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metabolicpathwayactivity

Calculate metabolic pathway activity scores across cell groups in Seurat objects.

22|4|Updated May 18, 2021
Search Tags:#single-cell#heatmap#scrna-seq#pathway-activity#metabolic-analysis#aucell#violin-plot
One-click install
npx skills add https://github.com/pwwang/immunopipe --skill metabolicpathwayactivity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: metabolicpathwayactivity
Source: https://github.com/pwwang/immunopipe/tree/main/skills/metabolicpathwayactivity
Command: npx skills add https://github.com/pwwang/immunopipe --skill metabolicpathwayactivity

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Calculates pathway activity scores for metabolic pathways across different cell groups and subsets. This process quantifies the metabolic activity of each pathway per group, generating visualizations (heatmaps and violin plots) to compare metabolic states between clusters or conditions. Based on the methodology from Xiao et al. (2019) Nature Communications.

Core Features & Use Cases

  • AUCell-like scoring: ranks gene expression per cell and computes pathway activity scores using gene sets.
  • Permutation-based significance: uses ntimes permutations to estimate p-values and normalize scores.
  • Group and subset analysis: supports grouping by metadata (e.g., seurat_clusters) and optional subsetting (subset_by) for focused comparisons.
  • Visualization: outputs heatmaps and violin plots to illustrate pathway activity across groups or conditions; can produce merged heatmaps for multiple subsets.
  • Upstream and downstream integration: designed to work within the ScrnaMetabolicLandscape group alongside MetabolicInput and related processes.

Quick Start

Run MetabolicPathwayActivity after MetabolicInput to compute pathway scores and generate heatmaps and violin plots for your Seurat object.

Frequently Asked Questions about metabolicpathwayactivity

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

FAQPage Schema
How do I calculate metabolic pathway activity scores in single-cell RNA-seq data?▼

To calculate metabolic pathway activity in scrna-seq data, this skill applies AUCell-like scoring to rank gene expression per cell and compute pathway scores using a GMT file. It then uses permutations to estimate p-values and normalize scores across cell groups.

Can I compare pathway activity across different cell conditions using a Seurat object?▼

Yes, you can compare pathway activity across cell conditions using a Seurat object with normalized expression data. The skill supports grouping by metadata variables like seurat_clusters and optional subsetting to compare metabolic states between specific cell groups or treatments.

How does AUCell scoring work for metabolic analysis in single-cell data?▼

AUCell scoring for metabolic analysis works by ranking gene expression per cell and computing pathway activity scores based on provided gene sets. It performs permutation-based significance testing to estimate p-values and normalize the activity scores for accurate metabolic analysis.

Do I need a GMT file to quantify pathway activity in scrna-seq data?▼

Yes, you need a GMT file of pathways to quantify pathway activity in scrna-seq data. Along with the GMT file, the skill requires a Seurat object containing normalized expression data and configuration options to perform the scoring and generate visualizations.

How can I visualize metabolic pathway activity across different cell clusters?▼

You can visualize metabolic pathway activity across cell clusters using the generated heatmaps and violin plots. The skill outputs these visualizations to illustrate pathway activity differences across groups or conditions, and can produce merged heatmaps for multiple subsets.

What are the limitations of using permutation-based scoring for single-cell pathway activity?▼

A limitation of permutation-based scoring for single-cell pathway activity is the computational cost of running multiple permutations to estimate p-values. The skill requires normalized expression data and a GMT file, making it dependent on upstream processing within the ScrnaMetabolicLandscape framework to function correctly.

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