Single-Cell RNA-seq Analysis in Python Workshop

This workshop introduces single-cell RNA-seq (scRNA-seq) analysis in Python using Scanpy and the classic PBMC 3k dataset. The workshop is delivered as a Jupyter notebook and is designed to be run on Oscar at Brown University via the OpenOnDemand portal.

Notebook

• notebooks/scRNAseq_in_Python.ipynb — Main workshop notebook covering quality control, normalization, dimensionality reduction, clustering, and visualization of scRNA-seq data.

Running the notebook on Oscar via OpenOnDemand

Prerequisites

Package setup: For this workshop we will use a pre-built Apptainer image that already contains all required Python packages (Scanpy, scVI-tools, Harmony, Scrublet, gseapy, and more). No manual package installation is needed when launching via OpenOnDemand as described below.

If you want to run the notebook outside the Apptainer image (e.g. on your own machine or on Oscar without the container), create a virtual environment and install the packages listed in requirements.txt:

On Oscar (load a Python module first):

module load python/3.12.4
python -m venv ~/venvs/singlecell
source ~/venvs/singlecell/bin/activate
pip install -r requirements.txt

On your local machine:

python -m venv singlecell
source singlecell/bin/activate          # Windows: singlecell\Scripts\activate
pip install -r requirements.txt

See the Oscar Python documentation for more details on managing Python environments on Oscar.

1. Clone this repository to your Oscar home or data directory:

   git clone https://github.com/compbiocore/singlecell_python_workshop.git

2. Obtain the Apptainer image (.sif file) — see notebooks/README.md for the pull command. For this workshop we are hosting the image on oscar for all attendees at /oscar/data/shared/workshops/ccv_scrnaseq_2026.sif.

Launching Jupyter via OpenOnDemand

1. Navigate to the Oscar OpenOnDemand portal: https://ood.ccv.brown.edu/pun/sys/dashboard/batch_connect/sys/bc_ccv_jupyter_singularity/session_contexts/new

2. Under Interactive Apps → Expert GUIs, select Jupyter Notebook for Apptainer Images.

3. Fill in the form fields as follows:

   Field	Value
   Path to apptainer image	Full path to the apptainer image file (.sif) file: /oscar/data/shared/workshops/ccv_scrnaseq_2026.sif
   Extra Jupyter Args	--notebook-dir=<path/to/cloned/repo>, e.g. --notebook-dir=/oscar/home/<username>/singlecell_python_workshop
   Partition	Leave blank to use the default partition
   Number of cores	1
   Memory per job	50G
   Number of GPUs	1 (no GPU used for this workshop, but value must be at least 1)
   Condo account	Leave blank unless you are using a condo
   Number of hours	2 (increase if you need more time)
   Additional Data Path	Path to any extra data on Oscar you need accessible inside the container, e.g. /oscar/data/<your-data-dir>

4. Click Launch.

5. Once the session starts (you will see it appear under My Interactive Sessions), click Connect to Jupyter.

6. In the Jupyter file browser, open notebooks/scRNAseq_in_Python.ipynb and run the cells.