metabolicfeatures

Rank metabolic pathways across scRNA-seq groups using GSEA-based enrichment.

22|4|Updated May 18, 2021
One-click install
npx skills add https://github.com/pwwang/immunopipe --skill metabolicfeatures
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: metabolicfeatures
Source: https://github.com/pwwang/immunopipe/tree/main/skills/metabolicfeatures
Command: npx skills add https://github.com/pwwang/immunopipe --skill metabolicfeatures

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enrichment analysis helps researchers identify which metabolic pathways are active across different cell groups, enabling biological interpretation and hypothesis generation.

Core Features & Use Cases

  • GSEA-based enrichment: ranks pathways across clusters, treatments, or states using the fgsea algorithm for rapid, robust results.
  • Visualization: generates summary plots and GSEA enrichment curves to facilitate interpretation.
  • Group-level analyses: integrates with ScrnaMetabolicLandscape and MetabolicInput to produce cluster- or condition-specific enrichment results.

Quick Start

Run MetabolicFeatures on a Seurat object to generate pathway enrichment results and visualizations for each cluster.

Frequently Asked Questions about metabolicfeatures

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

FAQPage Schema
How do I perform metabolic pathway enrichment on scRNA-seq clusters?

To run metabolic pathway enrichment, provide a Seurat object with normalized data and a GMT file of metabolic gene sets. The analysis uses GSEA-based ranking via the fgsea algorithm to return NES, p-values, FDR, and summary plots for each cluster.

What is GSEA-based ranking for single-cell metabolic features?

GSEA-based ranking identifies which metabolic pathways are active across different scRNA-seq cell groups. It uses the fgsea algorithm to generate NES, p-values, FDR, and visualizations, enabling biological interpretation and hypothesis generation.

Do I need a GMT file to run metabolic pathway analysis on a Seurat object?

Yes, a GMT file of metabolic gene sets is required along with a Seurat object containing normalized data. You must also configure group_by, prerank_method, and fgsea_args to execute the fgsea algorithm and generate enrichment results.

Can I visualize GSEA enrichment curves for different cell states?

Yes, the analysis generates summary plots and GSEA enrichment curves to facilitate interpretation. These visualizations map enriched metabolic pathways across different scRNA-seq clusters, treatments, or cell states.

How do I configure group-level metabolic enrichment for scRNA-seq treatments?

Configure group-level metabolic enrichment by setting the group_by parameter to target scRNA-seq treatments or states. This integrates with ScrnaMetabolicLandscape and MetabolicInput to produce cluster- or condition-specific enrichment results.