gretapy 0.0.1

Package to evaluate gene regulatory networks (GRNs).

gretapy - Evaluation and analysis of Gene Regulatory Networks (GRNs)

gretapy is a comprehensive framework for benchmarking and evaluating gene regulatory networks (GRNs) inferred from single-cell multiome (RNA+ATAC) data. It provides a systematic evaluation across four complementary dimensions: prior knowledge validation (TF markers, known TF-TF interactions, reference networks), genomic annotations (TF binding sites, cis-regulatory elements, chromatin-gene links), predictive performance (pathway enrichment, expression correlation), and mechanistic validation (perturbation forecasting, Boolean network simulations). The package includes built-in GRN inference methods, curated benchmark datasets, and visualization tools to facilitate rigorous comparison of network inference approaches.

Getting started

Please refer to the documentation, in particular, the API documentation.

Installation

You need to have Python 3.11 or newer installed on your system. If you don't have Python installed, we recommend installing uv.

There are several alternative options to install gretapy:

1. Install the latest stable release from PyPI with minimal dependancies:

pip install gretapy

2. Install the latest stable full release from PyPI with extra dependancies:

pip install gretapy[full]

3. Install the latest stable version from conda-forge using mamba or conda:

mamba create -n=greta conda-forge::gretapy

4. Install the latest development version:

pip install git+https://github.com/saezlab/gretapy.git@main

Release notes

See the changelog.

Contact

For questions and help requests, you can reach out in the scverse discourse. If you found a bug, please use the issue tracker.

Citation

Badia-i-Mompel P., Casals-Franch R., Wessels L., Müller-Dott S., Trimbour R., Yang Y., Ramirez Flores R.O., Saez-Rodriguez J. 2024. Comparison and evaluation of methods to infer gene regulatory networks from multimodal single-cell data. bioRxiv. https://doi.org/10.1101/2024.12.20.629764