Day 1: Introduction to Single-cell RNA-seq Analysis

Day 1: Introduction to Single-cell RNA-seq Analysis#

Environment setup#

conda create -y --name workshop_2025 python=3.10
conda activate workshop_2025
pip install jupyterlab notebook 
conda install -y ipykernel conda-forge::python-annoy
pip install scikit-misc PhenoGraph celltypist palantir scrublet cellrank pydeseq2 liana gseapy rpy2 anndata2ri scanpy python-igraph pyscipopt decoupler pybiomart adjustText
python -m ipykernel install --user --name workshop_2025

Data#

We will work with publicly available data throughout the workshop:

Notebooks#

Morning Session: Basic Analysis Pipeline#

  1. Quality Control and Normalization

    • Learn how to perform quality control on single-cell RNA-seq data

    • Understand and apply different normalization techniques

    • Identify and filter out low-quality cells

  2. Preprocessing

    • Explore data preprocessing steps including feature selection

    • Learn about dimensionality reduction techniques

    • Understand how to handle technical artifacts

  3. Batch Correction

    • Learn about batch effects in single-cell data

    • Apply different batch correction methods

    • Evaluate the effectiveness of batch correction

Afternoon Session: Advanced Analysis#

  1. Downstream Analysis

    • Perform clustering and cell type annotation

    • Conduct differential expression analysis

    • Visualize and interpret results

  2. Trajectory Inference

    • Learn about trajectory inference methods

    • Apply trajectory analysis to understand cell differentiation

    • Visualize and interpret developmental trajectories

Contents