a5c-ai Seurat Single-Cell Analyzer Skill
Purpose
Enable Seurat single-cell analysis for clustering, annotation, and trajectory analysis of scRNA-seq data.
Capabilities
- Quality filtering and normalization
- Dimensionality reduction (PCA, UMAP)
- Graph-based clustering
- Marker gene identification
- Cell type annotation
- Integration across datasets
- Trajectory inference
Usage Guidelines
- Apply quality filters appropriate for experiment
- Normalize data before dimensionality reduction
- Select clustering resolution based on biology
- Identify markers for cluster annotation
- Integrate datasets to remove batch effects
- Document analysis parameters
Dependencies
- Seurat
- Scanpy
- CellRanger
Process Integration
- Single-Cell RNA-seq Analysis (scrnaseq-analysis)
- Spatial Transcriptomics Analysis (spatial-transcriptomics)