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arboreto

Infer gene regulatory networks from expression data using gradient boosting and random forests.

2|Updated Jun 4, 2026
Search Tags:#python#bioinformatics#genomics#dask#transcriptomics#grn
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill arboreto-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/arboreto
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill arboreto-lord1egypt

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 addresses the computational challenge of identifying complex regulatory relationships between transcription factors and target genes within large-scale transcriptomics datasets.

Core Features & Use Cases

  • Scalable GRN Inference: Utilizes GRNBoost2 and GENIE3 algorithms to map regulatory interactions from bulk or single-cell RNA-seq data.
  • Distributed Computing: Leverages Dask to scale inference from local multi-core machines to high-performance computing clusters.
  • Use Case: Researchers can use this to infer cell-type-specific gene regulatory networks from single-cell RNA-seq data to identify key transcription factors driving cellular differentiation.

Quick Start

Use the arboreto skill to infer a gene regulatory network from the expression matrix file expression_data.tsv and save the results to network.tsv.

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 expression data?▼

You can infer gene regulatory networks from expression data using GRNBoost2 and GENIE3 algorithms, which map transcription factor-target interactions from bulk or single-cell RNA-seq expression matrices.

Can I use Dask to scale gene regulatory network inference on a high-performance computing cluster?▼

Yes, gene regulatory network inference leverages Dask for distributed computing, allowing you to scale transcriptomics analysis from local multi-core machines to high-performance computing clusters.

What input data format is needed to map transcription factor-target interactions using gradient boosting?▼

Mapping transcription factor-target interactions requires a gene expression matrix, such as a TSV file containing bulk or single-cell transcriptomics data, to compute regulatory relationships.

Do I need scikit-learn and scipy installed to run GRNBoost2 or GENIE3 for transcriptomics analysis?▼

Yes, running GRNBoost2 or GENIE3 for transcriptomics analysis requires scikit-learn, scipy, numpy, and pandas to execute the underlying gradient boosting and random forest algorithms.

What is the best way to identify key transcription factors driving cellular differentiation from an expression matrix?▼

The best way to identify key transcription factors driving cellular differentiation is to infer cell-type-specific gene regulatory networks from single-cell RNA-seq data using scalable gradient boosting algorithms.

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