This is a Python library from Aerts Lab that infers gene regulatory networks from RNA-seq data using tree-based ensemble methods. You get two algorithms: GRNBoost2 (faster, gradient boosting) and GENIE3 (classic random forest). Feed it an expression matrix, get back transcription factor to target gene relationships with importance scores. It uses Dask for parallelization, so you can scale from local cores to clusters for large single-cell datasets. The library is mature but not actively maintained (last PyPI release 2021), and it's the GRN inference engine behind pySCENIC. Main gotcha is you need the if __name__ == '__main__' guard everywhere because of Dask's multiprocessing. Set a seed for reproducibility and filter outputs by importance thresholds.
- skills.sh installs
- 2K
- GitHub stars
- 56.6K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add k-dense-ai/scientific-agent-skills --skill arboreto --agent claude-codeInstalls into .claude/skills of the current project.
Files
Arboreto
When to use
Use Arboreto to rank candidate regulator-target associations from expression measurements. GRNBoost2 fits stochastic gradient boosting regressions; GENIE3 fits random forests. Each target is predicted from candidate regulators, excluding itself. These are observational predictive associations, not proof of direct binding, activation/repression, or causal regulation.
The latest PyPI release checked is 0.1.6 (2021-02-09). Read the Docs still labels its documentation 0.1.5; the current GitHub source contains fixes that are not in the PyPI wheel. Do not assume a successful unpinned installation can run inference. See compatibility and distribution details.
Installation and compatibility
The following exact stack passed dense and CSC-sparse GRNBoost2, dense GENIE3, custom GBM/RF, and wrapper smoke tests on macOS arm64 with Python 3.11.11:
uv venv --python 3.11 .venv-arboreto
uv pip install --python .venv-arboreto/bin/python \
'arboreto==0.1.6' 'dask[complete]==2024.7.1' 'distributed==2024.7.1' \
'numpy==1.26.4' 'pandas==2.2.3' 'scikit-learn==1.5.2' 'scipy==1.13.1'
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- Repository
- github.com/k-dense-ai/scientific-agent-skills
- Category
- AI & Agent Building
- First seen
- Jun 3, 2026
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