seurat-single-cell-analyzer
// Seurat single-cell analysis skill for clustering, annotation, and trajectory analysis
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updated:March 4, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
nameseurat-single-cell-analyzer
descriptionSeurat single-cell analysis skill for clustering, annotation, and trajectory analysis
allowed-toolsRead,Write,Glob,Grep,Edit,WebFetch,WebSearch,Bash
metadata[object Object]
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)