Infer gene regulatory networks from expression data using GRNBoost2 or GENIE3.
npx skills add https://github.com/jackspace/ClaudeSkillz --skill arboreto
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/jackspace/ClaudeSkillz/tree/main/skills/scientific-pkg-arboreto Command: npx skills add https://github.com/jackspace/ClaudeSkillz --skill arboreto
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3).
Run grnboost2 on an expression matrix with a seed for reproducibility.
High-intent search queries and answers about installing and using this skill.
Gene regulatory network inference identifies transcription factor–target gene relationships from expression data. Arboreto implements scalable algorithms (GRNBoost2, GENIE3) that process Pandas DataFrames or NumPy arrays to produce a TF–target–importance table, supporting both bulk and single-cell RNA-seq datasets.
Yes. Arboreto supports distributed computing with Dask to scale GRNBoost2 and GENIE3 algorithms across large expression matrices, enabling reproducible inference through seed control and parallel execution on single-cell and bulk RNA-seq data.
Both algorithms infer gene regulatory networks from expression data; GRNBoost2 and GENIE3 are gradient boosting and tree-based approaches respectively. Arboreto provides both implementations with TF filtering and reproducibility options to let you select the best fit for your biological question.
Transcription factor filtering is an optional preprocessing step in Arboreto that restricts the inference algorithm to known or candidate TFs, reducing computational cost and improving biological relevance of the inferred regulatory interactions.
Arboreto enables reproducible GRN inference through seed parameters and controlled parallel execution with Dask and distributed computing, ensuring consistent TF–target–importance results across runs.
Arboreto accepts Pandas DataFrames and NumPy arrays as input for GRN inference, allowing flexible data preparation from expression matrices in standard bioinformatics formats before passing to GRNBoost2 or GENIE3 algorithms.
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