Metadata-Version: 2.1
Name: bengrn
Version: 1.0.1
Summary: benchmarking gene regulatory networks
Keywords: scRNAseq,GRN,gene regulatory networks,benchmark,comp bio,scPrint
Author: jkobject
Requires-Python: >=3.10,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: anndata
Requires-Dist: arboreto
Requires-Dist: ctxcore
Requires-Dist: dask-expr
Requires-Dist: decoupler
Requires-Dist: grnndata
Requires-Dist: numpy
Requires-Dist: omnipath
Requires-Dist: pandas
Requires-Dist: pyscenic
Requires-Dist: scdataloader
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: seaborn
Description-Content-Type: text/markdown

# bengrn

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Benchmark your gene regulatory networks inference algorithm (from scRNAseq or bulk RNAseq dataset) with BenGRN

The package is supposed to work with [GRnnData](https://cantinilab.github.io/GRnnData/) and only uses biological ground truth datasets.

It can run Genie3 & pyscenic on your data as a comparison

It has 3 main different types of key ground truth data to compare your GRN to:

- Mc Calla et al.'s ChIP+Perturb ground truth
- omnipath's literature curated ground truth
- genome wide perturb seq 's dataset

You can find the documentation [here](https://www.jkobject.com/benGRN/)

## Install it from PyPI

```bash
pip install bengrn
```

### Install it locally and run the notebooks:

```bash
git clone https://github.com/jkobject/benGRN.git
pip install -e benGRN
```

## Usage

```py
from bengrn import BenGRN
from bengrn import some_test_function

# a GRN in grnndata formart
grndata

BenGRN(grndata).do_tests()
#or
some_test_function(grndata)
```

see more in the notebooks in the docs folder or in the [documentation](https://jkobject.com/benGRN/)

## Development

Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.

Awesome Benchmark of Gene Regulatory Networks created by @jkobject
