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arboreto

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

1|Updated Mar 11, 2026
Search Tags:#gene-regulatory-network#distributed-computing#single-cell-rna-seq#grn#grnboost2#genie3#bulk-rna-seq
One-click install
npx skills add https://github.com/SciMate-AI/scicli --skill arboreto-scimate-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/SciMate-AI/scicli/tree/main/internal/skills/bundled/claude-scientific-skills/skills/arboreto
Command: npx skills add https://github.com/SciMate-AI/scicli --skill arboreto-scimate-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto provides scalable methods to infer gene regulatory networks from large-scale expression data, enabling researchers to uncover transcription factor–target relationships efficiently.

Core Features & Use Cases

  • GRN inference with GRNBoost2 and GENIE3 for both single-cell and bulk RNA-seq data.
  • Distributed computing support via Dask to scale analyses from laptop to cluster.
  • Ready-to-run scripts for common workflows and an API to integrate into pipelines.

Quick Start

Install Arboreto and run a GRNBoost2 workflow on your expression matrix to infer gene regulatory networks.

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?▼

You can infer gene regulatory networks from single-cell RNA-seq data by applying scalable algorithms like GRNBoost2 and GENIE3 to your expression matrix. This process outputs a table mapping transcription factor-target relationships and importance scores.

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

Yes, gene regulatory network inference supports distributed computing via Dask, allowing you to scale analyses from a laptop to a cluster when processing large bulk or single-cell RNA-seq datasets.

What is the difference between GRNBoost2 and GENIE3 for gene regulatory network inference?▼

Both GRNBoost2 and GENIE3 infer transcription factor-target relationships from expression data, but GRNBoost2 is specifically designed for scalable distributed computation across large datasets.

What output format does gene regulatory network inference produce from expression matrices?▼

Gene regulatory network inference produces a TF-target-importance table detailing the relationships between transcription factors and their targets. This table is generated from your expression matrix using the Python API.

Do I need a preprocessed expression matrix to run bulk RNA-seq gene regulatory network inference?▼

Yes, you need a preprocessed expression matrix as input to run bulk RNA-seq gene regulatory network inference. The algorithms use this matrix to calculate transcription factor-target relationships and output the importance table.

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