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scrnametaboliclandscape

Coordinate metabolic pathway analysis workflows for scRNA-seq data with Seurat inputs.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Comprehensive, end-to-end metabolic landscape analysis for scRNA-seq data, coordinating imputation, pathway activity scoring, heterogeneity assessment, and feature enrichment in a single workflow to save time and reduce manual setup.

Core Features & Use Cases

  • All-in-one pipeline: runs coordinated steps (expression imputation, pathway activity calculation, heterogeneity analysis, and gene-set enrichment) for complete metabolic profiling.
  • Flexible configuration: supports GMT-based pathway sets, group_by and subset_by metadata, and optional mutaters/arbitrary transformations.
  • Use Case: researchers analyzing metabolic heterogeneity across cell clusters or treatment conditions can compare pathway activity and enrichment results in a streamlined manner.

Quick Start

Run the ScrnaMetabolicLandscape pipeline using a Seurat clustering result to perform the complete metabolic landscape analysis.

Frequently Asked Questions about scrnametaboliclandscape

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

FAQPage Schema
How do I perform comprehensive metabolic pathway analysis on scRNA-seq data?▼

Comprehensive metabolic pathway analysis for scRNA-seq data is performed by coordinating expression imputation, pathway activity scoring, heterogeneity assessment, and gene-set enrichment in a single unified workflow.

Can I analyze metabolic heterogeneity across cell clusters using a Seurat object?▼

Yes, you can analyze metabolic heterogeneity using a Seurat object by configuring group_by metadata to compare pathway activity and enrichment results across different cell clusters or treatment conditions.

What do I need to run scRNA-seq metabolic profiling with fgsea?▼

To run scRNA-seq metabolic profiling, you need a Seurat input object, GMT pathway files, grouping metadata, and optional configurations for imputation tools, prerank methods, and fgsea settings.

Does this metabolic analysis pipeline support custom pathway sets and data subsets?▼

Yes, the metabolic analysis pipeline supports custom GMT-based pathway sets, allows subsetting via subset_by metadata, and applies optional mutaters or arbitrary transformations for flexible configuration.

Why is expression imputation necessary for scRNA-seq pathway activity scoring?▼

Expression imputation is necessary for scRNA-seq pathway activity scoring because it mitigates dropouts in sparse single-cell data, ensuring accurate metabolic pathway activity calculation before heterogeneity analysis.

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