Can this skill handle single-cell RNA-seq data?

Yes, Arboreto is a core component of the popular pySCENIC pipeline and is specifically designed to handle the scale and sparsity of single-cell datasets.

Which algorithm should I choose, GRNBoost2 or GENIE3?

GRNBoost2 is generally recommended because it is significantly faster and better optimized for large-scale datasets. GENIE3 is a classic Random Forest-based approach often used for cross-validation or specific comparative studies.

Why is the __main__ guard necessary when using Arboreto?

Arboreto utilizes Dask for parallel execution, which spawns multiple processes. The 'if __name__ == "__main__":' guard prevents recursive process creation and is required for stable execution.

What is Arboreto used for?

Arboreto is used to infer gene regulatory networks (GRNs) by identifying the most likely interactions between transcription factors and target genes based on gene expression patterns.

How do I scale Arboreto to a compute cluster?

By creating a Dask Distributed Client and passing it to the inference algorithm, you can scale the computation across multiple nodes in a high-performance computing environment.

Arboreto GRN Inference

Name: Arboreto GRN Inference
Author: x-cmd

byx-cmd

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데이터 과학 및 ML

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

소개

Arboreto is a specialized bioinformatics skill designed for inferring gene regulatory networks (GRNs) from gene expression data. It provides high-performance implementations of the GRNBoost2 and GENIE3 algorithms, which identify transcription factor-target gene interactions across various biological conditions. Built for scalability, it enables researchers to process massive datasets, such as single-cell RNA-seq (scRNA-seq), by distributing computations across local cores or remote Dask clusters. This skill is particularly useful for computational biologists needing to integrate regulatory network analysis into their Python-based research pipelines or the pySCENIC workflow.

주요 기능

Scalable architecture that transitions from local machines to distributed Dask clusters
High-performance GRN reconstruction using GRNBoost2 and GENIE3 algorithms
Optimized support for single-cell and bulk RNA-seq expression matrices
Transcription factor (TF) targeting for focused regulatory interaction analysis
Quantitative importance scoring to prioritize high-confidence regulatory links
8 GitHub stars

사용 사례

Reconstructing cell-type-specific regulatory networks from single-cell RNA-seq data
Identifying key transcription factors driving gene expression in bulk RNA-seq studies
Scaling gene network analysis for massive datasets using distributed cloud infrastructure

소개

주요 기능

Scalable architecture that transitions from local machines to distributed Dask clusters
High-performance GRN reconstruction using GRNBoost2 and GENIE3 algorithms
Optimized support for single-cell and bulk RNA-seq expression matrices
Transcription factor (TF) targeting for focused regulatory interaction analysis
Quantitative importance scoring to prioritize high-confidence regulatory links
8 GitHub stars

사용 사례

Reconstructing cell-type-specific regulatory networks from single-cell RNA-seq data
Identifying key transcription factors driving gene expression in bulk RNA-seq studies
Scaling gene network analysis for massive datasets using distributed cloud infrastructure