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arboreto

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Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships.

scientific-skills/Evidence Insight/arboreto/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

## When to Use

- You have a bulk RNA-seq expression matrix and want to infer transcription factor (TF) → target gene regulatory edges.
- You have single-cell RNA-seq data (after normalization/aggregation as needed) and want to recover putative regulatory interactions.
- You need GRN inference that can scale to large datasets using parallel/distributed execution.
- You want to compare gradient-boosting–based GRN inference (GRNBoost2) versus random-forest–based inference (GENIE3).
- You need a reproducible, scriptable pipeline to generate a ranked network edge list from expression data.

## Key Features

- GRN inference from gene expression data using **GRNBoost2** (gradient boosting) or **GENIE3** (random forest).
- Scalable execution via **Dask**, from a single machine to multi-node clusters.
- Command-line workflow for generating a GRN edge list from a tabular expression matrix.
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