Infer gene regulatory networks from expression data using GRNBoost2 or GENIE3.
npx skills add https://github.com/dralkh/seerai --skill arboreto-dralkh
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/dralkh/seerai/tree/main/skills/arboreto Command: npx skills add https://github.com/dralkh/seerai --skill arboreto-dralkh
Arboreto turns expression matrices into gene regulatory networks, helping researchers identify which transcription factors likely regulate each target gene without manually comparing thousands of relationships.
Use Arboreto to infer a gene regulatory network from your expression table by choosing GRNBoost2, optionally supplying a transcription factor list, and saving the resulting TF-target importance table.
High-intent search queries and answers about installing and using this skill.
To infer a gene regulatory network from single-cell RNA-seq data, you can use GRNBoost2 to rank transcription factor to target gene relationships. Provide your expression matrix as a DataFrame, NumPy array, or sparse CSC matrix with explicit gene names to output a ranked TF-target table.
The best way to scale gene network inference for large transcriptomic datasets is using distributed Dask execution. This allows you to scale GRNBoost2 or GENIE3 computations from a standard laptop to a full cluster while processing bulk or single-cell RNA-seq expression matrices.
Yes, you can restrict gene regulatory network inference to specific transcription factors by supplying an optional TF list. This TF filtering limits the GRNBoost2 or GENIE3 search space to only evaluate relationships involving your specified transcription factors.
Yes, GRNBoost2 works with both bulk and single-cell RNA-seq expression matrices. It applies to comparative condition analysis workflows and infers gene regulatory networks by ranking transcription factor to target relationships across different expression data formats.
To ensure reproducible results when inferring gene regulatory networks, you can use the reproducible seeding option before running GRNBoost2 or GENIE3. You can also apply optional top-link limits to generate cleaner, consistent TF-target importance tables.
You should use GENIE3 instead of GRNBoost2 for transcription factor target ranking when you need to validate inferred networks or compare results against published literature. Both methods rank TF-target relationships from expression data but offer different algorithmic approaches.
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