Systems Biology of Infectious Diseases Workshop:

Identification and Characterization of T cell Subsets from Single Cell Data

Goal:

We will introduce a general workflow to deal with single cell RNA-seq data from peripheral blood mononuclear cells, with a goal on identifying and characterizing T cell clusters.

Timeline

1. Before workshop:
   1. Read README.md and install prerequisites. Alternatively head to binder link:
      1. Download GitHub repository: git clone [email protected]:watronfire/CViSB_Workshop_TCells.git
      2. Change directory into downloaded GitHub repository.
      3. Install python requirements: pip3 install -r requirements.txt
      4. Install R requirements: Rscript install.R
   2. Download Dataset: Anndata h5ad file, Zanini et al. PNAS 2018 (Dengue infection vs. Healthy). Available as download from Anderson Lab's Google Cloud, or on flashdrive during workshop. Move this file into the data folder of GitHub repository.
   3. Load notebook: jupyter notebook Workshop_Notebook.ipynb
2. Tutorial Schedule (1 hour):
   1. Preparation: Dataset loading and exploring.
   2. Pre-processing: Quality control and filtering of data
   3. Normalization: Estimation of cell size with scran pooled normalization
   4. Visualization: Determine highly variable genes and perform dimensionality reduction
   5. T cell Selection: Identify CD3-positive populations using differentially expressed genes
   6. T cell Clustering and Characterization: Clustering and differential gene expression analysis
   7. Functional Comparison: Link differences in T cell response to Dengue infection status

Requirements

To run locally:

• Python packages: Scanpy, Pandas, NumPy, SciPy, Matplotlib, Seaborn, rp2, and leidenalg
• R packages: Scran To run Binder:
• Functional, modern web browser