Infer gene regulatory networks from expression data using GRNBoost2 and GENIE3.
npx skills add https://github.com/ovachiever/droid-tings --skill arboreto-ovachiever
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/ovachiever/droid-tings/tree/main/skills/arboreto Command: npx skills add https://github.com/ovachiever/droid-tings --skill arboreto-ovachiever
Arboreto infers gene regulatory networks (GRNs) from gene expression data, enabling discovery of transcription factor–target gene relationships and regulatory interactions at scale across bulk and single-cell datasets.
Run basic GRN inference on an expression matrix 'expression.csv' with TF list 'tfs.txt': python scripts/basic_grn_inference.py expression.csv output_network.tsv --tf-file tfs.txt
High-intent search queries and answers about installing and using this skill.
Gene regulatory network inference identifies transcription factor–target relationships in expression data using algorithms like GRNBoost2 and GENIE3. Arboreto implements scalable GRN inference on bulk and single-cell RNA-seq, outputting TF-target links with importance scores for downstream analysis across samples and conditions.
Yes, GRN inference works on single-cell RNA-seq to derive regulatory programs across cell types. Arboreto applies GRNBoost2 and GENIE3 with distributed computing support, enabling scalable network inference on large scRNA-seq datasets with optional transcription factor filtering.
GRN inference accepts flexible input formats including DataFrames and NumPy arrays for expression matrices. Arboreto processes expression data with optional transcription factor lists, outputting standardized TF-target-importance links in tab-separated format.
Both GRNBoost2 and GENIE3 infer regulatory relationships from expression data; GRNBoost2 typically offers faster computation on large datasets. Arboreto implements both algorithms with distributed computing, letting you choose based on dataset scale and computational resources.
Yes, transcription factor filtering narrows inference to specific regulatory proteins. Arboreto supports optional TF filtering during GRN inference, allowing you to focus network analysis on known or curated transcription factors relevant to your biological question.
GRN inference at scale requires distributed computing for large expression matrices. Arboreto supports Dask-backed computation, enabling scalable network inference on bulk RNA-seq and single-cell datasets with thousands of genes and samples.
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