arboreto

Name: arboreto
Availability: InStock
Author: shushuzn

Infer gene regulatory networks from expression matrices using GRNBoost2 or GENIE3.

4|Updated Mar 2, 2026

Search Tags:#transcriptomics #single-cell rna-seq #transcription factors #gene regulatory networks #grn inference #dask distributed #python genomics

Ready to run

npx skills add https://github.com/shushuzn/Rairos --skill arboreto-shushuzn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/shushuzn/Rairos/tree/main/skills/arboreto
Command: npx skills add https://github.com/shushuzn/Rairos --skill arboreto-shushuzn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Infer gene regulatory networks (TF-to-target relationships) from gene expression data so you can discover candidate regulatory interactions without manual feature engineering.

Core Features & Use Cases

Gene regulatory network inference: Predict transcription factor (TF) regulators and their target genes with importance scores using GRNBoost2 or GENIE3.
Algorithm choice for scale: Use GRNBoost2 for large datasets (e.g., 10k+ observations) and GENIE3 for comparison/validation.
Distributed computation: Run inference on local multi-core via Dask or on a remote Dask cluster for large single-cell or bulk RNA-seq matrices.

Quick Start

Run GRNBoost2 on your expression matrix file by telling the AI to execute: python scripts/basic_grn_inference.py expression_data.tsv output_network.tsv --tf-file tfs.txt --seed 777

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, use this Skill to estimate transcription factor-to-target regulatory links with importance scores from your gene expression matrix. It applies scalable parallel execution via Dask to handle large scRNA-seq datasets.

Can I use GRNBoost2 or GENIE3 for transcription factor regulatory network inference?▼

Yes, you can use either GRNBoost2 or GENIE3 for transcription factor regulatory network inference. Choose GRNBoost2 for large datasets like 10k+ observations, and use GENIE3 for comparison and validation of your predicted regulatory interactions.

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

For gene regulatory network inference, format your expression matrices with genes as columns. You can also include optional gene or transcription factor metadata files to restrict the inference to known TFs during the regulatory link prediction process.

Does distributed computing support large-scale bulk RNA-seq gene regulatory network inference?▼

Yes, distributed computing supports large-scale bulk RNA-seq gene regulatory network inference. You can run inference on local multi-core systems via Dask or deploy on a remote Dask cluster to process large expression matrices efficiently.

What is the best way to compare gene regulatory networks across multiple conditions?▼

The best way to compare gene regulatory networks across multiple conditions is to run TF-to-target inference separately for each condition using GRNBoost2 or GENIE3, then contrast the resulting importance scores to identify condition-specific regulatory interactions.