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---
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.
---
# 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
# 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
```
**Dependencies**: scipy, scikit-learn, numpy, pandas, dask, distributed
## 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**:
- 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

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# GRN Inference Algorithms
Arboreto provides two algorithms for gene regulatory network (GRN) inference, both based on the multiple regression approach.
## Algorithm Overview
Both algorithms follow the same inference strategy:
1. For each target gene in the dataset, train a regression model
2. Identify the most important features (potential regulators) from the model
3. Emit these features as candidate regulators with importance scores
The key difference is **computational efficiency** and the underlying regression method.
## GRNBoost2 (Recommended)
**Purpose**: Fast GRN inference for large-scale datasets using gradient boosting.
### When to Use
- **Large datasets**: Tens of thousands of observations (e.g., single-cell RNA-seq)
- **Time-constrained analysis**: Need faster results than GENIE3
- **Default choice**: GRNBoost2 is the flagship algorithm and recommended for most use cases
### Technical Details
- **Method**: Stochastic gradient boosting with early-stopping regularization
- **Performance**: Significantly faster than GENIE3 on large datasets
- **Output**: Same format as GENIE3 (TF-target-importance triplets)
### Usage
```python
from arboreto.algo import grnboost2
network = grnboost2(
expression_data=expression_matrix,
tf_names=tf_names,
seed=42 # For reproducibility
)
```
### Parameters
```python
grnboost2(
expression_data, # Required: pandas DataFrame or numpy array
gene_names=None, # Required for numpy arrays
tf_names='all', # List of TF names or 'all'
verbose=False, # Print progress messages
client_or_address='local', # Dask client or scheduler address
seed=None # Random seed for reproducibility
)
```
## GENIE3
**Purpose**: Classic Random Forest-based GRN inference, serving as the conceptual blueprint.
### When to Use
- **Smaller datasets**: When dataset size allows for longer computation
- **Comparison studies**: When comparing with published GENIE3 results
- **Validation**: To validate GRNBoost2 results
### Technical Details
- **Method**: Random Forest or ExtraTrees regression
- **Foundation**: Original multiple regression GRN inference strategy
- **Trade-off**: More computationally expensive but well-established
### Usage
```python
from arboreto.algo import genie3
network = genie3(
expression_data=expression_matrix,
tf_names=tf_names,
seed=42
)
```
### Parameters
```python
genie3(
expression_data, # Required: pandas DataFrame or numpy array
gene_names=None, # Required for numpy arrays
tf_names='all', # List of TF names or 'all'
verbose=False, # Print progress messages
client_or_address='local', # Dask client or scheduler address
seed=None # Random seed for reproducibility
)
```
## Algorithm Comparison
| Feature | GRNBoost2 | GENIE3 |
|---------|-----------|--------|
| **Speed** | Fast (optimized for large data) | Slower |
| **Method** | Gradient boosting | Random Forest |
| **Best for** | Large-scale data (10k+ observations) | Small-medium datasets |
| **Output format** | Same | Same |
| **Inference strategy** | Multiple regression | Multiple regression |
| **Recommended** | Yes (default choice) | For comparison/validation |
## Advanced: Custom Regressor Parameters
For advanced users, pass custom scikit-learn regressor parameters:
```python
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
# Custom GRNBoost2 parameters
custom_grnboost2 = grnboost2(
expression_data=expression_matrix,
regressor_type='GBM',
regressor_kwargs={
'n_estimators': 100,
'max_depth': 5,
'learning_rate': 0.1
}
)
# Custom GENIE3 parameters
custom_genie3 = genie3(
expression_data=expression_matrix,
regressor_type='RF',
regressor_kwargs={
'n_estimators': 1000,
'max_features': 'sqrt'
}
)
```
## Choosing the Right Algorithm
**Decision guide**:
1. **Start with GRNBoost2** - It's faster and handles large datasets better
2. **Use GENIE3 if**:
- Comparing with existing GENIE3 publications
- Dataset is small-medium sized
- Validating GRNBoost2 results
Both algorithms produce comparable regulatory networks with the same output format, making them interchangeable for most analyses.

