Infer gene regulatory networks from expression data using GRNBoost2 and GENIE3.
npx skills add https://github.com/tassiovale/claude-code-kit --skill arboreto-tassiovale
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/arboreto Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill arboreto-tassiovale
This Skill automates the inference of gene regulatory networks from gene expression data, saving researchers time and providing insights into transcription factor-target gene relationships.
Install arboreto and run the script: python scripts/basic_grn_inference.py expression_data.tsv output_network.tsv --tf-file tfs.txt --seed 777 --limit 5000
High-intent search queries and answers about installing and using this skill.
To infer gene regulatory networks from large-scale RNA-seq data, you can use scalable algorithms like GRNBoost2 and GENIE3. This approach applies to both bulk and single-cell transcriptomics to identify transcription factor-target gene relationships.
Identifying transcription factor-target relationships in single-cell RNA-seq is best done using scalable network inference algorithms. Applying distributed computing frameworks like dask allows efficient processing of large-scale transcriptomics datasets to map regulatory interactions.
To run gene regulatory network inference with a list of transcription factors, execute the inference script with your expression data TSV and a specified TF file. You can set parameters like a random seed and limit the number of genes for controlled processing.
Yes, gene regulatory network inference supports distributed computing for large datasets. By utilizing dask and distributed frameworks alongside scikit-learn and scipy, the process scales efficiently to handle large-scale transcriptomics data analysis.
Both GRNBoost2 and GENIE3 are scalable algorithms used for transcriptomics analysis to infer gene regulatory networks. They are utilized within a distributed computing environment to efficiently process large-scale gene expression data and identify regulatory interactions.
jasrajtulsiInfer gene regulatory networks from transcriptomics expression matrices using GRNBoost2 or GENIE3.
inflexa-aiConstruct gene co-expression networks and identify hub genes from expression matrices.
galeepInfer gene regulatory networks from gene expression data using GRNBoost2 and GENIE3.
HolobiomicsLabInfer gene regulatory networks from expression data with GRNBoost2 or GENIE3.
