This class will introduce to senior or graduate level students with hands-on experience to learning routine data analysis for single cell RNA-Seq data, a new type of high-throughput data that detect gene expression transcriptome-wide at the single cell resolution. Students will learn the basic QC, and pre-processing steps including gene and cell filtering, doublet removal. The students will learn data visualization using PCA or UMAP plot. The students will also learn how to conduct clustering, cell type annotation, differential expression, and further down stream analysis such as trajectory analysis, RNA velocity and multi-dataset integration (if there is time). Students will gain both the statistical and computational principles for the analysis, but also hand-on experience, using exemplar and their selected dataset from GEO repository for complete data analysis.