bio-temporal-genomics-temporal-clustering - Cluster time-course genes by trajectory shape
Groups pre-selected temporally variable genes into co-expression modules by trajectory shape using fuzzy, hierarchical, or DTW-based clustering.
Tags
Updated: 2026-10-07Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Validate temporal gene selection
- Standardize gene profiles
- Choose distance metrics
- Select clustering algorithms and k
- Assign soft or hard clusters
- Filter fuzzy memberships
- Validate cluster stability
- Interpret cluster centroids
- Run per-cluster enrichment
Inputs
- Pre-selected temporally variable genes
- Time-course expression matrix
- Timepoint information
- Sample metadata
- Enrichment background
Outputs
- Cluster assignments
- Fuzzy membership scores
- Trajectory centroids
- Cluster visualizations
- Cluster stability results
- Per-cluster enrichment results
Requirements
- Pre-selected temporally variable genes
- Mfuzz 2.64+
- TCseq 1.14+
- DEGreport 1.30+
- R and Bioconductor
- tslearn 0.8+
- scikit-learn 1.4+
- Python