How do I ensure reproducible results?

You should always provide a fixed integer to the 'seed' parameter in the grnboost2 or genie3 functions to ensure consistency across runs.

Does Arboreto require a cluster to run?

No, it runs efficiently on local multi-core machines by default, but can be configured to use Dask clusters for massive datasets.

Is Arboreto compatible with pySCENIC?

Yes, Arboreto serves as the primary inference engine for the SCENIC (Single-Cell Regulatory Network Inference and Clustering) workflow.

What is the main difference between GRNBoost2 and GENIE3?

GRNBoost2 uses gradient boosting and is optimized for speed and large datasets, while GENIE3 uses Random Forest and is the classic approach for GRN inference.

Can I use this skill for single-cell data?

Yes, Arboreto is specifically designed to scale to the large number of observations typical in single-cell RNA-seq datasets.

Arboreto Gene Regulatory Network Inference

Name: Arboreto Gene Regulatory Network Inference
Author: Activer007

byActiver007

0•

Data Science & ML

Infers gene regulatory networks (GRNs) from transcriptomics data using scalable machine learning algorithms like GRNBoost2 and GENIE3.

Arboreto is a specialized computational library designed for the large-scale inference of gene regulatory networks from gene expression data, supporting both bulk and single-cell RNA-seq analysis. By leveraging parallelized algorithms that scale from local machines to multi-node clusters via Dask, it allows researchers to identify transcription factor-target gene relationships with high efficiency. This skill is particularly useful for bioinformatics workflows requiring the discovery of regulatory interactions across thousands of cells or samples, providing a bridge between raw expression matrices and meaningful biological insights.

Key Features

01Scalable GRN inference using GRNBoost2 and GENIE3 algorithms

02Support for distributed computing via Dask integration

03Integration-ready output compatible with the SCENIC pipeline

04Seamless processing of bulk and single-cell RNA-seq datasets

050 GitHub stars

06Transcription factor filtering for targeted regulatory analysis

Use Cases

01Discovering novel transcription factor-target relationships in disease models

02Identifying cell-type-specific regulatory networks in single-cell transcriptomics

03Scaling gene network analysis from local workstations to HPC clusters

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add activer007/ordinary-claude-skills arboreto

For use in Claude.ai and ChatGPT

Download Skill