How does it handle large-scale datasets?

It utilizes Dask for distributed computing, allowing you to scale inference tasks across multiple cores or multi-node remote clusters automatically.

What algorithms does the Arboreto skill support?

It supports GRNBoost2, a fast gradient boosting-based inference method, and GENIE3, which uses a classic Random Forest approach.

Why is the 'if __name__ == "__main__":' guard required?

Arboreto uses Dask for parallel processing, which spawns new worker processes; the guard prevents infinite recursive process creation in Python.

Can I use this for single-cell data?

Yes, Arboreto is highly optimized for single-cell RNA-seq and serves as a core component of the popular SCENIC pipeline for regulon identification.

Arboreto GRN Inference

Name: Arboreto GRN Inference
Author: Microck

byMicrock

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Data Science & ML

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

About

Arboreto is a high-performance computational tool designed for bioinformaticians to map gene regulatory networks (GRNs) from large-scale gene expression data. It provides parallelized implementations of algorithms like GRNBoost2 and GENIE3, allowing users to identify transcription factor-target gene relationships across bulk or single-cell RNA-seq datasets. This skill facilitates complex genomic analysis by supporting distributed computing via Dask, making it possible to scale from local workstations to high-performance computing clusters while maintaining reproducibility and efficiency.

Key Features

Support for large-scale single-cell and bulk RNA-seq datasets
Scalable GRN inference using GRNBoost2 and GENIE3 algorithms
Transcription factor filtering and importance-based link scoring
Seamless integration with pySCENIC regulatory workflows
Distributed computing integration with Dask for cluster scaling
81 GitHub stars

Use Cases

Mapping transcription factor-target gene relationships in single-cell RNA-seq
Scaling gene regulatory network inference to massive datasets using Dask clusters
Identifying regulatory changes across different experimental conditions

About

Key Features

Support for large-scale single-cell and bulk RNA-seq datasets
Scalable GRN inference using GRNBoost2 and GENIE3 algorithms
Transcription factor filtering and importance-based link scoring
Seamless integration with pySCENIC regulatory workflows
Distributed computing integration with Dask for cluster scaling
81 GitHub stars

Use Cases

Mapping transcription factor-target gene relationships in single-cell RNA-seq
Scaling gene regulatory network inference to massive datasets using Dask clusters
Identifying regulatory changes across different experimental conditions