arboreto
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Arboreto ## Overview Arboreto is a Python library from [Aerts Lab](https://github.com/aertslab/arboreto) for inferring gene regulatory networks (GRNs) from gene expression data. It parallelizes tree-based ensemble regression (GRNBoost2, GENIE3) with [Dask](https://distributed.dask.org/) across local cores or remote clusters. **Core capability**: Identify which transcription factors (TFs) regulate which target genes based on expression patterns across observations (cells, samples, conditions). **Upstream**: PyPI **0.1.6** (2021-02-09, latest). Docs: [arboreto.readthedocs.io](https://arboreto.readthedocs.io/en/latest/). Primary downstream consumer: [pySCENIC](https://github.com/aertslab/pySCENIC). ## Quick Start Install arboreto: ```bash uv pip install arboreto ``` Basic GRN inference: ```python import pandas as pd from arboreto.algo import grnboost2 if __name__ == '__main__': # Load expression data (genes as columns) expression_matrix = pd.read_csv('expression_data.tsv', sep='\t') # Infer regulatory network network = grnboost2(expression_data=expression_matrix) # Save results (TF, target, importance) network.to_csv('network.tsv', sep='\t', index=False, header=False) ``` **Critical
- Overview
- Quick Start
- Core Capabilities
- 1. Basic GRN Inference
- 2. Algorithm Selection
- 3. Distributed Computing
- Installation
- Common Use Cases
- Single-Cell RNA-seq Analysis
- Bulk RNA-seq with TF Filtering
- Comparative Analysis (Multiple Conditions)
- Output Interpretation
- Integration with pySCENIC
- Reproducibility
uv pip install arboreto python scripts/basic_grn_inference.py expression_data.tsv output_network.tsv --tf-file tfs.txt --seed 777 --limit 5000 conda install -c bioconda arboreto
What does the arboreto skill do?
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.