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arboreto

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

Updated Aug 23, 2026
Search Tags:#transcriptomics#grnboost2#genie3#single-cell rna-seq#gene regulatory networks#GRN inference#dask distributed
One-click install
npx skills add https://github.com/gabrielvuksani/wotann --skill arboreto-gabrielvuksani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arboreto
Source: https://github.com/gabrielvuksani/wotann/tree/main/skills/scientific/arboreto
Command: npx skills add https://github.com/gabrielvuksani/wotann --skill arboreto-gabrielvuksani

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Arboreto solves the problem of inferring gene regulatory networks from gene expression measurements by estimating which transcription factors regulate which target genes.

Core Features & Use Cases

  • GRN inference (GRNBoost2, GENIE3): Predict TF-target interactions and assign regulatory importance scores from expression data.
  • Scalable parallel computation: Use all local CPU cores by default or run distributed inference with Dask for large datasets.
  • TF filtering support: Restrict candidate regulators to a provided transcription factor list to reduce computation and focus results.
  • Use case: Analyze single-cell or bulk RNA-seq expression matrices to produce a ranked TF→target network for downstream biological interpretation and validation.

Quick Start

Run gene regulatory network inference from an expression TSV by executing: 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?▼

Gene regulatory networks are inferred from single-cell RNA-seq data by learning transcription factor to target gene links from an expression matrix using GRNBoost2 or GENIE3, yielding a ranked network of regulatory interactions.

Can I use Dask for distributed GRN inference on large transcriptomics datasets?▼

Dask enables distributed GRN inference on large transcriptomics datasets, allowing scalable parallel computation across all local CPU cores or a distributed cluster to process bulk and single-cell expression matrices efficiently.

How do I restrict candidate regulators during GRN reconstruction?▼

Candidate regulators are restricted during GRN reconstruction by providing a transcription factor list file, which filters the search space to specified TFs, reducing computation and focusing the resulting target gene regulatory links.

What is the correct input format for running GENIE3 or GRNBoost2?▼

GENIE3 and GRNBoost2 require expression data formatted as a TSV file with genes as columns and observations as rows, ensuring the algorithm correctly estimates transcription factor to target gene regulatory importance scores.

Are GRN inference results reproducible across multiple runs?▼

GRN inference results are reproducible across multiple runs by setting a fixed seed parameter, ensuring deterministic outputs from GRNBoost2 or GENIE3 even when processing varying dataset sizes and conditions.

Does this approach work for both bulk and single-cell transcriptomics workflows?▼

GRN inference applies to both bulk and single-cell transcriptomics workflows, using expression matrices to produce a ranked transcription factor to target network suitable for downstream biological interpretation and validation.

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