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 .

With documentation dependencies

# Using uv
uv sync --extra docs

# Using pip
pip install -e ".[docs]"

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