An analysis framework for modeling dynamical single-cell data with neural differential equations, most notably stochastic differential equations allow us to build generative models of single-cell dynamics.
Quickstart
Please see the scDiffEq website for a quickstart notebook: link
Install the development package
Install generally only takes a few seconds.
Using uv (recommended)
git clone https://github.com/scDiffEq/scDiffEq.git; cd ./scDiffEq;
# Install uv if you haven't already: curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync
Using pip
git clone https://github.com/scDiffEq/scDiffEq.git; cd ./scDiffEq;
pip install -e .
Optional dependency groups
| Extra | Contents |
|---|---|
optional |
Lazily-imported integrations: umap-learn, pillow, ipython, psutil, wandb |
docs |
Sphinx and theme packages for building the documentation |
test |
pytest, for running the test suite |
dev |
Jupyter, ipykernel, and pytest for interactive development |
# Using uv
uv sync --extra docs
# Using pip
pip install -e ".[docs]"
Datasets
import scdiffeq as sdq
adata = sdq.datasets.larry()
Datasets are downloaded on first use and cached under
<data_dir>/scdiffeq_data/. They are hosted on Zenodo
(10.5281/zenodo.21947161); downloads
need no authentication and are verified against the record's md5 checksums.
The record redistributes data published by other groups — please cite the original publications, listed on the data page and in the Zenodo record.
Main API
import scdiffeq as sdq
model = sdq.scDiffEq(adata=adata)
model.fit(train_epochs = 1500)
Built on
System requirements
- Developed on linux20.04 and MacOS (with Apple Silicon), using Python3.11.
- Software dependencies are listed in pyproject.toml.
- Tested with NVIDIA GPUs (A100, T4) and Apple Silicon. Most datasets likely only require an NVIDIA Tesla T4 (free in Google Colab).
Reproducibility
- All results described in the manuscript detailing scDiffEq can be reproduced using notebooks in the companion repository: scdiffeq-analyses
Citation
@article{vinyard2025scdiffeq,
title = {Learning cell dynamics with neural differential equations},
author = {Vinyard, Michael E. and Rasmussen, Anders W. and Li, Ruitong
and Klein, Allon M. and Getz, Gad and Pinello, Luca},
journal = {Nature Machine Intelligence},
volume = {7},
number = {12},
pages = {1969--1984},
year = {2025},
doi = {10.1038/s42256-025-01150-3}
}
If you use the packaged datasets, please also cite their original publications (see the data page).
1 maintainer
Metadata
1 maintainer
Release files for scdiffeq 1.1.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scdiffeq-1.1.4.tar.gz | 4.1 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| scdiffeq-1.1.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 4.3 MB
Release files / scdiffeq-1.1.4.tar.gz
| Download URL | scdiffeq-1.1.4.tar.gz |
|---|---|
| Size | 4.1 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / scdiffeq-1.1.4-py3-none-any.whl
| Download URL | scdiffeq-1.1.4-py3-none-any.whl |
|---|---|
| Size | 209.0 kB |
| Tags | Python 3 |
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uv/0.12.5 {"installer":{"name":"uv","version":"0.12.5","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}
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