arboreto

Infer gene regulatory networks from expression data with GRNBoost2 or GENIE3.

1|Updated Mar 4, 2026

Search Tags:#bioinformatics #single-cell #arboreto #rna-seq #grn #grnboost2 #genie3

Ready to run

npx skills add https://github.com/Hung-3008/agusta --skill arboreto-hung-3008

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/arboreto
Command: npx skills add https://github.com/Hung-3008/agusta --skill arboreto-hung-3008

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto enables scalable inference of gene regulatory networks from gene expression data, enabling discovery of transcription factor–target relationships across large datasets.

Core Features & Use Cases

Basic GRN inference using GRNBoost2 or GENIE3 on Pandas or NumPy expression matrices.
Distributed computing support with local Dask clients or remote clusters for large-scale analyses.
Output formats designed for downstream workflows (e.g., pySCENIC integration) and reproducible results.

Quick Start

Install Arboreto and run GRN inference on your expression matrix using GRNBoost2 to produce a TF-target network.

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 single-cell RNA-seq data?▼

Infer gene regulatory networks from single-cell RNA-seq data by applying scalable algorithms like GRNBoost2 or GENIE3 to your expression matrix, revealing transcription factor–target relationships across large datasets.

Can I use Dask for distributed computing to scale up GRN inference?▼

Dask enables distributed computing for GRN inference, allowing you to scale gene regulatory network analysis across local clients or remote clusters when processing large bulk or single-cell expression datasets.

What is the difference between GRNBoost2 and GENIE3 for transcription factor target analysis?▼

GRNBoost2 and GENIE3 are both algorithms for inferring transcription factor–target relationships from expression matrices, differing primarily in their computational approach to reconstructing gene regulatory networks.

How do I format expression data for gene regulatory network inference?▼

Format expression data with genes as columns in a Pandas or NumPy matrix to run gene regulatory network inference, and provide a transcription factor list if available to guide the analysis.

Does Arboreto output integrate with pySCENIC for downstream regulatory network analysis?▼

Arboreto produces output formats designed for downstream workflows such as pySCENIC integration, enabling reproducible results and seamless transition to subsequent gene regulatory network analysis steps.

Why do I need a transcription factor list for RNA-seq gene regulatory network inference?▼

A transcription factor list guides gene regulatory network inference by specifying candidate regulators, helping the algorithm accurately map transcription factor–target relationships from your RNA-seq expression data.