in the analysis pipeline, and discusses the benefits and challenges of applying deep learning to scRNA-seq data.