# Arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable...

- Repository: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills (3090 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-07-21
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill arboreto -g -a claude-code -y`
Codex: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill arboreto -g -a codex -y`
Cursor: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill arboreto -g -a cursor -y`
Gemini CLI: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill arboreto -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:
```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 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:
```bash
python scripts/basic_grn_inference.py expression_data.tsv output_network.tsv --tf-file tfs.txt --seed 777
```

### 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:
```python
from arboreto.algo import grnboost2, genie3

The rest: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/blob/HEAD/skills/arboreto/SKILL.md