Infers gene regulatory networks from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3.
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
01Streamlined integration with the pySCENIC pipeline for regulon identification
02Scalable distributed computing via Dask for large-scale single-cell datasets
03High-performance GRN inference using the optimized GRNBoost2 algorithm
04Built-in utilities for transcription factor filtering and importance scoring
05Support for traditional Random Forest-based inference using GENIE3
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Casos de Uso
01Identifying cell-type-specific regulatory modules in complex biological tissues
02Comparing gene regulatory network shifts across different experimental conditions or time points
03Mapping transcription factor-target gene interactions in single-cell RNA-seq data
