# GRnnData
[](https://codecov.io/gh/cantinilab/GRnnData)
[](https://github.com/cantinilab/GRnnData/actions/workflows/main.yml)
[](https://badge.fury.io/py/benGRN)
[](https://pepy.tech/project/GRnnData)
[](https://pepy.tech/project/GRnnData)
[](https://pepy.tech/project/GRnnData)
[](https://img.shields.io/github/issues/cantinilab/GRnnData)
[](https://github.com/psf/black)
[](https://doi.org/10.1101/2024.07.29.605556)
<img src="./grnndata.png" width="300"/>
GRnnData works similarly to anndata. The goal was to use the .varm/.varp of anndata to store the GRN data associated with a dataset and have a formal way to work with GRNs.
GRnnData is a subclass of anndata.AnnData, it enforces only that a .varp exists for the anndata
GRnnData also contains multiple helper functions to work with GRNs in scRNAseq like compute_connectivities, get_centrality, compute_cluster, enrichment, metrics, etc. but also accessing the grn with .grn, .targets, .regulators,...
The package has been designed together with the [scPRINT paper](https://doi.org/10.1101/2024.07.29.605556) and [model](https://github.com/cantinilab/scPRINT), and the GRN benchmarking tool [BenGRN](https://github.com/jkobject/BenGRN)
## Install it from PyPI
```bash
pip install grnndata
```
### Install it locally and run the notebooks:
```bash
git clone https://github.com/cantinilab/GRnnData.git
pip install -e GRnnData
```
## Usage
```py
from grnndata import GRNAnnData, read_h5ad
from grnndata import utils
grn = np.random.rand(10,10)
adata = anndata.AnnData(X=np.random.rand(10,10))
grn = GRNAnnData(adata, grn=grn)
print(grn) #shows the number of elements
grn.varp['GRN'] or grn.grn #shows the GRN
subgrn = grn.get(['gene1', 'gene2']) #only gets some elements from the GRN
subgrn.targets #shows the target connections
subgrn.plot() # displays the network
subgrn.write_h5ad('grn.h5ad') #writes it
read_h5ad('grn.h5ad') #reads it
# or use some utils functions like:
# compute_connectivities
# get_centrality
# compute_cluster
# enrichment
# metrics
# see more in the documentation
utils.some_function(grn)
```
GRnnData works similarly to anndata. Learn more about usages in the documentation and its notebooks in the [docs](https://cantinilab.github.io/GRnnData/).
usage examples can be seen in the example notebooks of:
- [scPRINT](https://www.jkobject.com/scPRINT/notebooks/cancer_usecase/)
- [BenGRN](https://www.jkobject.com/benGRN/notebooks/bench_omni_genie3/)
### How do I do if I generate a GRN per cell type?
In this context, we recommend creating a grnndata per cell type. This will allow you to store the GRN data in the .varm of the grnndata and have a formal way to work with GRNs.
### How do I do if I generate a GRN per cell?
In this context, we recommend trying to merge them across a similar group of cells in some way and storing uncertainty or variance in the GRN and then creating a grnndata across this group of cells
## Development
Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.
Awesome gene regulatory network enhanced anndata created by @jkobject and @r-trimbour
Raw data
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"description": "# GRnnData\n\n[](https://codecov.io/gh/cantinilab/GRnnData)\n[](https://github.com/cantinilab/GRnnData/actions/workflows/main.yml)\n[](https://badge.fury.io/py/benGRN)\n[](https://pepy.tech/project/GRnnData)\n[](https://pepy.tech/project/GRnnData)\n[](https://pepy.tech/project/GRnnData)\n[](https://img.shields.io/github/issues/cantinilab/GRnnData)\n[](https://github.com/psf/black)\n[](https://doi.org/10.1101/2024.07.29.605556)\n\n<img src=\"./grnndata.png\" width=\"300\"/>\n\nGRnnData works similarly to anndata. The goal was to use the .varm/.varp of anndata to store the GRN data associated with a dataset and have a formal way to work with GRNs.\n\nGRnnData is a subclass of anndata.AnnData, it enforces only that a .varp exists for the anndata\n\nGRnnData also contains multiple helper functions to work with GRNs in scRNAseq like compute_connectivities, get_centrality, compute_cluster, enrichment, metrics, etc. but also accessing the grn with .grn, .targets, .regulators,...\n\nThe package has been designed together with the [scPRINT paper](https://doi.org/10.1101/2024.07.29.605556) and [model](https://github.com/cantinilab/scPRINT), and the GRN benchmarking tool [BenGRN](https://github.com/jkobject/BenGRN)\n## Install it from PyPI\n\n```bash\npip install grnndata\n```\n\n### Install it locally and run the notebooks:\n\n```bash\ngit clone https://github.com/cantinilab/GRnnData.git\npip install -e GRnnData\n```\n\n## Usage\n\n```py\nfrom grnndata import GRNAnnData, read_h5ad\nfrom grnndata import utils\n\ngrn = np.random.rand(10,10)\nadata = anndata.AnnData(X=np.random.rand(10,10))\n\ngrn = GRNAnnData(adata, grn=grn)\n\nprint(grn) #shows the number of elements\ngrn.varp['GRN'] or grn.grn #shows the GRN\nsubgrn = grn.get(['gene1', 'gene2']) #only gets some elements from the GRN\nsubgrn.targets #shows the target connections\nsubgrn.plot() # displays the network\n\nsubgrn.write_h5ad('grn.h5ad') #writes it\nread_h5ad('grn.h5ad') #reads it\n\n# or use some utils functions like:\n# compute_connectivities\n# get_centrality\n# compute_cluster\n# enrichment\n# metrics\n# see more in the documentation\n\nutils.some_function(grn)\n```\n\nGRnnData works similarly to anndata. Learn more about usages in the documentation and its notebooks in the [docs](https://cantinilab.github.io/GRnnData/).\n\nusage examples can be seen in the example notebooks of:\n- [scPRINT](https://www.jkobject.com/scPRINT/notebooks/cancer_usecase/)\n- [BenGRN](https://www.jkobject.com/benGRN/notebooks/bench_omni_genie3/)\n\n### How do I do if I generate a GRN per cell type?\n\nIn this context, we recommend creating a grnndata per cell type. This will allow you to store the GRN data in the .varm of the grnndata and have a formal way to work with GRNs.\n\n### How do I do if I generate a GRN per cell?\n\nIn this context, we recommend trying to merge them across a similar group of cells in some way and storing uncertainty or variance in the GRN and then creating a grnndata across this group of cells\n\n## Development\n\nRead the [CONTRIBUTING.md](CONTRIBUTING.md) file.\n\nAwesome gene regulatory network enhanced anndata created by @jkobject and @r-trimbour\n",
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