# 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. Supports distributed computation for large-scale datasets.

- Skill: `mkurman/arboreto` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add mkurman/arboreto`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mkurman/arboreto/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: BSD-3-Clause license
- Author: mkurman (https://skillmd.com/u/mkurman)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/mkurman/arboreto

---

--|-------------|
| `TF` | Transcription factor (regulator) |
| `target` | Target gene |
| `importance` | Regulatory importance score (higher = stronger) |

**Filtering strategy**:
- Top N links per target gene
- Importance threshold (e.g., > 0.5)
- Statistical significance testing (permutation tests)

## Integration with pySCENIC

Arboreto is a core component of the SCENIC pipeline for single-cell regulatory network analysis:

```python
# Step 1: Use arboreto for GRN inference
from arboreto.algo import grnboost2
network = grnboost2(expression_data=sc_data, tf_names=tf_list)

# Step 2: Use pySCENIC for regulon identification and activity scoring
# (See pySCENIC documentation for downstream analysis)
```

## Reproducibility

Always set a seed for reproducible results:
```python
network = grnboost2(expression_data=matrix, seed=777)
```

Run multiple seeds for robustness analysis:
```python
from distributed import LocalCluster, Client

if __name__ == '__main__':
    client = Client(LocalCluster())

    seeds = [42, 123, 777]
    networks = []

    for seed in seeds:
        net = grnboost2(expression_data=matrix, client_or_address=client, seed=seed)
        networks.append(net)

    # Combine networks and filter consensus links
    consensus = analyze_consensus(networks)
```

## Troubleshooting

**Memory errors**: Reduce dataset size by filtering low-variance genes or use distributed computing

**Slow performance**: Use GRNBoost2 instead of GENIE3, enable distributed client, filter TF list

**Dask errors**: Ensure `if __name__ == '__main__':` guard is present in scripts

**Empty results**: Check data format (genes as columns), verify TF names match gene names