Application of Deep Learning on Single-Cell RNA-sequencing Data Analysis: A Review

TL;DR

This review discusses how deep learning techniques are transforming single-cell RNA-sequencing data analysis by improving feature extraction and addressing challenges in high-dimensional, noisy biological data.

Contribution

It surveys recent deep learning methods applied to scRNA-seq analysis, highlighting advancements, benefits over traditional tools, and future challenges.

Findings

Deep learning enhances feature extraction from noisy scRNA-seq data.

Deep methods improve accuracy in identifying cell types and states.

Challenges include data heterogeneity and model interpretability.

Abstract

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.