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# Basic GRN Inference with Arboreto
## Input Data Requirements
Arboreto requires gene expression data in one of two formats:
### Pandas DataFrame (Recommended)
- **Rows**: Observations (cells, samples, conditions)
- **Columns**: Genes (with gene names as column headers)
- **Format**: Numeric expression values
Example:
```python
import pandas as pd
# Load expression matrix with genes as columns
expression_matrix = pd.read_csv('expression_data.tsv', sep='\t')
# Columns: ['gene1', 'gene2', 'gene3', ...]
# Rows: observation data
```
### NumPy Array
- **Shape**: (observations, genes)
- **Requirement**: Separately provide gene names list matching column order
Example:
```python
import numpy as np
expression_matrix = np.genfromtxt('expression_data.tsv', delimiter='\t', skip_header=1)
with open('expression_data.tsv') as f:
gene_names = [gene.strip() for gene in f.readline().split('\t')]
assert expression_matrix.shape[1] == len(gene_names)
```
## Transcription Factors (TFs)
Optionally provide a list of transcription factor names to restrict regulatory inference:
```python
from arboreto.utils import load_tf_names
# Load from file (one TF per line)
tf_names = load_tf_names('transcription_factors.txt')
# Or define directly
tf_names = ['TF1', 'TF2', 'TF3']
```
If not provided, all genes are considered potential regulators.
## Basic Inference Workflow
### Using Pandas DataFrame
```python
import pandas as pd
from arboreto.utils import load_tf_names
from arboreto.algo import grnboost2
if __name__ == '__main__':
# Load expression data
expression_matrix = pd.read_csv('expression_data.tsv', sep='\t')
# Load transcription factors (optional)
tf_names = load_tf_names('tf_list.txt')
# Run GRN inference
network = grnboost2(
expression_data=expression_matrix,
tf_names=tf_names # Optional
)
# Save results
network.to_csv('network_output.tsv', sep='\t', index=False, header=False)
```
**Critical**: The `if __name__ == '__main__':` guard is required because Dask spawns new processes internally.
### Using NumPy Array
```python
import numpy as np
from arboreto.algo import grnboost2
if __name__ == '__main__':
# Load expression matrix
expression_matrix = np.genfromtxt('expression_data.tsv', delimiter='\t', skip_header=1)
# Extract gene names from header
with open('expression_data.tsv') as f:
gene_names = [gene.strip() for gene in f.readline().split('\t')]
# Verify dimensions match
assert expression_matrix.shape[1] == len(gene_names)
# Run inference with explicit gene names
network = grnboost2(
expression_data=expression_matrix,
gene_names=gene_names,
tf_names=tf_names
)
network.to_csv('network_output.tsv', sep='\t', index=False, header=False)
```
## Output Format
Arboreto returns a Pandas DataFrame with three columns:
| Column | Description |
|--------|-------------|
| `TF` | Transcription factor (regulator) gene name |
| `target` | Target gene name |
| `importance` | Regulatory importance score (higher = stronger regulation) |
Example output:
```
TF1 gene5 0.856
TF2 gene12 0.743
TF1 gene8 0.621
```
## Setting Random Seed
For reproducible results, provide a seed parameter:
```python
network = grnboost2(
expression_data=expression_matrix,
tf_names=tf_names,
seed=777
)
```
## Algorithm Selection
Use `grnboost2()` for most cases (faster, handles large datasets):
```python
from arboreto.algo import grnboost2
network = grnboost2(expression_data=expression_matrix)
```
Use `genie3()` for comparison or specific requirements:
```python
from arboreto.algo import genie3
network = genie3(expression_data=expression_matrix)
```
See `references/algorithms.md` for detailed algorithm comparison.

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# Distributed Computing with Arboreto
Arboreto leverages Dask for parallelized computation, enabling efficient GRN inference from single-machine multi-core processing to multi-node cluster environments.
## Computation Architecture
GRN inference is inherently parallelizable:
- Each target gene's regression model can be trained independently
- Arboreto represents computation as a Dask task graph
- Tasks are distributed across available computational resources
## Local Multi-Core Processing (Default)
By default, arboreto uses all available CPU cores on the local machine:
```python
from arboreto.algo import grnboost2
# Automatically uses all local cores
network = grnboost2(expression_data=expression_matrix, tf_names=tf_names)
```
This is sufficient for most use cases and requires no additional configuration.
