Skill

/arboreto

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.

One skill from scientific-agent-skills.

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$ npx -y skills add K-Dense-AI/scientific-agent-skills --skill arboreto --agent claude-code

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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.

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Stars31,545

Forks3,146

LanguagePython

LicenseMIT

Ships with scientific-agent-skills

SKILL.md

arboreto.SKILL.md

---
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": "1.0", "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:
```bash
uv pip install arboreto
```

Basic GRN inference:
```python
import pandas as pd
from arboreto.algo import grnboost2