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.
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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. 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:
uv pip install arboreto
Basic GRN inference:
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:
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)
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:
Arboreto is a core component of the SCENIC pipeline for single-cell regulatory network analysis:
# Step 1: Use arboreto for GRN inferencefrom 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)
