Arboreto

Arboreto

Overview

Arboreto is a computational library for inferring gene regulatory networks (GRNs) from gene expression data using parallelized algorithms that scale from single machines to multi-node clusters.

Core capability: Identify which transcription factors (TFs) regulate which target genes based on expression patterns across observations (cells, samples, conditions).

Quick Start

Install arboreto:

uv pip install arboreto

Basic GRN inference:

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