Bulk WGCNA analysis with omicverse
CommunityDiscover gene co-expression networks.
Education & Research#bioinformatics#network analysis#bulk RNA-seq#hub genes#WGCNA#omicverse#gene co-expression
AuthorStarlitnightly
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Identifying biologically meaningful gene co-expression modules and hub genes from bulk RNA-seq data can be computationally intensive and require specialized algorithms. This Skill streamlines the Weighted Gene Co-expression Network Analysis (WGCNA) workflow.
Core Features & Use Cases
- Expression Data Preprocessing: Prepare and filter bulk expression data for WGCNA.
- Co-expression Network Construction: Build adjacency and topological overlap matrices to detect gene modules.
- Hub Gene Identification: Extract and visualize key hub genes within specific modules of interest.
- Use Case: Analyze bulk RNA-seq data from a disease cohort to find gene modules associated with disease progression, identify key hub genes within these modules, and correlate them with clinical traits.
Quick Start
Perform WGCNA on my 5xFAD expression data, visualize the resulting modules, and extract the top 10 hub genes from the 'lightgreen' module.
Dependency Matrix
Required Modules
omicversepandasstatsmodelsscanpymatplotlib
Components
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: Bulk WGCNA analysis with omicverse Download link: https://github.com/Starlitnightly/omicverse/archive/main.zip#bulk-wgcna-analysis-with-omicverse Please download this .zip file, extract it, and install it in the .claude/skills/ directory.