Should I use GRNBoost2 or GENIE3?

GRNBoost2 is generally recommended as it is faster and optimized for large-scale datasets (10k+ observations), while GENIE3 is the classic Random Forest-based approach.

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

Because Arboreto uses Dask to spawn new processes, this guard is required by Python's multiprocessing module to ensure processes are initialized correctly.

Can Arboreto handle single-cell RNA-seq data?

Yes, it is highly optimized for the high dimensionality of single-cell data and is a core component of the SCENIC pipeline for single-cell regulatory analysis.

What is the primary purpose of the Arboreto skill?

Arboreto is used to identify which transcription factors regulate specific target genes by analyzing expression patterns in transcriptomics data.

How does Arboreto handle large datasets?

It utilizes Dask and distributed computing to scale from a single local machine to multi-node clusters, preventing memory errors and reducing computation time.

Arboreto GRN Inference

Name: Arboreto GRN Inference
Author: Activer007

byActiver007

데이터 과학 및 ML

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

소개

Arboreto is a specialized computational tool for inferring gene regulatory networks (GRNs) from large-scale gene expression datasets, such as single-cell and bulk RNA-seq. By leveraging parallelized algorithms like GRNBoost2 and GENIE3, it identifies relationships between transcription factors and target genes with high efficiency. This skill is ideal for bioinformaticians and data scientists who need to perform complex regulatory analysis that scales from local multi-core machines to distributed Dask clusters, providing the necessary patterns for reliable network reconstruction and integration with pipelines like pySCENIC.

주요 기능

Support for both single-cell and bulk RNA-seq transcriptomics data
Integration-ready outputs for downstream regulatory analysis pipelines
High-performance GRN inference using GRNBoost2 and GENIE3 algorithms
Scalable computation across local multi-core systems and Dask clusters
Targeted inference through transcription factor filtering and selection
0 GitHub stars

사용 사례

Comparing gene regulatory network shifts across experimental conditions
Mapping cell-type-specific transcription factor targets in single-cell datasets
Processing massive transcriptomics datasets requiring distributed computing

소개

주요 기능

Support for both single-cell and bulk RNA-seq transcriptomics data
Integration-ready outputs for downstream regulatory analysis pipelines
High-performance GRN inference using GRNBoost2 and GENIE3 algorithms
Scalable computation across local multi-core systems and Dask clusters
Targeted inference through transcription factor filtering and selection
0 GitHub stars

사용 사례

Comparing gene regulatory network shifts across experimental conditions
Mapping cell-type-specific transcription factor targets in single-cell datasets
Processing massive transcriptomics datasets requiring distributed computing