bio-gene-regulatory-networks-differential-networks
Compare gene regulatory and co-expression networks between biological conditions to identify rewired regulatory relationships using DiffCorr. Detects gained, lost, and reversed gene-gene correlations between conditions. Use when comparing co-expression networks between disease vs control, treatment conditions, or developmental stages.
npx skills add BioTender-max/awesome-bio-agent-skills --skill differential-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: matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scipy 1.12+, statsmodels 0.14+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - 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. # Differential Networks **"Compare gene co-expression networks between my disease and control groups"** → Test whether gene-gene correlations differ significantly between two conditions using Fisher's z-transform, identifying gained, lost, and reversed regulatory relationships. - R: `DiffCorr::comp.2.cc.fdr()` for differential correlation analysis - Python: custom Fisher z-test with `scipy.stats` and `statsmodels` for FDR correction Compare co-expression and regulatory networks between biological conditions to identify rewired gene-gene relationships. ## DiffCorr Workflow DiffCorr uses Fisher's z-transform to test whether the correlation between two genes differs sig
- Version Compatibility
- DiffCorr Workflow
- Required Libraries
- Input Preparation
- Run DiffCorr Analysis
- Parse and Classify Results
- Identify Rewired Hub Genes
- DGCA (Alternative)
- Python NetworkX Approach
- Visualize Differential Network
- Statistical Considerations
- Related Skills
What does the bio-gene-regulatory-networks-differential-networks skill do?
Compare gene regulatory and co-expression networks between biological conditions to identify rewired regulatory relationships using DiffCorr. Detects gained, lost, and reversed gene-gene correlations between conditions. Use when comparing co-expression networks between disease vs control, treatment conditions, or developmental stages.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill differential-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.