bio-temporal-genomics-circadian-rhythms
Detects circadian and ultradian rhythms in time-series omics data using CosinorPy cosinor models, MetaCycle (JTK_CYCLE, ARSER), and RAIN non-parametric tests. Fits cosine models to estimate phase and amplitude, tests rhythmicity significance at pre-specified periods. Use when testing for 24-hour or other known-period oscillations in circadian, feeding-fasting, or light-dark cycle experiments. Not for unknown-period discovery (see temporal-genomics/periodicity-detection).
npx skills add BioTender-max/awesome-bio-agent-skills --skill circadian-rhythms --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: R stats (base), pandas 2.2+, 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. # Circadian Rhythm Detection **"Test which genes in my time-course data have circadian rhythms"** → Fit cosinor models at a specified period (typically 24h) to expression time series, estimating amplitude, phase (acrophase), and rhythmicity significance for each gene. - Python: `CosinorPy.cosinor.fit_group()` for cosinor regression - R: `MetaCycle::meta2d()` for multi-method rhythmicity testing (JTK_CYCLE + ARSER) Identifies periodic gene expression patterns at known periods (typically 24h) using cosinor regression, non-parametric rhythmicity tests, and meta-analysis approaches combining multiple methods. ## Core Workflow 1. Prepare time-series expression matrix (genes x timepoints) 2. Fit cos
- Version Compatibility
- Core Workflow
- CosinorPy (Python)
- Single-Component Cosinor
- Multi-Component Cosinor
- Population-Mean Cosinor
- Batch Processing
- MetaCycle (R)
- MetaCycle Output Interpretation (R stats (base)+)
- RAIN (R/Bioconductor)
- DiscoRhythm (R/Bioconductor)
- Parameter Guide
- Method Selection
- Related Skills
What does the bio-temporal-genomics-circadian-rhythms skill do?
Detects circadian and ultradian rhythms in time-series omics data using CosinorPy cosinor models, MetaCycle (JTK_CYCLE, ARSER), and RAIN non-parametric tests. Fits cosine models to estimate phase and amplitude, tests rhythmicity significance at pre-specified periods. Use when testing for 24-hour or other known-period oscillations in circadian, feeding-fasting, or light-dark cycle experiments. Not for unknown-period discovery (see temporal-genomics/periodicity-detection).
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill circadian-rhythms --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.