  Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to
analyze gene expression at the resolution of individual cells, providing
unprecedented insights into cellular heterogeneity and complex biological
systems. This paper reviews various advanced computational and machine learning
techniques tailored for the analysis of scRNA-seq data, emphasizing their roles
in different stages of the data processing pipeline.
