---
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'
  category: life-sciences
  maintainer: Kalaris Labs
---

# 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

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 --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:
```python
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:
```python
network = grnboost2(expression_data=matrix)
```

**Custom local client** - Control resources:
```python
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:
```python
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

```bash
uv pip install arboreto
```

Conda (Bioconda):

```bash
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
```python
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
```python
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)
```python
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 |
| `importance` | Regulatory importance score (higher = stronger) |

**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](https://github.com/aertslab/pySCENIC). pySCENIC 0.11+ passes sparse expression matrices to `grnboost2` / `genie3`; pySCENIC 0.12+ defaults to `arboreto_with_multiprocessing.py` (no Dask) for compatibility — use standalone arboreto when you need Dask scaling.

```python
# 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:

```python
expression_df = adata.to_df()  # cells x genes
```

## 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)

    # Consensus: links recurring across runs (example: mean importance per TF-target pair)
    import pandas as pd
    combined = pd.concat(networks)
    consensus = (
        combined.groupby(['TF', 'target'], as_index=False)['importance']
        .mean()
        .query('importance > 0.5')
    )
```

## 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 (required on Windows/macOS with spawn-based multiprocessing)

**Empty results**: Check data format (genes as columns), verify TF names match column names in the expression matrix

**Sparse data**: Use `scipy.sparse.csc_matrix` and pass matching `gene_names`; supported since arboreto 0.1.6 / pySCENIC 0.11

## Agent operating procedure

1. **Check the environment.** Confirm tool versions, the reference genome/annotation build and the input formats (FASTQ, BAM, VCF, h5ad).
2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. **Run a small version first.** Run the pipeline on a small subset (one sample, one chromosome, a few thousand cells) first.
4. **Execute the full task** using the instructions and references above.
5. **Validate the result.** Check QC metrics, sample identities, genome build consistency and batch effects before interpreting results.
6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

| If this happens | Do this |
|---|---|
| Genome builds or identifiers do not match between inputs | Stop and harmonize (liftover, ID mapping) before continuing. |
| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |
| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |

**Integrity rules**

- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
- Do not interpret biological significance beyond what the statistics support; report multiple-testing correction.
- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

## Related skills

- `cellxgene-census`: Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data.
- `pydeseq2`: Runs differential expression analysis on bulk RNA-seq count data with PyDESeq2, the Python port of DESeq2.
- `bulk-rnaseq`: End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantificat…