Infer gene regulatory networks from RNA-seq data using GRNBoost2 or GENIE3.
npx skills add https://github.com/OwnLabAI/ownlab --skill arboreto-ownlabai
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/arboreto Command: npx skills add https://github.com/OwnLabAI/ownlab --skill arboreto-ownlabai
Arboreto enables scalable inference of gene regulatory networks from gene expression data, turning raw counts into interpretable regulatory relationships.
Run GRN inference on a gene expression matrix to generate TF–target regulatory links.
High-intent search queries and answers about installing and using this skill.
To infer gene regulatory networks from scRNA-seq data, apply GRNBoost2 or GENIE3 algorithms to a gene expression matrix to identify transcription factor–target relationships.
To scale GRN inference for large cohorts, use a Dask client for distributed computing alongside Arboreto, allowing the process to run from a single machine to cluster environments.
Yes, Arboreto supports both bulk RNA-seq and single-cell RNA-seq workflows to construct regulatory networks for cell-type-specific analyses and comparative studies across conditions.
Yes, you can constrain gene regulatory network inference by providing optional TF lists, limiting the algorithm to identify relationships only for specified transcription factors.
No, a Dask client is optional; you can run GRNBoost2 or GENIE3 on a single machine using Python and Arboreto, but Dask is required for distributed computing on cluster environments.
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