arboretolisted

# Arboreto ## Overview Arboreto is a Python library from [Aerts Lab](https://github.com/aertslab/arboreto) for inferring gene regulatory networks (GRNs) from gene expression data. It parallelizes tree-based ensemble regression (GRNBoost2, GENIE3) with [Dask](https://distributed.dask.org/) across local cores or remote clusters. **Core capability**: Identify which transcription factors (TFs) regulate which target genes based on expression patterns across observations (cells, samples, conditions). **Upstream**: PyPI **0.1.6** (2021-02-09, latest). Docs: [arboreto.readthedocs.io](https://arboreto.readthedocs.io/en/latest/). Primary downstream consumer: [pySCENIC](https://github.com/aertslab/pySCENIC). ## Quick Start Install arboreto: ```bash uv pip install arboreto ``` Basic GRN inference: ```python import pandas as pd from arboreto.algo import grnboost2 if __name__ == '__main__': # Load expression data (genes as columns) expression_matrix = pd.read_csv('expression_data.tsv', sep='\t') # Infer regulatory network network = grnboost2(expression_data=expression_matrix) # Save results (TF, target, importance) network.to_csv('network.tsv', sep='\t', index=False, header=False) ``` **Critical**: Always use `if __name__ == '__main__':` guard because Dask spawns new processes. ## Core Capabilities ### 1. Basic GRN Inference For standard GRN inference workflows including: - Input data preparation (Pandas DataFrame or NumPy array) - Running inference wi