Single cell RNA sequencing technologies have been rapidly developed in recent years. The 10x droplet-based single cell RNA sequencing technology makes it possible to profile gene expression of tens of thousands of cells per sample. Standard analysis of single cell RNA sequencing data usually includes quality control, normalization, dimension reduction, cell clustering and differential expression analysis. Removing the potential doublets is also recommended in the the standard analysis. Multiple samples at different stages can be integrated together, and the downstream trajectory analysis can be performed to study the cell development process. Here, we further extend the downstream analysis to time course analysis taking advantage of the pseudotime inferred from trajectory analysis. In this workflow, we use single cell RNA sequencing data of mouse mammary gland epithelium at five different stages to demonstrate the standard analysis and integration analysis with Seurat, the doublet prediction with scDblFinder, the ternary plot analysis using signature genes, the trajectory analysis with monocle3, and time course analysis using pseudo-bulk data with edgeR.