Metadata-Version: 2.1
Name: rapids_singlecell
Version: 0.8.1
Summary: running single cell analysis on Nvidia GPUs
Project-URL: Documentation, https://rapids-singlecell.readthedocs.io
Project-URL: Source, https://github.com/scverse/rapids_singlecell
Author: Severin Dicks
License: MIT License
        
        Copyright (c) 2022 Severin Dicks
        
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License-File: LICENSE
Requires-Python: >=3.8
Requires-Dist: anndata>=0.7.4
Requires-Dist: decoupler>=1.3.2
Requires-Dist: matplotlib>=3.4
Requires-Dist: natsort
Requires-Dist: numpy>=1.17.0
Requires-Dist: pandas>=1.0
Requires-Dist: scanpy>=1.9.3
Requires-Dist: scikit-misc>=0.1.3
Requires-Dist: scipy>=1.4
Requires-Dist: seaborn
Requires-Dist: statsmodels>=0.12.0
Requires-Dist: tqdm
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Requires-Dist: readthedocs-sphinx-ext; extra == 'doc'
Requires-Dist: scanpydoc[theme,typehints]>=0.9.4; extra == 'doc'
Requires-Dist: sphinx-copybutton; extra == 'doc'
Requires-Dist: sphinx>=4.5.0; extra == 'doc'
Provides-Extra: rapids
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Provides-Extra: test
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Description-Content-Type: text/markdown

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# rapids-singlecell

## Background
This library is designed to accelerate single cell data analysis by utilizing the capabilities of GPU computing. Drawing inspiration from both the [scanpy](https://github.com/scverse/scanpy) library by Theis lab and the [rapids-single-cell-examples](https://github.com/clara-parabricks/rapids-single-cell-examples) library from Nvidia's RAPIDS team, it introduces GPU-optimized versions of their functions. While aiming to remain compatible with the original codes, the library's primary objective is to blend the computational strength of GPUs with the user-friendly nature of the scverse ecosystem.

## Installation
### Conda
The easiest way to install *rapids-singlecell* is to use one of the *yaml* file provided in the [conda](https://github.com/scverse/rapids_singlecell/tree/main/conda) folder. These *yaml* files install everything needed to run the example notbooks and get you started.
```
conda env create -f conda/rsc_rapids_23.04.yml
# or
mamba env create -f conda/rsc_rapids_23.08.yml
```
### PyPI
As of version 0.4.0 *rapids-singlecell* is now on PyPI.
```
pip install rapids-singlecell
```
The default installer doesn't cover RAPIDS nor cupy. Information on how to install RAPIDS & cupy can be found [here](https://rapids.ai/start.html).

If you want to use RAPIDS new PyPI packages, the whole library with all dependencies can be install with:
````
pip install 'rapids-singlecell[rapids]' --extra-index-url=https://pypi.nvidia.com
````
Please note that the RAPIDS PyPI packages are still considered experimental. It is important to ensure that the CUDA environment is set up correctly so that RAPIDS and Cupy can locate the necessary libraries.

To view a full guide how to set up a fully functioned single cell GPU accelerated conda environment visit [GPU_SingleCell_Setup](https://github.com/Intron7/GPU_SingleCell_Setup)

## Documentation

Please have a look through the [documentation](https://rapids-singlecell.readthedocs.io/en/latest/)


## Citation

If you use this code, please cite: [![DOI](https://zenodo.org/badge/364573913.svg)](https://zenodo.org/badge/latestdoi/364573913)

Please also consider citing: [rapids-single-cell-examples](https://zenodo.org/badge/latestdoi/265649968) and  [scanpy](https://doi.org/10.1186/s13059-017-1382-0)

In addition to that please cite the methods' original research articles in the [scanpy documentation](https://scanpy.readthedocs.io/en/latest/references.html)

If you use the accelerated decoupler functions please cite [decoupler](https://doi.org/10.1093/bioadv/vbac016)
