Infer gene regulatory networks from expression data with GRNBoost2 or GENIE3.
npx skills add https://github.com/Hung-3008/agusta --skill arboreto-hung-3008
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/arboreto Command: npx skills add https://github.com/Hung-3008/agusta --skill arboreto-hung-3008
Arboreto enables scalable inference of gene regulatory networks from gene expression data, enabling discovery of transcription factor–target relationships across large datasets.
Install Arboreto and run GRN inference on your expression matrix using GRNBoost2 to produce a TF-target network.
High-intent search queries and answers about installing and using this skill.
Infer gene regulatory networks from single-cell RNA-seq data by applying scalable algorithms like GRNBoost2 or GENIE3 to your expression matrix, revealing transcription factor–target relationships across large datasets.
Dask enables distributed computing for GRN inference, allowing you to scale gene regulatory network analysis across local clients or remote clusters when processing large bulk or single-cell expression datasets.
GRNBoost2 and GENIE3 are both algorithms for inferring transcription factor–target relationships from expression matrices, differing primarily in their computational approach to reconstructing gene regulatory networks.
Format expression data with genes as columns in a Pandas or NumPy matrix to run gene regulatory network inference, and provide a transcription factor list if available to guide the analysis.
Arboreto produces output formats designed for downstream workflows such as pySCENIC integration, enabling reproducible results and seamless transition to subsequent gene regulatory network analysis steps.
A transcription factor list guides gene regulatory network inference by specifying candidate regulators, helping the algorithm accurately map transcription factor–target relationships from your RNA-seq expression data.
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