bio-temporal-genomics-trajectory-modeling
Models continuous temporal trajectories from bulk or time-resolved omics data using generalized additive models (mgcv), spline regression, and changepoint detection (segmented, ruptures). Fits smooth gene expression curves and tests trajectory differences between conditions. Use when fitting non-linear temporal models to bulk time-series data or comparing developmental trajectories across conditions. Not for single-cell pseudotime (see single-cell/trajectory-inference).
npx skills add BioTender-max/awesome-bio-agent-skills --skill trajectory-modeling --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), numpy 1.26+, pandas 2.2+, scanpy 1.10+ 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. # Temporal Trajectory Modeling **"Fit smooth curves to my gene expression time series"** → Model continuous temporal trajectories using generalized additive models (GAMs) or spline regression, test for condition differences, and detect changepoints where dynamics shift abruptly. - R: `mgcv::gam()` for GAM fitting with smooth terms - Python: `ruptures` for changepoint detection in temporal profiles Fits smooth non-linear curves to gene expression time series using generalized additive models (GAMs) and detects abrupt changes in temporal dynamics using changepoint algorithms. ## Core Workflow 1. Prepare expression data with timepoint and condition metadata 2. Fit GAM or spline models per
- Version Compatibility
- Core Workflow
- mgcv GAM (R)
- Basic GAM Fitting (R stats (base)+)
- Condition Comparison with GAM (R stats (base)+)
- Model Diagnostics (R stats (base)+)
- Prediction and Visualization (R stats (base)+)
- Genome-Wide GAM Fitting (R stats (base)+)
- tradeSeq (R/Bioconductor)
- segmented (R)
- ruptures (Python)
- Binary Segmentation Alternative (R stats (base)+)
- Genome-Wide Changepoint Detection
- Model Comparison
What does the bio-temporal-genomics-trajectory-modeling skill do?
Models continuous temporal trajectories from bulk or time-resolved omics data using generalized additive models (mgcv), spline regression, and changepoint detection (segmented, ruptures). Fits smooth gene expression curves and tests trajectory differences between conditions. Use when fitting non-linear temporal models to bulk time-series data or comparing developmental trajectories across conditions. Not for single-cell pseudotime (see single-cell/trajectory-inference).
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill trajectory-modeling --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.