Recap

In this tutorial, we walked through the Seurat workflow for single-cell RNA-seq analysis:

1. Data preprocessing: loading the dataset, performing quality control, and filtering out low-quality cells.
2. Dimensional reduction: using PCA for linear dimensional reduction, followed by UMAP for non-linear embedding.
3. Clustering: grouping cells into clusters based on gene expression.
4. Differential expression: identifying biomarkers that distinguish genes in each cluster.
5. Visualization: generating plots to assist the process and interpretation.