metabolicpathwayheterogeneity

Compute NES scores for metabolic pathways across scRNA-seq cell groups.

22|4|Updated May 18, 2021

Search Tags:#analyze #heterogeneity #pca #pathway #metabolic-heterogeneity #scrna-seq #gsea

Ready to run

npx skills add https://github.com/pwwang/immunopipe --skill metabolicpathwayheterogeneity

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: metabolicpathwayheterogeneity
Source: https://github.com/pwwang/immunopipe/tree/main/skills/metabolicpathwayheterogeneity
Command: npx skills add https://github.com/pwwang/immunopipe --skill metabolicpathwayheterogeneity

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzes metabolic pathway heterogeneity within cell populations by computing normalized enrichment scores (NES) for each pathway across groups and identifying subpopulation-specific metabolic states using PCA and GSEA.

Core Features & Use Cases

Compute NES per pathway across cell groups to quantify metabolic variability.
Perform PCA-based heterogeneity analysis and FGSEA on PCs to highlight pathway-PC associations.
Generate plots and rankings to interpret pathway heterogeneity across conditions, timepoints, or treatments.
Integrate with GMT databases (e.g., KEGG, Reactome) via MetabolicInput for flexible analyses.

Quick Start

Run a complete heterogeneity analysis to identify variable metabolic pathways across your cell groups.

Frequently Asked Questions about metabolicpathwayheterogeneity

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I quantify metabolic pathway heterogeneity across scRNA-seq cell groups?▼

Metabolic pathway heterogeneity is quantified across scRNA-seq cell groups by computing normalized enrichment scores (NES) for each pathway, followed by PCA and FGSEA to identify subpopulation-specific metabolic states.

How does PCA and GSEA identify subpopulation-specific metabolic states?▼

PCA and GSEA identify subpopulation-specific metabolic states by applying PCA to pathway NES scores, then running FGSEA on the resulting principal components to highlight significant pathway-PC associations across cell groups.

Can I use KEGG or Reactome GMT databases for scRNA-seq metabolic heterogeneity analysis?▼

You can use KEGG or Reactome GMT databases for scRNA-seq metabolic heterogeneity analysis by integrating them via MetabolicInput to flexibly compute and evaluate pathway enrichment scores across defined cell groups.

How do I analyze metabolic pathway changes across different timepoints or treatments?▼

Metabolic pathway changes across timepoints or treatments are analyzed by computing NES scores across Seurat clustering groups, generating plots and rankings to interpret pathway heterogeneity driven by specific experimental conditions.

What parameters are needed to configure pathway heterogeneity analysis with FGSEA and PCA?▼

Configuring pathway heterogeneity analysis with FGSEA and PCA requires parameters such as select_pcs to define principal components and pathway_pval_cutoff to filter significant pathway-PC associations during the analysis.

What is the best way to reveal treatment-driven metabolic transitions in single-cell data?▼

Treatment-driven metabolic transitions in single-cell data are best revealed by computing NES scores across Seurat clustering groups and applying PCA to identify variable metabolic pathways responding to specific treatments.