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Arboreto Gene Regulatory Network Inference

Guides Claude to infer transcription factor–target gene networks from expression data using GRNBoost2/GENIE3.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 7/10Last updated: Aug 9, 2026

What it does

Teaches Claude how to run the Arboreto library to infer gene regulatory networks (GRNs) from bulk or single-cell RNA-seq expression matrices.

  • Chooses between GRNBoost2 (fast gradient boosting) and GENIE3 (Random Forest baseline)
  • Restricts regulators with a TF list and tunes limit, seed, and input formats (DataFrame, ndarray, sparse CSC)
  • Scales from all local cores to a custom LocalCluster or a remote Dask scheduler
  • Interprets the output DataFrame (TF, target, importance) and filters high-confidence links
  • Bridges into pySCENIC workflows, AnnData conversion, and multi-seed consensus for reproducibility

Who it's for

  • Bioinformaticians building regulatory networks from transcriptomics data
  • pySCENIC users who need the co-expression module step before cisTarget pruning
  • Teams running large single-cell datasets that require distributed computation

Examples

  1. "Infer a network from scrna_counts.tsv and keep only links with importance > 0.5" → seeded script plus filtering step
  2. "Restrict regulators to the TFs in human_tfs.txt for my bulk TPM matrix" → uses load_tf_names with tf_names
  3. "Compare control vs treatment_24h vs treatment_48h networks" → per-condition loop and multi-seed consensus analysis

· · · Install guide · · ·

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 in the folder where you keep skills.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Copy the skill into Claude Code: mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/arboreto ~/.claude/skills/
  4. Install the Python package: uv pip install arboreto (or conda install -c bioconda arboreto).
  5. Restart Claude Code and ask something like "use arboreto to infer a GRN from my expression matrix".
  6. Always wrap script calls in if __name__ == '__main__': because Dask spawns worker processes.
View source on GitHub ↗License: MIT