bio-workflows-timecourse-pipeline
End-to-end time-course analysis from expression matrix to temporal patterns and enrichment. Covers temporal DE, Mfuzz soft clustering, optional rhythm detection, GAM trajectory fitting, and per-cluster pathway enrichment. Use when analyzing bulk time-series expression experiments from any omics platform.
npx skills add BioTender-max/awesome-bio-agent-skills --skill timecourse-pipeline --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: DESeq2 1.42+, clusterProfiler 4.10+, limma 3.58+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, 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. # Time-Course Analysis Pipeline **"Analyze my time-course expression data end-to-end"** → Orchestrate temporal differential expression, Mfuzz soft clustering, optional circadian rhythm detection, GAM trajectory fitting, changepoint detection, and per-cluster pathway enrichment. Complete workflow from expression matrix through temporal differential expression, soft clustering, optional rhythm detection, trajectory fitting, and per-cluster pathway enrichment. ## Pipeline Overview ``` Expression matrix + time metadata | v [1. Temporal DE] ---------> limma splines / DESeq2 LRT | v [2. Filter] --------------> Significant temp
- Version Compatibility
- Pipeline Overview
- Step 1: Temporal Differential Expression
- R (limma splines)
- R (DESeq2 LRT)
- Python (statsmodels)
- QC Checkpoint: Temporal DE
- Step 2: Filter Significant Genes
- Step 3: Mfuzz Soft Clustering
- Python Alternative (tslearn)
- QC Checkpoint: Clustering
- Step 4a: Rhythm Detection (Optional - Circadian Designs)
- R (MetaCycle)
- Python (CosinorPy)
What does the bio-workflows-timecourse-pipeline skill do?
End-to-end time-course analysis from expression matrix to temporal patterns and enrichment. Covers temporal DE, Mfuzz soft clustering, optional rhythm detection, GAM trajectory fitting, and per-cluster pathway enrichment. Use when analyzing bulk time-series expression experiments from any omics platform.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill timecourse-pipeline --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.