scDiffEq: modelling single-cell dynamics using neural differential equations.
Project description
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.
Install the development package:
git clone https://github.com/mvinyard/sc-neural-diffeqs.git; cd ./sc-neural-diffeqs;
pip install -e .
Main API
import scdiffeq as sdq
from neural_diffeqs import NeuralSDE
model = sdq.models.scDiffEq(
adata, func=NeuralSDE(state_size=50, mu_hidden=[400, 400], sigma_hidden=[400, 400])
)
model.fit()
Built on:
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
scdiffeq-0.0.46rc3.tar.gz
(55.0 kB
view hashes)
Built Distribution
Close
Hashes for scdiffeq-0.0.46rc3-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2ad5e79e81c8abe37f2a71cd8b92d011b16609f339b5d6b0f11b3e5a0e7230d2 |
|
MD5 | 91a7900948e0f09be047c3a417ea00fe |
|
BLAKE2b-256 | daec4ef63d7c4833f1f4bd4bb12b9c62b3484ac457b81140d47fdaddc07ef9ef |