seurat

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.