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arboreto

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

Updated Mar 30, 2026
Search Tags:#single-cell#arboreto#gene-regulatory-network#distributed-computing#grn#grnboost2#genie3
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill arboreto-ya-way
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/labclaw-arboreto
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill arboreto-ya-way

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires arboreto, pandas, numpy, scipy, scikit-learn, dask, distributed, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Infers gene regulatory networks from gene expression data to reveal transcription factor–target gene relationships and regulatory interactions across conditions.

Core Features & Use Cases

  • Scalable GRN inference using GRNBoost2 and GENIE3 for both single-cell and bulk RNA-seq data.
  • Supports local and distributed computing to handle large datasets.
  • Integrates with downstream workflows such as pySCENIC for regulon analysis and activity scoring.
  • Use Case: Build a TF–target network from a scRNA-seq matrix and compare networks across conditions.

Quick Start

Install Arboreto and run GRN inference on your expression data to generate 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?▼

To infer gene regulatory networks from single-cell RNA-seq data, you can use the Arboreto library which applies scalable GRNBoost2 and GENIE3 algorithms to identify transcription factor-target relationships from your expression matrix.

Can I use distributed computing for gene regulatory network inference on large datasets?▼

Yes, gene regulatory network inference supports distributed computing via Dask to handle large datasets, allowing you to scale GRNBoost2 and GENIE3 calculations across clusters for processing massive single-cell or bulk RNA-seq matrices.

What is the difference between GRNBoost2 and GENIE3 for transcription factor-target network inference?▼

Both GRNBoost2 and GENIE3 infer transcription factor-target networks from expression data, but GRNBoost2 is specifically designed for scalable distributed computing, whereas GENIE3 provides a standard tree-based approach for network inference.

What dependencies do I need to run Arboreto for GRN inference?▼

Running Arboreto for GRN inference requires installing pandas, NumPy, SciPy, scikit-learn, and the Dask distributed computing libraries to process expression matrices and output transcription factor-target importance networks.

How do I integrate gene regulatory network inference results with pySCENIC for regulon analysis?▼

Gene regulatory network inference outputs a tabular network with TF, target, and importance columns that integrates directly with pySCENIC downstream workflows for regulon analysis and transcription factor activity scoring.

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