single-cell-rnaseq-pipeline
Generate single-cell RNA-seq analysis code templates for Seurat and Scanpy, supporting QC, clustering, visualization, and downstream analysis. Trigger when users need scRNA-seq analysis pipelines, preprocessing workflows, or batch correction code.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/aipoch-ai/single-cell-rnaseq-pipelineSingle-Cell RNA-seq Pipeline
Overview
Generate comprehensive single-cell RNA-seq analysis code templates for Seurat (R) and Scanpy (Python). This skill provides ready-to-use code frameworks for preprocessing, quality control, normalization, clustering, marker identification, visualization, and advanced analyses like batch correction and trajectory inference.
Technical Difficulty: High
When to Use
- Building scRNA-seq analysis pipelines from raw count matrices
- Need standardized QC and preprocessing workflows
- Performing batch correction across multiple samples/datasets
- Running dimensionality reduction and clustering
- Identifying cell type-specific marker genes
- Creating publication-ready visualizations (UMAP, violin plots, heatmaps)
- Conducting trajectory inference (pseudotime analysis)
- Comparing cell populations between conditions
Core Features
Seurat (R) Templates
- Data Loading: 10x Genomics, H5AD, Cell Ranger outputs
- QC Metrics: Mitochondrial content, gene counts, doublet detection
- Normalization: Log-normalization, SCTransform
- Integration: Harmony, RPCA, CCA for batch correction
- Clustering: Graph-based clustering with optimization
- Visualization: UMAP, t-SNE, feature plots, dot plots
- Marker Analysis: Wilcoxon tests, conserved markers
- Differential Expression: FindAllMarkers, FindConservedMarkers
- Cell Typing: Reference-based annotation with SingleR/Azimuth
Scanpy (Python) Templates
- Data Loading: AnnData, 10x, CSV, loom files
- QC Workflow: Comprehensive filtering and metrics
- Normalization: Log1p, scran, Combat batch correction
- Integration: scVI, Scanorama, BBKNN
- Clustering: Leiden/Louvain with resolution sweep
- Visualization: UMAP, PAGA, embeddings
- Marker Analysis: rank_genes_groups, filter markers
- Trajectory: PAGA, diffusion pseudotime (DPT)
- CellChat/CellPhoneDB: Cell-cell communication
Usage
Generate Seurat Template
python scripts/main.py --tool seurat --output seurat_analysis.R --species human
Generate Scanpy Template
python scripts/main.py --tool scanpy --output scanpy_analysis.py --species mouse
Generate Both Templates
python scripts/main.py --tool both --output scrna_pipeline --species human --batch-correction harmony --trajectory true
Command-Line Parameters
Metadata
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{
"plugins": {
"official-aipoch-ai-single-cell-rnaseq-pipeline": {
"enabled": true,
"auto_update": true
}
}
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