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arboreto

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

Updated Jul 1, 2026
Search Tags:#dask#transcriptomics#grnboost2#genie3#single-cell rna-seq#gene regulatory networks
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill arboreto-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/arboreto
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill arboreto-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto helps you discover which transcription factors regulate which genes by turning expression data into a ranked gene regulatory network. It reduces the manual burden of interpreting large bulk or single-cell transcriptomics datasets and makes it easier to spot meaningful regulatory relationships.

Core Features & Use Cases

  • Fast network inference: Use GRNBoost2 for scalable inference on large datasets, or GENIE3 when you want a classic random-forest baseline.
  • Flexible inputs: Work with Pandas DataFrames, NumPy arrays, or sparse CSC matrices, and optionally restrict inference to a known transcription factor list.
  • Distributed execution: Run locally across CPU cores or connect to a Dask cluster for larger transcriptomics workflows.
  • Use case: A researcher can analyze single-cell RNA-seq counts, infer cell-type-specific regulatory links, compare multiple conditions, and export the resulting network for downstream visualization or validation.

Quick Start

Use the arboreto skill to infer a gene regulatory network from your expression matrix, optionally applying a transcription factor list and writing the ranked links to a TSV file.

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?▼

Gene regulatory network inference from single-cell RNA-seq uses GRNBoost2 or GENIE3 to rank transcription factor-target gene relationships from an expression matrix. It accepts pandas DataFrames, NumPy arrays, or sparse CSC matrices and optionally filters by a transcription factor list.

What is the best way to scale gene regulatory network inference for large transcriptomics datasets?▼

The best way to scale gene regulatory network inference is using GRNBoost2 with Dask-backed distributed execution. This approach processes large transcriptomics datasets across local CPU cores or a connected Dask cluster, ensuring scalable ranking of transcription factor-target relationships.

Can I use a sparse CSC matrix as input for inferring transcription factor-target relationships?▼

Yes, you can use a sparse CSC matrix as input for inferring transcription factor-target relationships. The pipeline accepts pandas DataFrames, NumPy arrays, and sparse CSC matrices, correctly shaping them to rank gene regulatory network links.

Does GRNBoost2 vs GENIE3 matter when analyzing bulk RNA-seq expression matrices?▼

Choosing GRNBoost2 versus GENIE3 matters when analyzing bulk RNA-seq expression matrices. GRNBoost2 provides scalable inference for large datasets via Dask, while GENIE3 serves as a classic random-forest baseline for ranking transcription factor-target gene regulatory relationships.

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

To restrict gene regulatory network inference to a specific list of transcription factors, you provide an optional transcription factor list alongside your expression matrix. This ensures the pipeline only ranks relationships involving those specified transcription factors.

Do I need a Dask cluster to run GRNBoost2 inference on my expression matrix?▼

You do not need a Dask cluster to run GRNBoost2 inference; you can execute locally across available CPU cores. Connecting to a Dask cluster is recommended for larger single-cell or bulk transcriptomics workflows that require distributed execution.

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