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

Because Arboreto uses Dask for parallel processing, which spawns new processes; this guard prevents recursive process creation and errors during execution.

Can I use Arboreto for single-cell RNA-seq data?

Yes, Arboreto is highly optimized for single-cell data and is a core component of the pySCENIC pipeline for identifying regulons and activity scores.

What is the difference between GRNBoost2 and GENIE3?

GRNBoost2 is based on gradient boosting and is significantly faster, making it the preferred choice for large datasets, while GENIE3 uses the classic Random Forest approach.

Does this skill support distributed computing?

Absolutely. It integrates with Dask to scale inference tasks from local multi-core machines to remote high-performance computing clusters.

Arboreto GRN Inference

Name: Arboreto GRN Inference
Author: Microck

byMicrock

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数据科学与机器学习

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

关于

Arboreto is a specialized computational tool for reconstructing gene regulatory networks (GRNs) from high-throughput gene expression data such as bulk or single-cell RNA-seq. It leverages high-performance algorithms like GRNBoost2 and GENIE3 to identify transcription factor-target gene relationships, scaling efficiently from local machines to distributed clusters via Dask. This skill is indispensable for bioinformaticians and researchers needing to map complex regulatory interactions in genomic datasets without being limited by computational bottlenecks or manual scripting overhead.

主要功能

81 GitHub stars
Transcription factor (TF) filtering to refine regulatory target mapping
Ready-to-run Python scripts and best practices for reproducible research
Support for distributed computing via Dask for massive datasets
Specialized workflows for single-cell and bulk RNA-seq analysis
Scalable GRN inference using GRNBoost2 and GENIE3 algorithms

使用场景

Mapping cell-type-specific regulatory networks from single-cell transcriptomics
Scaling regulatory network inference to multi-node cluster environments
Identifying candidate transcription factors driving disease-specific gene expression

关于

主要功能

81 GitHub stars
Transcription factor (TF) filtering to refine regulatory target mapping
Ready-to-run Python scripts and best practices for reproducible research
Support for distributed computing via Dask for massive datasets
Specialized workflows for single-cell and bulk RNA-seq analysis
Scalable GRN inference using GRNBoost2 and GENIE3 algorithms

使用场景

Mapping cell-type-specific regulatory networks from single-cell transcriptomics
Scaling regulatory network inference to multi-node cluster environments
Identifying candidate transcription factors driving disease-specific gene expression