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flatironinstitute / inferelator

Task-based gene regulatory network inference using single-cell or bulk gene expression data conditioned on a prior network.
BSD 2-Clause "Simplified" License
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inference network

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Inferelator 3.0

PyPI version CI codecov Documentation Status

The Inferelator 3.0 is a package for gene regulatory network inference that is based on regularized regression. It is an update of the Inferelator 2.0, which is an update of the original Inferelator It is maintained by the Bonneau lab in the Systems Biology group of the Flatiron Institute.

This repository is the actively developed inferelator package for python. It works for both single-cell and bulk transcriptome experiments. Includes AMuSR (Castro et al 2019), elements of InfereCLaDR (Tchourine et al 2018), and single-cell workflows (Jackson et al 2020).

We recommend installing this package from PyPi using python -m pip install inferelator. If running locally, also install joblib by python -m pip install joblib for parallelization. If running on a cluster, also install dask by python -m pip install dask[complete] dask_jobqueue for dask-based parallelization.

This package can also be installed from the github repository. Clone the inferelator GitHub repository and run python setup.py install.

Documentation is available at https://inferelator.readthedocs.io, and basic workflows for Bacillus subtilis and Saccharomyces cerevisiae are included with a tutorial.

All current example data and scripts are available from Zenodo DOI.