Infer gene regulatory networks from RNA-seq data with GRNBoost2 and GENIE3.
npx skills add https://github.com/JosephWoodall/noosphere --skill arboreto-josephwoodall
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/arboreto Command: npx skills add https://github.com/JosephWoodall/noosphere --skill arboreto-josephwoodall
Infers gene regulatory networks from gene expression data using scalable algorithms, enabling researchers to uncover regulatory relationships at scale.
Run grnboost2 on your expression matrix to infer a gene regulatory network and export the results as a tab-separated file.
High-intent search queries and answers about installing and using this skill.
You can infer gene regulatory networks from single-cell RNA-seq data by applying scalable algorithms like GRNBoost2 directly to your expression matrix to calculate TF-target importance scores.
Scaling GRN inference for large gene expression datasets is achieved by using distributed computing frameworks like Dask, which allow the underlying algorithms to process massive matrices efficiently.
Yes, Arboreto supports bulk RNA-seq workflows. It processes both bulk and single-cell expression data to generate regulatory networks using algorithms like GRNBoost2 and GENIE3.
Yes, GRNBoost2 supports distributed computing with Dask. This integration allows you to handle large datasets during gene regulatory network inference without running into memory limitations.
To export inferred TF-target networks for downstream analysis, run the inference algorithm on your expression matrix and save the resulting TF-target importance scores as a tab-separated file.
AlterLab-IEUInfer gene regulatory networks from RNA-seq data using GRNBoost2 and GENIE3.
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