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arboreto

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

33.0k|3.2k|Updated Oct 19, 2025
Search Tags:#bioinformatics#gene-regulatory-network#distributed-computing#single-cell-rna-seq#grnboost2#genie3#bulk-rna-seq
One-click install
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill arboreto-k-dense-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/arboreto
Command: npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill arboreto-k-dense-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto infers gene regulatory networks (GRNs) from gene expression data using scalable algorithms such as GRNBoost2 and GENIE3, enabling the discovery of transcription factor–target relationships across transcriptomics experiments.

Core Features & Use Cases

  • Basic GRN Inference: Accepts expression data in Pandas DataFrame or NumPy array, runs GRNBoost2 or GENIE3, and outputs a TF–target–importance table.
  • Algorithm Selection: Provides fast GRNBoost2 as the default for large datasets and GENIE3 for comparison or validation.
  • Distributed Computing: Supports local multi-core operation, custom Dask clients, and cluster-based workflows for large-scale analyses.
  • Output & Integration: Returns a standardized regulatory network format that can be fed into downstream pipelines (e.g., pySCENIC) for regulon analysis.

Quick Start

Install Arboreto and prepare your expression matrix (genes as columns), then run GRNBoost2 to generate the regulatory network.

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?▼

Infer gene regulatory networks from single-cell RNA-seq data by applying GRNBoost2 or GENIE3 algorithms to your expression matrix, generating a standardized TF-target-importance table that maps transcription factor relationships across large studies.

Can I use Dask for distributed computing when running GRNBoost2 on large expression datasets?▼

Dask enables distributed computing for GRNBoost2, supporting local multi-core operation, custom Dask clients, and cluster-based workflows to scale gene regulatory network inference across large bulk or single-cell RNA-seq datasets.

What is the best way to identify transcription factor target relationships from bulk RNA-seq data?▼

Identifying transcription factor target relationships from bulk RNA-seq is best handled by scalable GRNBoost2, which processes expression matrices and outputs a standardized regulatory network format for downstream regulon analysis.

Does this gene regulatory network inference tool accept both pandas DataFrames and numpy arrays?▼

Gene regulatory network inference accepts both pandas DataFrames and numpy arrays with gene names, running GRNBoost2 or GENIE3 to produce a TF-target-importance output table with optional transcription factor filtering.

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

GRNBoost2 provides fast gene regulatory network inference as the default for large datasets, while GENIE3 offers an alternative for comparison or validation, both outputting a standardized TF-target-importance format for downstream pipelines.

Can the regulatory network output be integrated with downstream pipelines like pySCENIC?▼

The regulatory network output uses a standardized TF-target-importance format designed for integration into downstream pipelines such as pySCENIC, enabling regulon analysis and further transcription factor network exploration.

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