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seurat

Analyze scRNA-seq data in R with Seurat for QC, clustering, and marker discovery.

Updated Apr 19, 2026
Search Tags:#normalization#r#single-cell#clustering#scRNA-seq#seurat#marker-identification
One-click install
npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill seurat
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seurat
Source: https://github.com/CHENyiru3/AI-Skills-Collections/tree/main/skills-market/compbio/single-cell/analysis/seurat
Command: npx skills add https://github.com/CHENyiru3/AI-Skills-Collections --skill seurat

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Seurat provides an end-to-end toolkit for analyzing single-cell RNA-seq data in R, enabling quality control, normalization, dimensionality reduction, clustering, marker identification, and multi-modal integration.

Core Features & Use Cases

  • QC metrics and filtering, normalization, variable feature selection, and scaling to prepare data for downstream analysis.
  • Dimensionality reduction (PCA, UMAP, t-SNE), clustering, and marker discovery to identify cell types and states.
  • Multi-modal data integration (e.g., CITE-seq) and cross-dataset comparisons for integrative analyses.
  • Real-world use case: analyze a PBMC dataset to identify immune cell populations and annotate cell types based on marker genes.

Quick Start

Install Seurat and load your scRNA-seq data, then create a Seurat object and perform a basic QC-normalization-clustering workflow.

Frequently Asked Questions about seurat

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

FAQPage Schema
How do I perform scRNA-seq clustering and marker identification in R?▼

To perform scRNA-seq clustering and marker identification in R, use Seurat to create an object from raw counts, apply QC and normalization, then execute dimensionality reduction and clustering to discover marker genes for cell types.

What is the standard workflow for single-cell RNA-seq normalization and dimensionality reduction?▼

The standard single-cell RNA-seq normalization and dimensionality reduction workflow involves applying QC metrics, normalizing counts, selecting variable features, scaling, and running PCA, UMAP, or t-SNE to reduce dimensions for clustering.

Can I use Seurat for multi-modal data integration like CITE-seq analysis?▼

Yes, you can use Seurat for multi-modal data integration like CITE-seq analysis. It supports cross-dataset comparisons and multi-modal integration to simultaneously analyze diverse modalities within a single workflow.

Does single-cell RNA-seq analysis in R require specific packages for tissue atlas datasets?▼

Single-cell RNA-seq analysis in R for tissue atlas datasets requires the Seurat package and related utilities for data ingestion, preprocessing, visualization, and integration, ensuring reproducibility through documented parameter settings.

What's the best way to identify immune cell populations in PBMC datasets using single-cell RNA-seq?▼

The best way to identify immune cell populations in PBMC datasets using single-cell RNA-seq is to execute a QC-normalization-clustering workflow and annotate identified cell types based on discovered marker genes.

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