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Arboreto Gene Regulatory Network Inference

Guides Claude to infer transcription factor–target gene networks from expression data using arboreto's GRNBoost2 and GENIE3.

Data & AnalyticsAdvanced★ 41,485⑂ 3,824AI score 8/10Last updated: Aug 31, 2026

What it does

  • Helps you write and run arboreto code to infer gene regulatory networks (GRNs) from an expression matrix (observations × genes).
  • Provides selection guidance between GRNBoost2 (fast gradient boosting, recommended) and GENIE3 (Random Forest, classic baseline).
  • Covers Dask-based scaling: default local cores, a custom LocalCluster, or a remote scheduler — plus the mandatory if __name__ == '__main__' guard.
  • Explains the output DataFrame (TF, target, importance), filtering strategies, seed-based reproducibility and consensus runs, and pySCENIC integration.

Who it's for

  • Bioinformaticians and graduate researchers working with single-cell or bulk RNA-seq data.
  • Users who want direct control over the GRN inference step of a pySCENIC pipeline.
  • Anyone scaling large expression matrices across multiple cores or a cluster.

Example uses

  1. Load scrna_counts.tsv, run GRNBoost2 with a fixed seed, and export only links with importance > 0.5 as a high-confidence network.
  2. Restrict regulators to a human TF list (human_tfs.txt) to build a TF–target network from bulk RNA-seq TPM data.
  3. Infer separate networks for control / treatment_24h / treatment_48h and compare how regulatory links shift across conditions.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/arboreto/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/arboreto folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/arboreto/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/sas && mkdir -p ~/.claude/skills && cp -r /tmp/sas/skills/arboreto ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  2. Create the skills directory: mkdir -p ~/.claude/skills
  3. Copy just this skill: cp -r scientific-agent-skills/skills/arboreto ~/.claude/skills/
  4. Confirm that ~/.claude/skills/arboreto contains SKILL.md plus the references/ and scripts/ folders.
  5. Install the Python library in your environment: uv pip install arboreto (or conda install -c bioconda arboreto).
  6. Restart Claude Code and ask something like "Use arboreto to infer a GRN from my expression matrix" to trigger the skill.
View source on GitHub ↗License: MIT