Infer gene regulatory networks from expression matrices using GRNBoost2 or GENIE3.
npx skills add https://github.com/shushuzn/Rairos --skill arboreto-shushuzn
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/shushuzn/Rairos/tree/main/skills/arboreto Command: npx skills add https://github.com/shushuzn/Rairos --skill arboreto-shushuzn
Infer gene regulatory networks (TF-to-target relationships) from gene expression data so you can discover candidate regulatory interactions without manual feature engineering.
Run GRNBoost2 on your expression matrix file by telling the AI to execute: python scripts/basic_grn_inference.py expression_data.tsv output_network.tsv --tf-file tfs.txt --seed 777
High-intent search queries and answers about installing and using this skill.
To infer gene regulatory networks from single-cell RNA-seq data, use this Skill to estimate transcription factor-to-target regulatory links with importance scores from your gene expression matrix. It applies scalable parallel execution via Dask to handle large scRNA-seq datasets.
Yes, you can use either GRNBoost2 or GENIE3 for transcription factor regulatory network inference. Choose GRNBoost2 for large datasets like 10k+ observations, and use GENIE3 for comparison and validation of your predicted regulatory interactions.
For gene regulatory network inference, format your expression matrices with genes as columns. You can also include optional gene or transcription factor metadata files to restrict the inference to known TFs during the regulatory link prediction process.
Yes, distributed computing supports large-scale bulk RNA-seq gene regulatory network inference. You can run inference on local multi-core systems via Dask or deploy on a remote Dask cluster to process large expression matrices efficiently.
The best way to compare gene regulatory networks across multiple conditions is to run TF-to-target inference separately for each condition using GRNBoost2 or GENIE3, then contrast the resulting importance scores to identify condition-specific regulatory interactions.
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