Single-cell RNA sequencing (scRNA-seq) has transformed our ability to study cellular heterogeneity, but it also presents major computational challenges, including doublet detection, batch effect correction, clustering, and cell type annotation. In this presentation, I will briefly introduce the workflow of scRNA-seq and highlight recent advances addressing these issues. Among these, I will emphasize our work on developing a robust cell-typing tool. Beyond computational innovations, I will also present our application of scRNA-seq to breast cancer, focusing on different cancer subtypes. By analyzing both immune and non-immune cell populations, we identified therapy-responsive subgroups and characterized the tumor microenvironment under standard and experimental treatments. Together, these studies demonstrate how combining novel computational methods with applied scRNA-seq analyses can yield deeper biological insights and support the advancement of precision oncology.