Infer gene regulatory networks from expression data using GRNBoost2 and GENIE3.
npx skills add https://github.com/crazymsn/academic-skills --skill arboreto-crazymsn
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/crazymsn/academic-skills/tree/main/academic-skills/arboreto Command: npx skills add https://github.com/crazymsn/academic-skills --skill arboreto-crazymsn
Infer gene regulatory networks (GRNs) from gene expression data to reveal transcription factor target gene relationships across samples, conditions, and cell types.
Provide an expression matrix to GRNBoost2 to generate the regulatory network.
High-intent search queries and answers about installing and using this skill.
Gene regulatory network inference from single-cell RNA-seq uses algorithms like GRNBoost2 or GENIE3 on an expression matrix. It outputs a TF-target-importance table revealing transcription factor relationships across cell types and conditions.
Scaling GRN inference for large gene expression datasets is handled through optional distributed computing with Dask and multi-core setups. This allows GRNBoost2 and GENIE3 to process bulk and single-cell data efficiently across clusters.
Yes, arboreto works with Dask for distributed computing. It supports optional distributed computing with Dask and multi-core setups to scale GRNBoost2 and GENIE3 network inference across large bulk and single-cell RNA-seq datasets.
You identify transcription factor target relationships with GRNBoost2 by providing a gene expression matrix. The algorithm computes regulatory importance scores and outputs a three-column TF-target-importance table for downstream analysis.
Yes, the inferred TF-target-importance network integrates with pySCENIC. The three-column output table is explicitly formatted for downstream pySCENIC analyses to validate and refine transcription factor target relationships.
Both GRNBoost2 and GENIE3 infer transcription factor target relationships from gene expression data. The Skill implements both algorithms to generate a three-column TF-target-importance network, allowing methodological comparison for downstream analysis.
galeepInfer gene regulatory networks from gene expression data using GRNBoost2 and GENIE3.
anthropicsFilter low-quality cells from single-cell RNA-seq data using MAD-based QC.
HolobiomicsLabInfer gene regulatory networks from expression data with GRNBoost2 or GENIE3.
Hung-3008Infer gene regulatory networks from expression data with GRNBoost2 or GENIE3.
