arboreto

Name: arboreto
Availability: InStock
Author: HolobiomicsLab

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

4|1|Updated Jun 18, 2025

Search Tags:#single-cell #arboreto #gene-regulatory-network #grn #grnboost2 #genie3

Ready to run

npx skills add https://github.com/HolobiomicsLab/Toolomics --skill arboreto-holobiomicslab

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/HolobiomicsLab/Toolomics/tree/main/mcp_host/skills/scientific-skills/scientific-skills/arboreto
Command: npx skills add https://github.com/HolobiomicsLab/Toolomics --skill arboreto-holobiomicslab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, arboreto, 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.

Core Features & Use Cases

GRN inference with GRNBoost2 and GENIE3 for large-scale datasets.
Supports distributed computing with Dask for multi-node or multi-core execution.
Produces a TF–target network DataFrame with an 'TF', 'target', and 'importance' score suitable for downstream analyses like pySCENIC.
Use cases include single-cell RNA-seq regulatory analysis and bulk RNA-seq inference, TF filtering, and method comparisons.

Quick Start

Run GRN inference on your expression matrix using grnboost2 and save the resulting TF–target network to a tab-delimited file.

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 expression data?▼

To infer gene regulatory networks from single-cell RNA-seq data, apply GRNBoost2 or GENIE3 algorithms to your expression matrix. This generates a TF–target network DataFrame with importance scores for identifying transcription factor–target relationships.

What is the difference between using GRNBoost2 and GENIE3 for GRN inference?▼

GRNBoost2 and GENIE3 are both tree-based methods for GRN inference, but GRNBoost2 supports scalable distributed computing with Dask for large-scale datasets. Both output a TF–target network DataFrame with importance scores for downstream regulatory analysis.

Can I use Dask for distributed computing when inferring GRNs on large datasets?▼

Yes, you can use Dask for distributed computing to enable multi-node or multi-core execution during GRN inference. This allows scalable processing of large-scale single-cell or bulk RNA-seq datasets using the Arboreto APIs.

How do I filter transcription factors before running GRN inference?▼

You can apply optional TF filtering before running GRN inference to restrict the analysis to specific transcription factors. This refines the resulting TF–target network DataFrame by focusing on relevant transcription factor–target gene relationships.

What is the output format of the gene regulatory network for downstream analysis?▼

The output format is a TF–target network DataFrame containing columns for the transcription factor, target gene, and importance score. This tabular format is directly suitable for downstream analyses like pySCENIC.

Does this GRN inference approach work for both bulk and single-cell RNA-seq workflows?▼

Yes, this GRN inference approach applies to both single-cell and bulk RNA-seq workflows. It identifies transcription factor–target gene relationships across different expression data scales using scalable algorithms like GRNBoost2 and GENIE3.