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seurat-single-cell-analysis

Run Seurat v5 single-cell RNA-seq analysis in R from .rds/.rdata inputs.

1|Updated Dec 3, 2025
Search Tags:#visualization#r#single-cell#h5ad#seurat#rna-seq#go enrichment
One-click install
npx skills add https://github.com/Ketomihine/my_skills --skill seurat-single-cell-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seurat-single-cell-analysis
Source: https://github.com/Ketomihine/my_skills/tree/main/seurat-single-cell
Command: npx skills add https://github.com/Ketomihine/my_skills --skill seurat-single-cell-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables researchers to perform comprehensive Seurat v5–based single-cell RNA-seq analysis in R, covering data loading, quality control, normalization, variable feature selection, dimensionality reduction, clustering, marker identification, and GO enrichment interpretation, all within a unified workflow.

Core Features & Use Cases

  • End-to-end workflow: Load .rds/.rdata objects, normalize data, identify highly variable features, run PCA/UMAP, construct neighbor graphs, cluster cells, and extract marker genes.
  • GO enrichment visualizations: Generate publication-ready GO plots for Biological Process, Cellular Component, and Molecular Function, with separate or combined formats.
  • Cross-platform workflows: Convert Seurat objects to H5AD to enable cross-platform analyses with Scanpy while preserving metadata and reductions.
  • Example: Analyze a PBMC dataset to discover cluster-specific markers and interpret enriched GO terms.

Quick Start

Load your .rds/.rdata Seurat object and run a basic Seurat workflow, then generate GO enrichment visualizations.

Frequently Asked Questions about seurat-single-cell-analysis

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

FAQPage Schema
How do I run end-to-end single-cell RNA-seq analysis in R using Seurat v5?▼

To run single-cell RNA-seq analysis in R, load your .rds or .rdata object to perform QC, normalization, HVG selection, dimensionality reduction, clustering, and marker gene identification within a unified Seurat v5 workflow.

Can I convert Seurat objects to h5ad for Scanpy cross-platform workflows?▼

Yes, you can convert Seurat objects to h5ad format to enable cross-platform analyses with Scanpy, preserving your metadata and dimensional reductions during the conversion process.

How do I generate publication-ready GO enrichment plots for single-cell data?▼

You generate publication-ready GO enrichment visualizations by plotting Biological Process, Cellular Component, and Molecular Function terms in separate or combined formats with fixed plot formatting for your single-cell data.

What input data formats are supported for Seurat single-cell analysis?▼

Seurat single-cell analysis supports .rds and .rdata input file formats, allowing you to load existing Seurat objects directly to begin the QC and clustering workflow.

Does this single-cell RNA-seq workflow include quality control and UMAP visualization?▼

Yes, the single-cell RNA-seq workflow includes quality control, PCA and UMAP dimensionality reduction, neighbor graph construction, and cell clustering to visualize and interpret your dataset.

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