Infer gene regulatory networks from RNA-seq data using GRNBoost2 and GENIE3.
npx skills add https://github.com/Sologa/codex-pipeline --skill arboreto-sologa
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/arboreto Command: npx skills add https://github.com/Sologa/codex-pipeline --skill arboreto-sologa
This Skill automates the complex process of inferring gene regulatory networks (GRNs) from gene expression data, helping researchers understand gene interactions and regulatory mechanisms.
Use the arboreto skill to infer a gene regulatory network from the expression data in 'expression_data.tsv' and save the output to 'network.tsv'.
High-intent search queries and answers about installing and using this skill.
You can infer gene regulatory networks from RNA-seq data by using algorithms like GRNBoost2 and GENIE3 to identify transcription factor-target relationships from gene expression matrices. This Skill automates that process and outputs the network to a TSV file.
Building a gene regulatory network from single-cell transcriptomics data is best handled by scalable algorithms like GRNBoost2 that identify transcription factor-target relationships. This Skill supports single-cell data and uses Dask for parallel processing to manage large-scale datasets.
Yes, you can use Dask for distributed GRN inference on large gene expression datasets. This Skill leverages Dask and distributed computing to scale network inference, allowing you to process large-scale transcriptomics data efficiently.
Yes, you need scikit-learn and pandas to identify transcription factor-target relationships. Pandas handles gene expression data manipulation, while scikit-learn executes the machine learning models required for GRNBoost2 and GENIE3 algorithms.
GRNBoost2 supports both bulk and single-cell RNA-seq data for network inference. This Skill applies scalable algorithms to both transcriptomics data types to identify regulatory mechanisms and transcription factor-target gene relationships.
Gene regulatory network computations on large transcriptomics datasets run slow without distributed computing. This Skill uses Dask for parallel processing, which scales the GRNBoost2 and GENIE3 algorithms to handle large-scale data efficiently.
jasrajtulsiInfer gene regulatory networks from transcriptomics expression matrices using GRNBoost2 or GENIE3.
omicverseInfer gene regulatory networks from single-cell RNA sequencing data.
tassiovaleScale data processing beyond RAM limits with Dask and Python 3.10+.
OwnLabAIInfer gene regulatory networks from RNA-seq data using GRNBoost2 or GENIE3.
