Is Arboreto compatible with the SCENIC pipeline?

Yes, Arboreto is a core component of the SCENIC protocol, specifically used for the initial step of inferring the gene regulatory network.

Why do I need to use the __main__ guard in my scripts?

Since Arboreto uses Dask to spawn new processes for parallel computation, the 'if __name__ == "__main__":' guard is required to prevent infinite loops and runtime errors.

What algorithms does the Arboreto skill support?

Arboreto supports two primary algorithms: GRNBoost2, which uses gradient boosting for speed and scale, and GENIE3, which uses a random forest approach.

Can this skill handle large single-cell datasets?

Yes, Arboreto is built to scale. It uses Dask for distributed computing, allowing it to process 10k+ observations by utilizing multi-core machines or remote clusters.

Arboreto GRN Inference

Name: Arboreto GRN Inference
Author: K-Dense-AI

byK-Dense-AI

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2,188

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Data Science & ML

Infers gene regulatory networks from transcriptomics data using high-performance algorithms like GRNBoost2 and GENIE3.

Arboreto is a specialized computational tool for bioinformaticians and researchers to reconstruct gene regulatory networks (GRNs) from large-scale gene expression datasets, such as bulk or single-cell RNA-seq. By leveraging scalable algorithms like GRNBoost2 and GENIE3, it identifies critical transcription factor-target gene relationships with high efficiency. It is designed to handle massive datasets through distributed computing via Dask, making it ideal for modern genomic research and a core component of advanced analysis pipelines like pySCENIC.

Key Features

01High-speed Gene Regulatory Network (GRN) inference using GRNBoost2

02Transcription factor (TF) filtering for targeted regulatory analysis

03Support for GENIE3 random forest-based regression algorithms

042,188 GitHub stars

05Distributed computing support via Dask for multi-node cluster scaling

06Optimized handling of single-cell and bulk RNA-seq expression matrices

Use Cases

01Comparing gene regulation patterns across different experimental conditions or time points

02Generating input networks for downstream regulon analysis in pySCENIC

03Identifying cell-type-specific regulatory links in single-cell RNA-seq datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add k-dense-ai/claude-scientific-skills arboreto

For use in Claude.ai and ChatGPT

Download Skill