# bengrn
[](https://codecov.io/gh/jkobject/benGRN)
[](https://github.com/jkobject/benGRN/actions/workflows/main.yml)
[](https://badge.fury.io/py/benGRN)
[](https://pepy.tech/project/benGRN)
[](https://pepy.tech/project/benGRN)
[](https://pepy.tech/project/benGRN)
[](https://img.shields.io/github/issues/jkobject/benGRN)
[](https://github.com/psf/black)
[](https://doi.org/10.1101/2024.07.29.605556)
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 the notebooks in [docs](https://www.jkobject.com/benGRN/):
1. [omnipath](https://www.jkobject.com/benGRN/notebooks/bench_omni_genie3)
2. [genome wide perturb seq](https://www.jkobject.com/benGRN/notebooks/bench_perturbseq_genie3_transp/)
3. [Mc Calla](https://www.jkobject.com/benGRN/notebooks/bench_sroy_genie3_transp/)
## Development
Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.
Awesome Benchmark of Gene Regulatory Networks created by @jkobject
Raw data
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