## Custom Local Dask Client
For fine-grained control over local resources, create a custom Dask client:
```python
from distributed import LocalCluster, Client
from arboreto.algo import grnboost2
if __name__ == '__main__':
# Configure local cluster
local_cluster = LocalCluster(
n_workers=10, # Number of worker processes
threads_per_worker=1, # Threads per worker
memory_limit='8GB' # Memory limit per worker
)
# Create client
custom_client = Client(local_cluster)
# Run inference with custom client
network = grnboost2(
expression_data=expression_matrix,
tf_names=tf_names,
client_or_address=custom_client
)
# Clean up
custom_client.close()
local_cluster.close()
```
### Benefits of Custom Client
- **Resource control**: Limit CPU and memory usage
- **Multiple runs**: Reuse same client for different parameter sets
- **Monitoring**: Access Dask dashboard for performance insights
## Multiple Inference Runs with Same Client
Reuse a single Dask client for multiple inference runs with different parameters:
```python
from distributed import LocalCluster, Client
from arboreto.algo import grnboost2
if __name__ == '__main__':
# Initialize client once
local_cluster = LocalCluster(n_workers=8, threads_per_worker=1)
client = Client(local_cluster)
# Run multiple inferences
network_seed1 = grnboost2(
expression_data=expression_matrix,
tf_names=tf_names,
client_or_address=client,
seed=666
)
network_seed2 = grnboost2(
expression_data=expression_matrix,
tf_names=tf_names,
client_or_address=client,
seed=777
)
# Different algorithms with same client
from arboreto.algo import genie3
network_genie3 = genie3(
expression_data=expression_matrix,
tf_names=tf_names,
client_or_address=client
)
# Clean up once
client.close()
local_cluster.close()
```
## Distributed Cluster Computing
For very large datasets, connect to a remote Dask distributed scheduler running on a cluster:
### Step 1: Set Up Dask Scheduler (on cluster head node)
```bash
dask-scheduler
# Output: Scheduler at tcp://10.118.224.134:8786
```
### Step 2: Start Dask Workers (on cluster compute nodes)
```bash
dask-worker tcp://10.118.224.134:8786
```
### Step 3: Connect from Client
```python
from distributed import Client
from arboreto.algo import grnboost2
if __name__ == '__main__':
# Connect to remote scheduler
scheduler_address = 'tcp://10.118.224.134:8786'
cluster_client = Client(scheduler_address)
# Run inference on cluster
network = grnboost2(
expression_data=expression_matrix,
tf_names=tf_names,
client_or_address=cluster_client
)
cluster_client.close()
```
### Cluster Configuration Best Practices
**Worker configuration**:
```bash
dask-worker tcp://scheduler:8786 \
--nprocs 4 \ # Number of processes per node
--nthreads 1 \ # Threads per process
--memory-limit 16GB # Memory per process
```
**For large-scale inference**:
- Use more workers with moderate memory rather than fewer workers with large memory
- Set `threads_per_worker=1` to avoid GIL contention in scikit-learn
- Monitor memory usage to prevent workers from being killed
## Monitoring and Debugging
### Dask Dashboard
Access the Dask dashboard for real-time monitoring:
```python
from distributed import Client
client = Client() # Prints dashboard URL
# Dashboard available at: http://localhost:8787/status
```
The dashboard shows:
- **Task progress**: Number of tasks completed/pending
- **Resource usage**: CPU, memory per worker
- **Task stream**: Real-time visualization of computation
- **Performance**: Bottleneck identification
### Verbose Output
Enable verbose logging to track inference progress:
```python
network = grnboost2(
expression_data=expression_matrix,
tf_names=tf_names,
verbose=True
)
```
## Performance Optimization Tips
### 1. Data Format
- **Use Pandas DataFrame when possible**: More efficient than NumPy for Dask operations
- **Reduce data size**: Filter low-variance genes before inference
### 2. Worker Configuration
- **CPU-bound tasks**: Set `threads_per_worker=1`, increase `n_workers`
- **Memory-bound tasks**: Increase `memory_limit` per worker
