arboreto

Topics in the source

• Arboreto
• Overview
• Quick Start
• Core Capabilities
• 1. Basic GRN Inference
• 2. Algorithm Selection
• 3. Distributed Computing
• Installation

Arboreto is a computational library for inferring gene regulatory networks (GRNs) from gene expression data using parallelized algorithms that scale from single machines to multi-node clusters. Core capability: Identify which transcription factors (TFs) regulate which target genes based on expression patterns across observations (cells, samples, conditions). Install arboreto: Basic GRN inference: Critical: Always use if name == 'main': guard because Dask spawns new processes. For standard GRN inference workflows including: Input data preparation (Pandas DataFrame or NumPy array) Running inference with GRNBoost2 or GENIE3 Filtering by transcription factors Output format and interpretation See: references/basicinference.md Use the ready-to-run script: scripts/basicgrninference.py for standard inference tasks: Arboreto provides two algorithms: GRNBoost2 (Recommended): Fast gradient boosting-based inference Optimized for large datasets (10k+ observations) Default choice for most analyses GENIE3: Random Forest-based inference Original multiple regression approach Use for comparison or validation Quick comparison: For detailed algorithm comparison, parameters, and selection guidance: ref

This is an excerpt. Refer to the complete source and its current requirements.