metabolicpathwayheterogeneity
Analyzes metabolic pathway heterogeneity within cell populations by calculating normalized enrichment scores (NES) for each pathway across different groups. Quantifies metabolic diversity and identifies pathways with variable activity patterns. Uses principal component analysis and GSEA to assess pathway heterogeneity, revealing subpopulation-specific metabolic states and transitions.
npx skills add majiayu000/claude-skill-registry --skill metabolicpathwayheterogeneity --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.
What it does
Analyzes metabolic pathway heterogeneity within cell populations by calculating normalized enrichment scores (NES) for each pathway across different groups. Quantifies metabolic diversity and identifies pathways with variable activity patterns. Uses principal component analysis and GSEA to assess pathway heterogeneity, revealing subpopulation-specific metabolic states and transitions.
How it works
- PCA on pathway activity scores per group to capture variance
- Select top PCs via select_pcs to determine how much variance to include
- Run fgsea on each selected PC to identify pathways correlated with variance
- Compute NES to quantify pathway-PC associations
- Identify pathways with high |NES| as heterogeneous across groups
- Output includes NES per pathway/PC, dot plots, PCA results, and pathway rankings
When to use it
- Assess metabolic diversity: quantify heterogeneity within clusters or conditions
- Identify variable pathways: find pathways with high vs low heterogeneity across groups
- Compare metabolic variability: between treatments, timepoints, or cell types
- After pathway activity analysis: adds heterogeneity dimension to ScrnaMetabolicLandscape workflow
- Typically the last metabolic analysis step
- Subpopulation discovery: when metabolic variability suggests hidden substructure
What it can touch
- Inputs from Seurat objects with normalized expression data
- GMT file with metabolic pathway gene sets (gmtfile)
- Metadata columns such as seurat_clusters and subset_by (e.g., treatment, timepoint)
- Output directory outdir containing heterogeneity scores, dot plots, PCA results, and pathway rankings
Caveats
- Requires Seurat object with matching gene names to GMT sets
- At least 2 groups are required for meaningful heterogeneity analysis
- Parameter choices (select_pcs, pathway_pval_cutoff, fgsea_args) influence results and may affect runtime
- FGSEA parameters: scoreType must be std, pos, or neg; minSize < maxSize; minSize >= 1
- Some configurations imply parallelization across groups rather than within fgsea (nproc, ncores)
# MetabolicPathwayHeterogeneity Process Configuration ## Purpose Analyzes metabolic pathway heterogeneity within cell populations by calculating normalized enrichment scores (NES) for each pathway across different groups. Quantifies metabolic diversity and identifies pathways with variable activity patterns. Uses principal component analysis and GSEA to assess pathway heterogeneity, revealing subpopulation-specific metabolic states and transitions. ## When to Use - **Assess metabolic diversity**: When you need to quantify metabolic heterogeneity within clusters or conditions - **Identify variable pathways**: To find which metabolic pathways show high vs low heterogeneity across groups - **Compare metabolic variability**: To compare heterogeneity between treatments, timepoints, or cell types - **After pathway activity analysis**: Complements MetabolicPathwayActivity by adding heterogeneity dimension - **Final metabolic analysis step**: Typically the last process in the ScrnaMetabolicLandscape workflow - **Subpopulation discovery**: When metabolic variability suggests hidden substructure ## Configuration Structure ### Process Enablement MetabolicPathwayHeterogeneity is part of the Sc
- Purpose
- When to Use
- Configuration Structure
- Process Enablement
- Input Specification
- Environment Variables
- Heterogeneity Analysis Method
- Algorithm Overview
- PC Selection (selectpcs)
- Pathway P-value Cutoff
- FGSEA Score Type
- GMT File Sources
- Configuration Examples
- Minimal Configuration (Default Settings)
What does the metabolicpathwayheterogeneity skill do?
Analyzes metabolic pathway heterogeneity within cell populations by calculating normalized enrichment scores (NES) for each pathway across different groups. Quantifies metabolic diversity and identifies pathways with variable activity patterns. Uses principal component analysis and GSEA to assess pathway heterogeneity, revealing subpopulation-specific metabolic states and transitions.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill metabolicpathwayheterogeneity --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 majiayu000/claude-skill-registry, a repository with 534 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.