arboreto

Name: arboreto
Availability: InStock
Author: zeyuyang-0420

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

Updated May 8, 2026

Search Tags:#distributed computing #gene regulatory network #transcriptomics #GRN inference #scalable algorithms

Ready to run

npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill arboreto-zeyuyang-0420

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/Zeyuyang-0420/bio-ai-research-skills/tree/main/categories/single-cell-sequencing/arboreto
Command: npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill arboreto-zeyuyang-0420

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides scalable algorithms for inferring gene regulatory networks from gene expression data, addressing the need for efficient analysis of large-scale transcriptomics datasets.

Core Features & Use Cases

GRN Inference: Infers regulatory relationships between transcription factors and target genes using scalable algorithms.
Algorithm Selection: Offers both GRNBoost2 and GENIE3 algorithms for efficient and accurate GRN inference.
Distributed Computing: Supports distributed computing for large-scale datasets, enabling analysis on single machines and multi-node clusters.
Use Case: Ideal for single-cell RNA-seq analysis, bulk RNA-seq with transcription factor filtering, and comparative analysis across multiple conditions.

Quick Start

To install arboreto and perform basic GRN inference, use the following command:

uv pip install arboreto

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 large-scale transcriptomics data?▼

You can infer gene regulatory networks from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3. This approach supports distributed computing with Dask to efficiently process large-scale single-cell or bulk RNA-seq datasets.

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

GRNBoost2 and GENIE3 are both provided for gene regulatory network inference. GRNBoost2 is designed for scalable, distributed computing environments, while GENIE3 offers an established tree-based approach for identifying regulatory relationships between transcription factors and target genes.

Can I use distributed computing to scale gene regulatory network inference on multi-node clusters?▼

Yes, gene regulatory network inference supports distributed computing for large-scale datasets. By leveraging Dask and distributed computing frameworks, the analysis can scale from a single machine to multi-node clusters to handle massive transcriptomics datasets.

How do I run GRN inference on single-cell RNA-seq data?▼

To run GRN inference on single-cell RNA-seq data, you input gene expression matrices into GRNBoost2 or GENIE3 algorithms. The process identifies regulatory relationships between transcription factors and target genes, scalable via distributed computing for large single-cell datasets.

What dependencies are required for scalable gene regulatory network inference?▼

Scalable gene regulatory network inference requires scipy, scikit-learn, numpy, pandas, dask, and distributed. These dependencies provide the foundational scientific computing, data manipulation, and distributed computing capabilities needed for processing large-scale transcriptomics datasets.

When should I use distributed computing over a single machine for GRN inference?▼

Use distributed computing for GRN inference when analyzing large-scale transcriptomics datasets that exceed single-machine memory or processing limits. The implementation scales from local execution to multi-node clusters, accommodating massive gene expression matrices efficiently.