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

Yes, Arboreto is specifically optimized for the high dimensionality of single-cell data and is a core component used in the SCENIC pipeline.

How does Arboreto handle very large datasets?

It utilizes Dask for distributed computing, allowing the algorithm to scale from a single local machine to a multi-node cluster environment.

What algorithms does the Arboreto skill support?

It primarily supports GRNBoost2 (gradient boosting) for high-speed inference and GENIE3 (Random Forest) for traditional regression-based network modeling.

Does it require specific input formats?

Arboreto works with standard Python data structures like Pandas DataFrames and NumPy arrays, where genes are typically represented as columns.

Arboreto Gene Network Inference

Name: Arboreto Gene Network Inference
Author: BbgnsurfTech

byBbgnsurfTech

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データサイエンスとML

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

概要

Arboreto is a specialized capability for Claude designed to facilitate the inference of gene regulatory networks (GRNs) from complex gene expression data. By leveraging high-performance algorithms such as GRNBoost2 and GENIE3, it identifies critical transcription factor-target gene relationships across thousands of observations. This skill is particularly effective for bioinformaticians and data scientists working with bulk or single-cell RNA-seq data, offering built-in support for distributed computing via Dask to handle massive datasets that exceed local memory limits.

主な機能

3 GitHub stars
Optimized for both single-cell and bulk RNA-seq transcriptomics
Transcription factor filtering to refine regulatory relationship identification
Seamless integration with the pySCENIC pipeline for regulon analysis
Scalable GRN inference using GRNBoost2 and GENIE3 algorithms
Distributed computing support via Dask for multi-node cluster scaling

ユースケース

Identifying transcription factor targets in bulk RNA-seq experiments across different conditions
Scaling gene network modeling to high-dimensional datasets using distributed clusters
Mapping cell-type-specific regulatory networks from single-cell RNA-seq datasets

概要

主な機能

3 GitHub stars
Optimized for both single-cell and bulk RNA-seq transcriptomics
Transcription factor filtering to refine regulatory relationship identification
Seamless integration with the pySCENIC pipeline for regulon analysis
Scalable GRN inference using GRNBoost2 and GENIE3 algorithms
Distributed computing support via Dask for multi-node cluster scaling

ユースケース

Identifying transcription factor targets in bulk RNA-seq experiments across different conditions
Scaling gene network modeling to high-dimensional datasets using distributed clusters
Mapping cell-type-specific regulatory networks from single-cell RNA-seq datasets