Arboreto GRN Inference

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Arboreto is a specialized computational tool for identifying transcription factor-target gene relationships within large-scale gene expression datasets. It provides a robust interface for inferring gene regulatory networks (GRNs) using parallelized algorithms that scale seamlessly from local machines to multi-node clusters via Dask. Whether analyzing bulk RNA-seq or high-throughput single-cell RNA-seq, Arboreto enables researchers to uncover complex regulatory interactions and build high-confidence biological models with precision and speed.

Características Principales

• Streamlined integration with the pySCENIC pipeline for regulon identification
• Scalable distributed computing via Dask for large-scale single-cell datasets
• High-performance GRN inference using the optimized GRNBoost2 algorithm
• Built-in utilities for transcription factor filtering and importance scoring
• Support for traditional Random Forest-based inference using GENIE3
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Casos de Uso

• Identifying cell-type-specific regulatory modules in complex biological tissues
• Comparing gene regulatory network shifts across different experimental conditions or time points
• Mapping transcription factor-target gene interactions in single-cell RNA-seq data