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arboreto

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

Updated May 24, 2026
Search Tags:#distributed computing#dask#transcriptomics#grnboost2#single-cell rna-seq#gene regulatory networks#tf-target inference
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill arboreto-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/arboreto
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill arboreto-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto solves the problem of inferring gene regulatory networks (which transcription factors regulate which target genes) from gene expression datasets.

Core Features & Use Cases

  • Scalable GRN inference (GRNBoost2/GENIE3): Predict TF→target regulatory links with an importance score from expression matrices.
  • TF-restricted modeling: Limit candidate regulators to a provided transcription factor list to improve relevance and efficiency.
  • Parallel and distributed computation: Use Dask-backed execution for faster inference on large bulk or single-cell RNA-seq data.
  • Use Case: Given a bulk RNA-seq or single-cell RNA-seq expression matrix, infer a TF-target network, then filter high-importance edges to prioritize candidate regulatory interactions.

Quick Start

Run GRN inference by executing the ready-to-run example script with your expression matrix, output path, and optional TF list.

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 by applying scalable GRNBoost2 or GENIE3 algorithms to your expression matrix, predicting transcription factor to target gene relationships with importance scores.

Can I restrict gene regulatory network inference to a specific list of transcription factors?▼

Yes, you can restrict gene regulatory network inference by providing a transcription factor list as input, limiting candidate regulators to improve computational efficiency and the biological relevance of the predicted network.

What is the best way to scale up GRNBoost2 inference for large transcriptomics datasets?▼

The best way to scale GRNBoost2 inference for large transcriptomics datasets is to use Dask-backed distributed execution, enabling parallel computation to efficiently process large bulk or single-cell RNA-seq expression matrices.

Does distributed computing with Dask work for both bulk and single-cell RNA-seq workflows?▼

Yes, distributed computing with Dask works for both bulk and single-cell RNA-seq workflows, providing scalable parallel execution for large-scale gene regulatory network inference across multiple experimental conditions.

How do I ensure reproducibility when predicting transcription factor to target gene relationships?▼

To ensure reproducibility when predicting transcription factor to target gene relationships, this Skill supports seed-controlled execution during the GRNBoost2 or GENIE3 inference process to generate consistent, reproducible network outputs.

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