bio-gene-regulatory-networks-coexpression-networks
Build weighted gene co-expression networks to identify modules of co-regulated genes and relate them to phenotypes using WGCNA and CEMiTool. Detects hub genes and module-trait relationships from bulk or single-cell expression data. Use when finding co-expression modules, identifying hub genes, or relating gene networks to clinical or experimental variables.
npx skills add BioTender-max/awesome-bio-agent-skills --skill coexpression-networks --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: WGCNA 1.72+, CEMiTool 1.26+ Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion("<pkg>")` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Co-expression Networks **"Find co-expression modules and hub genes from my RNA-seq data"** → Build a weighted gene co-expression network, detect modules of co-regulated genes via hierarchical clustering, and correlate modules with sample traits to identify hub genes. - R: `WGCNA::blockwiseModules()` for network construction and module detection - R: `CEMiTool::cemitool()` for automated co-expression analysis Build weighted gene co-expression networks to identify modules of co-regulated genes and relate them to sample traits. ## WGCNA Workflow ### Required Libraries ```r library(WGCNA) options(stringsAsFactors = FALSE) allowWGCNAThreads() ``` ### Input Preparation ```r # Expression matrix: genes as columns, samples as rows (WGCNA convention) expr_data <- read.csv('normalized_counts.csv', r
- Version Compatibility
- WGCNA Workflow
- Required Libraries
- Input Preparation
- Soft-Thresholding Power Selection
- Network Construction and Module Detection
- Module Eigengenes and Trait Relationships
- Hub Gene Identification
- Export for Cytoscape
- CEMiTool (Automated Analysis)
- hdWGCNA (Single-Cell)
- PyWGCNA (Python Alternative)
- Statistical Considerations
- Related Skills
What does the bio-gene-regulatory-networks-coexpression-networks skill do?
Build weighted gene co-expression networks to identify modules of co-regulated genes and relate them to phenotypes using WGCNA and CEMiTool. Detects hub genes and module-trait relationships from bulk or single-cell expression data. Use when finding co-expression modules, identifying hub genes, or relating gene networks to clinical or experimental variables.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill coexpression-networks --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.