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arboreto skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

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.

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Install the arboreto skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/arboreto ~/.claude/skills/arboreto
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Arboreto

Overview

Arboreto is a Python library from Aerts Lab for inferring gene regulatory networks (GRNs) from gene expression data. It parallelizes tree-based ensemble regression (GRNBoost2, GENIE3) with Dask 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. Primary downstream consumer: 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/basicgrninference.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.cscmatrix (rows = observations, columns = genes). For array/matrix inputs, pass genenames 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:

Filtering strategy:

  • limit=N at inference time (return top N links globally)
  • Post-hoc importance threshold (e.g., > 0.5)
  • Top links per target via groupby('target')
  • Statistical significance testing (permutation tests, external tools)

Integration with pySCENIC

Arboreto powers the GRN inference step in pySCENIC. pySCENIC 0.11+ passes sparse expression matrices to grnboost2 / genie3; pySCENIC 0.12+ defaults to arboretowithmultiprocessing.py (no Dask) for compatibility — use standalone arboreto when you need Dask scaling.

# Standalone: infer co-expression modules before pySCENIC cisTarget pruning
from arboreto.algo import grnboost2

network = grnboost2(expression_data=expression_df, tf_names=tf_list, limit=5000)

# Downstream: pySCENIC ctx pruning, regulon definition, AUCell (see pySCENIC docs)

Convert AnnData to a DataFrame for arboreto directly:

expression_df = adata.to_df()  # cells x genes

Reproducibility

Always set a seed for reproducible results:

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