Why do I need the 'if __name__ == "__main__":' guard?

Since Arboreto uses Dask for multiprocessing and distributed computing, this guard is essential to prevent recursive process spawning during execution.

Is Arboreto compatible with the SCENIC pipeline?

Yes, Arboreto is a core component of the SCENIC (and pySCENIC) workflow for single-cell regulatory network analysis and regulon identification.

Which algorithms does Arboreto support?

It supports GRNBoost2 (fast, gradient boosting-based) and GENIE3 (classic random forest-based) algorithms for regulatory network reconstruction.

What is Arboreto used for?

Arboreto is a Python library used to infer gene regulatory networks (GRNs) from gene expression data, identifying which transcription factors regulate which target genes.

Can Arboreto handle large single-cell datasets?

Yes, it is designed specifically for scalability and uses Dask to distribute computations across multiple CPU cores or multi-node clusters.

Arboreto GRN Inference

Name: Arboreto GRN Inference
Author: Microck

byMicrock

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Ciencia de Datos y ML

Infers gene regulatory networks from transcriptomics data using scalable machine learning algorithms like GRNBoost2 and GENIE3.

Arboreto is a specialized computational biology tool designed to identify transcription factor-target gene relationships within large-scale gene expression datasets. By leveraging parallelized algorithms such as GRNBoost2 and GENIE3, it enables researchers to reconstruct complex regulatory architectures from bulk or single-cell RNA-seq data. It is particularly useful for bioinformatics workflows requiring high-performance computing, as it natively supports distributed execution via Dask to handle tens of thousands of observations efficiently across local cores or multi-node clusters.

Características Principales

01High-performance GRN inference using GRNBoost2 and GENIE3 algorithms

02Optimized for both bulk and single-cell RNA-seq (scRNA-seq) workflows

03Transcription factor filtering to focus analysis on known regulators

0481 GitHub stars

05Scalable distributed computing support via Dask for large datasets

06Seamless integration with Pandas DataFrames and NumPy arrays

Casos de Uso

01Analyzing differential gene regulation between control and treatment conditions

02Scaling gene network reconstruction to multi-node compute clusters

03Identifying cell-type-specific regulatory interactions in single-cell transcriptomics

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills arboreto

For use in Claude.ai and ChatGPT

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