### 3. Cluster Setup
- **Network**: Ensure high-bandwidth, low-latency network between nodes
- **Storage**: Use shared filesystem or object storage for large datasets
- **Scheduling**: Allocate dedicated nodes to avoid resource contention
### 4. Transcription Factor Filtering
- **Limit TF list**: Providing specific TF names reduces computation
```python
# Full search (slow)
network = grnboost2(expression_data=matrix)
# Filtered search (faster)
network = grnboost2(expression_data=matrix, tf_names=known_tfs)
```
## Example: Large-Scale Single-Cell Analysis
Complete workflow for processing single-cell RNA-seq data on a cluster:
```python
from distributed import Client
from arboreto.algo import grnboost2
import pandas as pd
if __name__ == '__main__':
# Connect to cluster
client = Client('tcp://cluster-scheduler:8786')
# Load large single-cell dataset (50,000 cells x 20,000 genes)
expression_data = pd.read_csv('scrnaseq_data.tsv', sep='\t')
# Load cell-type-specific TFs
tf_names = pd.read_csv('tf_list.txt', header=None)[0].tolist()
# Run distributed inference
network = grnboost2(
expression_data=expression_data,
tf_names=tf_names,
client_or_address=client,
verbose=True,
seed=42
)
# Save results
network.to_csv('grn_results.tsv', sep='\t', index=False)
client.close()
```
This approach enables analysis of datasets that would be impractical on a single machine.

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#!/usr/bin/env python3
"""
Basic GRN inference example using Arboreto.
This script demonstrates the standard workflow for inferring gene regulatory
networks from expression data using GRNBoost2.
Usage:
python basic_grn_inference.py <expression_file> <output_file> [--tf-file TF_FILE] [--seed SEED]
Arguments:
expression_file: Path to expression matrix (TSV format, genes as columns)
output_file: Path for output network (TSV format)
--tf-file: Optional path to transcription factors file (one per line)
--seed: Random seed for reproducibility (default: 777)
"""
import argparse
import pandas as pd
from arboreto.algo import grnboost2
from arboreto.utils import load_tf_names
def run_grn_inference(expression_file, output_file, tf_file=None, seed=777):
"""
Run GRN inference using GRNBoost2.
Args:
expression_file: Path to expression matrix TSV file
output_file: Path for output network file
tf_file: Optional path to TF names file
seed: Random seed for reproducibility
"""
print(f"Loading expression data from {expression_file}...")
expression_data = pd.read_csv(expression_file, sep='\t')
print(f"Expression matrix shape: {expression_data.shape}")
print(f"Number of genes: {expression_data.shape[1]}")
print(f"Number of observations: {expression_data.shape[0]}")
# Load TF names if provided
tf_names = 'all'
if tf_file:
print(f"Loading transcription factors from {tf_file}...")
tf_names = load_tf_names(tf_file)
print(f"Number of TFs: {len(tf_names)}")
# Run GRN inference
print(f"Running GRNBoost2 with seed={seed}...")
network = grnboost2(
expression_data=expression_data,
tf_names=tf_names,
seed=seed,
verbose=True
)
# Save results
print(f"Saving network to {output_file}...")
network.to_csv(output_file, sep='\t', index=False, header=False)
print(f"Done! Network contains {len(network)} regulatory links.")
print(f"\nTop 10 regulatory links:")
print(network.head(10).to_string(index=False))
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Infer gene regulatory network using GRNBoost2'
)
parser.add_argument(
'expression_file',
help='Path to expression matrix (TSV format, genes as columns)'
)
parser.add_argument(
'output_file',
help='Path for output network (TSV format)'
)
parser.add_argument(
'--tf-file',
help='Path to transcription factors file (one per line)',
default=None
)
parser.add_argument(
'--seed',
help='Random seed for reproducibility (default: 777)',
type=int,
default=777
)
args = parser.parse_args()
run_grn_inference(
expression_file=args.expression_file,
output_file=args.output_file,
tf_file=args.tf_file,
seed=args.seed
)