Introduction to Single-Cell Transcriptomics

Program Outline & Learning Objectives

The course begins with landmark papers that shaped the field, providing context for modern single-cell technologies and experimental design. Participants then explore key steps in single-cell workflows, from data generation to biological interpretation.

The workshop includes a practical training component using Seurat, where participants perform quality control, dimensionality reduction (UMAP), and cell type annotation on real single-cell datasets. By the end of the course, participants gain both conceptual understanding and practical skills required to analyze and interpret single-cell transcriptomic data.

Course Outline:

Day 1: Basics of scRNA-seq genomics
- Introduction to scRNA-seq and key landmark papers
- Overview of major scRNA-seq technologies: droplet-based, plate-based, and snRNA-seq
 - Experimental workflow: cell isolation, barcoding, UMIs, and sequencing
- Overview of data analysis tools (BeeNetPlus, Seurat) and standard data formats
 
Day 2: Bioinformatics pipeline & preprocessing
- Setting up the analysis environment in Google Colab
- Loading scRNA-seq datasets
- Quality control metrics: counts, genes, mitochondrial reads
- Normalization and log transformation
- Theoretical overview of cell annotation and downstream analysis steps
 
Day 3: Downstream analysis and future directions
- Dimensionality reduction using PCA
- Non-linear embedding method: UMAP
- Annotation of marker genes and coarse cell type
- Visualization and interpretation
- Limitations and common pitfalls