Deep Learning Applications in Single-Cell Multi-Omics Analysis: A Review

Abstract

The emergence of single-cell sequencing technologies has transformed our comprehension of cellular diversity, providing unprecedented insights into individual cell states within complex tissues. Coupled with the power of deep learning, these technologies offer new avenues for interpreting the vast and intricate data generated. This review examines the foundational principles and practical applications of deep learning in single-cell omics, highlighting its role in cell annotation, gene regulatory network identification, and multi-omics integration. It also explores the challenges and future directions for integrating deep learning with single-cell analysis.