Objectives
- Understand and learn the main steps of scRNA-seq data analysis, up to marker gene detection and cell type identification.
- Be able to use the Seurat package on a small test dataset, from count matrices to clustering and cluster annotation.
- Understand the basics of the analysis in order to apply them to one’s own dataset.

Course Content
I. Introduction
- Single-cell RNA sequencing
- From raw sequencing data to count matrices
- Software tools

II. Preprocessing of the expression matrix (Theory and Practice)
- Quality control
- Normalization
- Dimensionality reduction (HVG, PCA, UMAP)
- Detection of expression biases

III. Annotation (Theory and Practice)
- Clustering
- Marker genes
- Cell type identification
- Analysis of marker gene lists with the R package ClusterProfiler

IV. Practical Workshop “Bring your own data”
- Semi-autonomous execution of primary analysis on learners’ own data