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

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

GRETA logo

Tests Documentation

Issues Coverage Stars

PyPI Downloads month Downloads all

Conda version Conda downloads

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.

GRETA graphical abstract

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

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

pip install gretapy[full]

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

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

  1. 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