Differential Expression Analysis

This chapter delves into the realm of single-cell RNA sequencing (scRNA-seq) and its pivotal role in unraveling the intricacies of gene expression dynamics. It explores the necessity and motivation behind single-cell differential expression analysis, emphasizing its significance in understanding molecular mechanisms, biomarker discovery, drug development, and disease subtype characterization across various biological disciplines. The chapter provides an overview of the statistical methods and machine learning approaches employed for scRNA-seq differential expression analysis, showcasing their efficacy in handling challenges unique to single-cell data. Through case studies, it elucidates the practical application of machine learning-based methods in predicting disease phenotypes and identifying cell-type-specific differentially expressed genes. By bridging theory with practical application, this chapter equips researchers with the knowledge and tools needed to leverage single-cell data effectively, advancing our understanding of gene expression in complex biological systems.