arboreto

Infer gene regulatory networks from expression data using GRNBoost2 and GENIE3.

52|6|Updated Nov 24, 2025

Search Tags:#single-cell #GENIE3 #GRNBoost2 #GRN #transcription-factors #network-inference

Ready to run

npx skills add https://github.com/ovachiever/droid-tings --skill arboreto-ovachiever

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/ovachiever/droid-tings/tree/main/skills/arboreto
Command: npx skills add https://github.com/ovachiever/droid-tings --skill arboreto-ovachiever

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires arboreto, pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Arboreto infers gene regulatory networks (GRNs) from gene expression data, enabling discovery of transcription factor–target gene relationships and regulatory interactions at scale across bulk and single-cell datasets.

Core Features & Use Cases

GRN inference at scale using GRNBoost2 and GENIE3
Distributed computing with Dask for large datasets
TF-target networks with importance scores for downstream analyses
Use Case: Derive regulatory programs across cell types from scRNA-seq data

Quick Start

Run basic GRN inference on an expression matrix 'expression.csv' with TF list 'tfs.txt': python scripts/basic_grn_inference.py expression.csv output_network.tsv --tf-file tfs.txt

Frequently Asked Questions about arboreto

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I infer gene regulatory networks from RNA-seq data?▼

Gene regulatory network inference identifies transcription factor–target relationships in expression data using algorithms like GRNBoost2 and GENIE3. Arboreto implements scalable GRN inference on bulk and single-cell RNA-seq, outputting TF-target links with importance scores for downstream analysis across samples and conditions.

Can I use GRN inference on single-cell RNA-seq data?▼

Yes, GRN inference works on single-cell RNA-seq to derive regulatory programs across cell types. Arboreto applies GRNBoost2 and GENIE3 with distributed computing support, enabling scalable network inference on large scRNA-seq datasets with optional transcription factor filtering.

What input formats does gene regulatory network inference accept?▼

GRN inference accepts flexible input formats including DataFrames and NumPy arrays for expression matrices. Arboreto processes expression data with optional transcription factor lists, outputting standardized TF-target-importance links in tab-separated format.

How does GRNBoost2 differ from GENIE3 for network inference?▼

Both GRNBoost2 and GENIE3 infer regulatory relationships from expression data; GRNBoost2 typically offers faster computation on large datasets. Arboreto implements both algorithms with distributed computing, letting you choose based on dataset scale and computational resources.

Can I filter transcription factors in network inference?▼

Yes, transcription factor filtering narrows inference to specific regulatory proteins. Arboreto supports optional TF filtering during GRN inference, allowing you to focus network analysis on known or curated transcription factors relevant to your biological question.

Does gene regulatory network inference scale to large datasets?▼

GRN inference at scale requires distributed computing for large expression matrices. Arboreto supports Dask-backed computation, enabling scalable network inference on bulk RNA-seq and single-cell datasets with thousands of genes and samples.