Metadata-Version: 2.4
Name: multivelovae
Version: 0.1.0
Summary: Velocity inference from multi-lineage, multi-omic, and multi-sample single-cell data
Author: Chen Li, Yichen Gu
Maintainer-email: Chen Li <chlseven@umich.edu>
License-Expression: BSD-3-Clause
Project-URL: Homepage, https://github.com/welch-lab/MultiVeloVAE
Keywords: bioinformatics,RNA velocity,single-cell,multi-omic,variational autoencoder,deep generative model,batch correction,bayesian inference,trajectory inference,ordinary differential equation
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: anndata>=0.8.0
Requires-Dist: hnswlib>=0.6.2
Requires-Dist: igraph>=0.10.4
Requires-Dist: ipywidgets
Requires-Dist: leidenalg
Requires-Dist: loess>=2.1.2
Requires-Dist: matplotlib>=3.3.0
Requires-Dist: multivelo>=0.1.2
Requires-Dist: numpy>=1.17
Requires-Dist: pandas>=0.23
Requires-Dist: scanpy>=1.5
Requires-Dist: scikit-learn>=1.2.2
Requires-Dist: scipy>=1.4.1
Requires-Dist: scvelo>=0.2.3
Requires-Dist: seaborn>=0.10.0
Requires-Dist: torch>=1.8.0
Requires-Dist: tqdm
Requires-Dist: umap-learn
Provides-Extra: dev
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Requires-Dist: scikit-misc; extra == "dev"
Dynamic: license-file

# MultiVeloVAE - Velocity inference from multi-lineage, multi-omic, and multi-sample single-cell data
## Package Installation
The package depends on several popular packages in computational biology and machine learning, including [scanpy](https://scanpy.readthedocs.io/en/stable/), [scVelo](https://scvelo.readthedocs.io/en/stable/), [PyTorch](https://pytorch.org/), and [scikit-learn](https://scikit-learn.org/stable/). We suggest using a GPU to accelerate the training process.

To install the MultiVeloVAE package through PyPI:
```
pip install multivelovae
```
And import the package inside python:
```python
import multivelovae as vv
```

## Package Usage
Please feel free to test this method on our previously published 10X Multiome datasets. See https://multivelo.readthedocs.io/en/latest/MultiVelo_Demo.html. The example of running the mouse brain dataset is located in [paper-notebooks](https://github.com/welch-lab/MultiVeloVAE/tree/main/paper-notebooks). Alternatively, you can apply the same training and analysis steps on our single-sample HSPC dataset for which we provide the AnnData objects directly in [figshare](https://multivelo.readthedocs.io/en/latest/MultiVelo_Fig5.html). Expected runtimes can be found inside each notebook.

[This file](https://github.com/welch-lab/MultiVeloVAE/blob/main/paper-notebooks/reproducible_package_versions.txt) lists the versions of packages used to generate manuscript figures.

## TODO
bioconda
readthedocs
