How do I ensure reproducible results with Arboreto?

You should always set a fixed 'seed' parameter in the grnboost2 or genie3 functions to ensure the stochastic nature of the algorithms yields consistent results across different runs.

What is the recommended algorithm for most transcriptomics tasks?

GRNBoost2 is generally recommended because it is significantly faster and more memory-efficient than GENIE3 while maintaining high accuracy for regulatory network inference.

Does this skill require specific data formats?

It typically requires a gene expression matrix (as a Pandas DataFrame or NumPy array) where genes are columns and observations (cells or samples) are rows.

What algorithms does Arboreto support?

Arboreto supports GRNBoost2, which uses gradient boosting for speed and performance, and GENIE3, which uses the classic random forest-based regression approach.

Can Arboreto handle large-scale single-cell datasets?

Yes, Arboreto is built on Dask, allowing it to scale from a single laptop to large high-performance computing clusters to handle datasets with tens of thousands of cells.

Arboreto GRN Inference

Name: Arboreto GRN Inference
Author: Yezez9

byYezez9

0•

数据科学与机器学习

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

Arboreto is a specialized computational biology tool designed to identify transcription factor-target gene relationships within large-scale gene expression datasets. By leveraging parallelized algorithms such as GRNBoost2 and GENIE3, it scales efficiently from local multi-core machines to multi-node clusters via Dask. This skill is essential for bioinformaticians and researchers analyzing single-cell or bulk RNA-seq data to uncover complex regulatory interactions, offering high performance and integration with common genomics pipelines like pySCENIC.

主要功能

01Distributed computing support via Dask for processing massive single-cell datasets

02Scalable Gene Regulatory Network (GRN) inference using GRNBoost2 and GENIE3

030 GitHub stars

04Transcription factor filtering to focus analysis on known regulators

05High-performance gradient boosting and random forest implementations

06Standardized output format containing TF-target pairs and importance scores

使用场景

01Mapping cell-type-specific regulatory networks in single-cell RNA-seq studies

02Identifying key transcription factors and their targets in bulk RNA-seq samples

03Performing comparative regulatory analysis across different experimental conditions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yezez9/research-agent arboreto

For use in Claude.ai and ChatGPT