/arboreto

name: arboreto
description: Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
license: BSD-3-Clause license
metadata:
  version: "1.1"
  skill-author: K-Dense Inc.

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:

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

For standard GRN inference workflows including:

• Input data preparation (Pandas DataFrame or NumPy array)
• Running inference with GRNBoost2 or GENIE3
• Filtering by transcription factors
• Output format and interpretation

**See**: `references/basic_inference.md`

**Use the ready-to-run script**: `scripts/basic_grn_inference.py` for standard inference tasks:

python scripts/basic_grn_inference.py expression_data.tsv output_network.tsv --tf-file tfs.txt --seed 777 --limit 5000

2. Algorithm Selection

Arboreto provides two algorithms:

**GRNBoost2 (Recommended)**:

• Fast gradient boosting-based inference
• Optimized for large datasets (10k+ observations)
• Default choice for most analyses

**GENIE3**:

• Random Forest-based inference
• Original multiple regression approach
• Use for comparison or validation

Quick comparison:

from arboreto.algo import grnboost2, genie3

# Fast, recommended
network_grnboost = grnboost2(expression_data=matrix)

# Classic algorithm
network_genie3 = genie3(expression_data=matrix)

**For detailed algorithm comparison, parameters, and selection guidance**: `references/algorithms.md`

3. Distributed Computing

Scale inference from local multi-core to cluster environments:

**Local (default)** - Uses all available cores automatically:

network = grnboost2(expression_data=matrix)

**Custom local client** - Control resources:

from distributed import LocalCluster, Client

local_cluster = LocalCluster(n_workers=10, memory_limit='8GB')
client = Client(local_cluster)

network = grnboost2(expression_data=matrix, client_or_address=client)

client.close()
local_cluster.close()

**Cluster computing** - Connect to remote Dask scheduler:

from distributed import Client

client = Client('tcp://scheduler:8786')
network = grnboost2(expression_data=matrix, client_or_address=client)

**For cluster setup, performance optimization, and large-scale workflows**: `references/distributed_computing.md`

Installation

uv pip install arboreto

Conda (Bioconda):

conda install -c bioconda arboreto

**Dependencies** (from upstream `requirements.txt`): `dask[complete]`, `distributed`, `numpy`, `pandas`, `scikit-learn`, `scipy`

**Input formats**: pandas DataFrame, dense `numpy.ndarray`, or sparse `scipy.sparse.csc_matrix` (rows = observations, columns = genes). For array/matrix inputs, pass `gene_names` explicitly.

Common Use Cases

Single-Cell RNA-seq Analysis

import pandas as pd
from arboreto.algo import grnboost2

if __name__ == '__main__':
    # Load single-cell expression matrix (cells x genes)
    sc_data = pd.read_csv('scrna_counts.tsv', sep='\t')

    # Infer cell-type-specific regulatory network
    network = grnboost2(expression_data=sc_data, seed=42)

    # Filter high-confidence links
    high_confidence = network[network['importance'] > 0.5]
    high_confidence.to_csv('grn_high_confidence.tsv', sep='\t', index=False)

Bulk RNA-seq with TF Filtering

from arboreto.utils import load_tf_names
from arboreto.algo import grnboost2

if __name__ == '__main__':
    # Load data
    expression_data = pd.read_csv('rnaseq_tpm.tsv', sep='\t')
    tf_names = load_tf_names('human_tfs.txt')

    # Infer with TF restriction
    network = grnboost2(
        expression_data=expression_data,
        tf_names=tf_names,
        seed=123
    )

    network.to_csv('tf_target_network.tsv', sep='\t', index=False)

Comparative Analysis (Multiple Conditions)

from arboreto.algo import grnboost2

if __name__ == '__main__':
    # Infer networks for different conditions
    conditions = ['control', 'treatment_24h', 'treatment_48h']

    for condition in conditions:
        data = pd.read_csv(f'{condition}_expression.tsv', sep='\t')
        network = grnboost2(expression_data=data, seed=42)
        network.to_csv(f'{condition}_network.tsv', sep='\t', index=False)

Output Interpretation

Arboreto returns a DataFrame with regulatory links:

| Column | Description | |--------|-------------| | `TF` | Transcription factor (regulator) | | `target` | Target gene | | `i