AnnData PyTorch Tutorials

Tutorials for using single-cell data with PyTorch. These materials are aimed at two groups: machine learning practitioners new to genomics, and genomics researchers wanting to use deep learning.

View the interactive tutorial website →

Tutorials

Tutorial	Audience	Duration	Open in Colab	What it covers
Basics: Single-cell Data in AnnData format	ML practitioners new to single-cell data	30-45 min		What scRNA-seq data is and how it differs from typical ML datasets
Basics: Machine Learning with PyTorch	Genomics researchers new to ML	45-60 min		PyTorch basics explained using genomics examples
Single-cell data in PyTorch	All participants	45-60 min		Use AnnData's AnnLoader for per-cell classification and regression
Grouped cell populations	Participants interested in sample-level prediction	30-45 min		Model patient-region samples with PyTorch datasets and ggml-ot

Suggested workshop flow: Choose the primer for the domain that is less familiar to you. In a mixed workshop, participants can work through the two primers in parallel, then regroup for Single-cell data in PyTorch. Continue with Grouped cell populations as an optional advanced tutorial.

Local installation

Google Colab requires no local setup. To run the tutorials locally on macOS or Linux:

git clone https://github.com/DaminK/AnnData_PyTorch_Tutorials.git
cd AnnData_PyTorch_Tutorials
python -m venv .venv && .venv/bin/python -m pip install -r requirements.txt
.venv/bin/python -m jupyter lab

The third line creates the virtual environment and installs the dependencies into it in one shell command. Activation is not required. On Windows, replace .venv/bin/python with .venv\Scripts\python.exe.

What you'll learn

These tutorials show you how to:

• Load scRNA-seq data with Scanpy and inspect AnnData objects
• Build supervised models with PyTorch
• Handle sparse, high-dimensional biological data
• Batch cells with AnnData's AnnLoader and model grouped cell populations with ggml-ot
• Evaluate classification and regression workflows

The tutorials include notes explaining genomics concepts for ML practitioners and ML concepts for genomics researchers.

Acknowledgments

These tutorials use Scanpy, AnnData, PyTorch, and ggml-ot. Documentation is built with Jupyter Book.

Additional Resources

Useful links:

• Single-cell best practices
• Scanpy tutorials
• PyTorch tutorials
• AnnLoader documentation - PyTorch batches from AnnData
• ggml-ot - Grouped-cell distribution learning

License

This project is available under the MIT License.