bio-temporal-genomics-periodicity-detection
Discovers periodic signals of unknown period in time-series omics data using Lomb-Scargle periodograms (scipy), autocorrelation, and wavelet time-frequency decomposition (pywt). Identifies dominant frequencies, handles irregularly sampled data, and detects transient periodicity. Use when searching for periodic patterns of unknown period length, analyzing cell cycle oscillations, or processing unevenly spaced time-series. Not for testing known 24-hour rhythms (see temporal-genomics/circadian-rhythms).
npx skills add BioTender-max/awesome-bio-agent-skills --skill periodicity-detection --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+, pwr 1.3+, 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 If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Periodicity Detection **"Find periodic patterns of unknown period in my time-series data"** → Compute frequency spectra using Lomb-Scargle periodograms (handles irregular sampling), identify significant spectral peaks, and detect transient periodicity via continuous wavelet transforms. - Python: `scipy.signal.lombscargle()` for Lomb-Scargle periodogram - Python: `pywt.cwt()` for wavelet time-frequency decomposition Discovers periodic signals of unknown frequency in time-series omics data. Handles irregular sampling, identifies dominant oscillation periods, and detects transient or time-varying periodicity through spectral and time-frequency methods. ## Core Workflow 1. Prepare time-series expression data (handle missing values, detrend if n
- Version Compatibility
- Core Workflow
- Lomb-Scargle Periodogram (scipy)
- Basic Lomb-Scargle
- Peak Detection and Significance
- False Alarm Probability (FAP)
- Genome-Wide Periodicity Screening
- Autocorrelation
- Wavelet Time-Frequency Decomposition (pywt)
- Continuous Wavelet Transform (CWT)
- Scalogram Visualization
- Detect Time-Varying Periodicity
- Welch Periodogram (Evenly Sampled)
- Permutation-Based FDR
What does the bio-temporal-genomics-periodicity-detection skill do?
Discovers periodic signals of unknown period in time-series omics data using Lomb-Scargle periodograms (scipy), autocorrelation, and wavelet time-frequency decomposition (pywt). Identifies dominant frequencies, handles irregularly sampled data, and detects transient periodicity. Use when searching for periodic patterns of unknown period length, analyzing cell cycle oscillations, or processing unevenly spaced time-series. Not for testing known 24-hour rhythms (see temporal-genomics/circadian-rhythms).
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill periodicity-detection --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.