This repository contains the SwitchTFI python package as presented in SwitchTFI: identifying transcription factors driving cell differentiation (doi).
SwitchTFI can be used in two different ways:
1. Clone the GitHub Repository
Preprocessed example datasets and previously inferred GRNs are available via the switchtfi.data module.
# Clone the repository
git clone git@github.com:bionetslab/SwitchTFI.git
# Navigate to the project directory
cd SwitchTFI
# Create and activate the Conda environment from the .yml file
conda env create -f switchtfi.yml
conda activate switchtfi2. Install directly from Conda
This is the simplest way to install the package. Datasets and GRNs are not included in this installation, but a usage example is included in the repository under /docs/example.ipynb.
conda install -c conda-forge -c bioconda switchtfiAll relevant functions are documented with docstrings. For details on function parameters and available options please refer to those.
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Repository clone:
Preprocessed scRNA-seq datasets and previously inferred GRNs can be accessed via the
switchtfi.datamodule. An example workflow is provided in example.py, please also see the comments there for additional information. To select an example dataset set the flag to ery, beta, or alpha.# Run SwitchTFI analyses with the preprocessed scRNA-seq data and a previously inferred GRN as an input python example.py -d ery -
Conda installation:
Datasets are not included, but a usage example is provided in the repository under /docs/example.ipynb. Data preprocessing is demonstrated there as well.
This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.
If you use SwitchTFI in your research or publication, please cite the corresponding publication:
https://doi.org/10.1186/s13059-025-03876-0
Paul Martini - paul.martini@fau.de
Project Link: https://github.com/bionetslab/SwitchTFI