What is the difference between GRNBoost2 and GENIE3 in Arboreto?

GRNBoost2 is significantly faster (10-100x) as it uses gradient boosting with early stopping, while GENIE3 uses a classical Random Forest approach which is better for smaller datasets or reproducing older studies.

How do I ensure reproducible results when using this skill?

Always specify a 'seed' parameter in your algorithm function calls (e.g., grnboost2(..., seed=42)) to prevent variation between runs caused by stochastic algorithm behavior.

Can I use Arboreto for large-scale single-cell datasets?

Yes, Arboreto is specifically designed to scale from single machines to multi-node clusters using the Dask distributed computing framework to handle memory-intensive genomic data.

What is the required data format for Arboreto?

Expression data should be provided as a matrix where rows represent observations (cells or samples) and columns represent genes. If your data has genes as rows, you must transpose it first.

Arboreto GRN Inference

Name: Arboreto GRN Inference
Author: lifangda

bylifangda

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Data Science & ML

Infers gene regulatory networks from expression data using high-performance machine learning algorithms like GRNBoost2 and GENIE3.

About

Arboreto provides a robust framework for reconstructing gene regulatory networks (GRNs) from single-cell or bulk RNA-seq data. By leveraging Dask for distributed computing and optimized algorithms, it enables researchers to identify transcription factor-target relationships across massive datasets. This skill assists users in generating analysis scripts, troubleshooting installation or memory issues, configuring distributed Dask clusters, and integrating GRN inference into larger bioinformatics pipelines such as pySCENIC.

Key Features

Distributed computing support via Dask for single-cell datasets
Automated script generation for genomic data preprocessing
Scalable GRN inference using GRNBoost2 and GENIE3 algorithms
Troubleshooting guides for memory management and cluster connectivity
16 GitHub stars
Integration with pySCENIC for comprehensive regulon prediction

Use Cases

Benchmarking regulatory network performance between different ML algorithms
Setting up multi-node distributed clusters for high-throughput genomic analysis
Identifying transcription factor targets from scRNA-seq expression matrices

About

Key Features

Distributed computing support via Dask for single-cell datasets
Automated script generation for genomic data preprocessing
Scalable GRN inference using GRNBoost2 and GENIE3 algorithms
Troubleshooting guides for memory management and cluster connectivity
16 GitHub stars
Integration with pySCENIC for comprehensive regulon prediction

Use Cases

Benchmarking regulatory network performance between different ML algorithms
Setting up multi-node distributed clusters for high-throughput genomic analysis
Identifying transcription factor targets from scRNA-seq expression matrices