Seurat Single-Cell Analyzer Skill
SkillDev toolsSeurat single-cell analysis skill for clustering, annotation, and trajectory analysis
Use Seurat Single-Cell Analyzer Skill in Claude, ChatGPT or Ahel Desktop
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Then ask your AI: use the Seurat Single-Cell Analyzer Skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/bioinformatics/skills/seurat-single-cell-analyzer/SKILL.md and read by Ahel’s review.
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)
Signals
- GitHub stars
- 2k
- Forks
- 113
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
seurat-single-cell-analyzer- Source
- github.com/a5c-ai/babysitter