seurat - Analyze scRNA-seq data with Seurat v5
Runs R-based scRNA-seq workflows including quality control, normalization, clustering, visualization, marker analysis, and multi-sample integration.
Tags
Updated: 2026-10-01Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Load count matrices
- Perform quality control
- Normalize expression data
- Select variable features
- Scale expression data
- Run PCA
- Build neighbor graphs
- Cluster cells
- Run UMAP or t-SNE
- Identify marker genes
- Visualize cell populations
- Annotate cell types
- Integrate multiple samples
- Analyze multimodal data
- Save Seurat objects
Inputs
- Filtered 10X matrices
- H5 count files
- Count tables
- Sample metadata
- Cell-type marker genes
- Integration method
Outputs
- Seurat objects
- RDS files
- Quality-control plots
- Dimensionality-reduction plots
- Cell clusters
- Marker-gene tables
- Cell-type annotations
- Integrated embeddings
Requirements
- R 4.0 or later
- Seurat v5
- Linux or macOS preferred
- Sufficient system memory
- Optional integration backends
- Separate conda environment for scVI