Does this skill support distributed computing?

Yes, Arboreto leverages Dask to scale computations across multiple local cores or large-scale multi-node clusters.

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

Because Arboreto uses Dask for parallel processing, this guard is critical to prevent the spawning of infinite subprocesses when running in a distributed environment.

What algorithms does Arboreto support?

Arboreto supports GRNBoost2 (gradient boosting) and GENIE3 (Random Forest) for inferring regulatory links between transcription factors and target genes.

Can I use Arboreto for single-cell data?

Yes, it is specifically optimized for the high dimensionality of single-cell RNA-seq datasets and serves as a core component of the popular pySCENIC pipeline.

Arboreto Gene Regulatory Network Inference

Name: Arboreto Gene Regulatory Network Inference
Author: Activer007

byActiver007

数据科学与机器学习

Infers gene regulatory networks (GRNs) from gene expression data using scalable GRNBoost2 and GENIE3 algorithms.

关于

Arboreto is a specialized computational library designed to identify transcription factor-target gene relationships by analyzing expression patterns across various samples or cells. It provides high-performance implementations of the GRNBoost2 and GENIE3 algorithms, optimized for massive datasets like single-cell RNA-seq through distributed computing with Dask. This skill is essential for bioinformatics and computational biology workflows, allowing researchers to reconstruct complex regulatory landscapes and scale their analysis seamlessly from local machines to multi-node clusters.

主要功能

Scalable GRN inference using GRNBoost2 and GENIE3
0 GitHub stars
Distributed computation integration via Dask for large-scale datasets
Support for bulk and single-cell RNA-seq transcriptomics
Seamless integration with the pySCENIC regulatory analysis pipeline
Automated transcription factor-target gene relationship identification

使用场景

Reconstructing cell-type-specific gene regulatory networks in scRNA-seq
Identifying key transcription factors in bulk transcriptomics studies
Comparative regulatory analysis between control and treatment conditions

关于

主要功能

Scalable GRN inference using GRNBoost2 and GENIE3
0 GitHub stars
Distributed computation integration via Dask for large-scale datasets
Support for bulk and single-cell RNA-seq transcriptomics
Seamless integration with the pySCENIC regulatory analysis pipeline
Automated transcription factor-target gene relationship identification

使用场景

Reconstructing cell-type-specific gene regulatory networks in scRNA-seq
Identifying key transcription factors in bulk transcriptomics studies
Comparative regulatory analysis between control and treatment conditions