  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 development of complex organisms and improved
our understanding of disease states, such as cancer, diabetes, and COVID, among
others. 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, 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 analysis tools. Finally, we
summarize the challenges in current deep learning approaches faced within
scRNA-seq data and discuss potential directions for improvements in deep
algorithms for scRNA-seq data analysis.
