bio-temporal-genomics-temporal-clustering
Clusters genes by temporal expression profile shape using Mfuzz soft clustering, TCseq, and DEGreport degPatterns. Groups co-regulated genes into shared trajectory patterns via fuzzy c-means or hierarchical approaches. Use when categorizing temporally dynamic genes into response groups or identifying co-expression modules across time points. Requires temporally variable genes identified first (see differential-expression/timeseries-de).
npx skills add BioTender-max/awesome-bio-agent-skills --skill temporal-clustering --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: numpy 1.26+, scanpy 1.10+, scikit-learn 1.4+ 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 Gene Clustering **"Group my time-course genes by expression pattern shape"** → Cluster temporally variable genes into co-expression modules by trajectory shape using fuzzy c-means (Mfuzz), hierarchical methods, or DTW-based approaches, revealing coordinated response patterns. - R: `Mfuzz::mfuzz()` for soft (fuzzy) temporal clustering - Python: `sklearn.cluster.KMeans` on z-scored time profiles for hard clustering Groups genes with similar temporal expression dynamics into clusters, revealing shared regulatory programs and coordinated response patterns across time-course experiments. ## Core Workflow 1. Select temporally variable genes (pre-filtered by DE or variance) 2. Standardize ex
- Version Compatibility
- Core Workflow
- Mfuzz (R/Bioconductor)
- Setup and Preprocessing
- Fuzzifier Estimation and Clustering
- Visualization
- Cluster Number Selection
- TCseq (R/Bioconductor)
- DEGreport degPatterns (R)
- tslearn (Python)
- Cluster Number Selection with Silhouette
- Method Comparison
- Tips
- Related Skills
What does the bio-temporal-genomics-temporal-clustering skill do?
Clusters genes by temporal expression profile shape using Mfuzz soft clustering, TCseq, and DEGreport degPatterns. Groups co-regulated genes into shared trajectory patterns via fuzzy c-means or hierarchical approaches. Use when categorizing temporally dynamic genes into response groups or identifying co-expression modules across time points. Requires temporally variable genes identified first (see differential-expression/timeseries-de).
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill temporal-clustering --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.