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BytesAgain β€Ί Skills β€Ί Single-cell Pipeline
πŸ¦€ ClawHub

Single-cell Pipeline

by @aipoch-ai

Generate single-cell RNA-seq analysis code templates for Seurat and Scanpy, supporting QC, clustering, visualization, and downstream analysis. Trigger when u...

Versionv1.0.0
Downloads675
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TERMINAL
clawhub install single-cell-rnaseq-pipeline

πŸ“– About This Skill

name: single-cell-rnaseq-pipeline description: 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. version: 1.0.0 category: Bioinfo tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

Single-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

    1. Data Loading: 10x Genomics, H5AD, Cell Ranger outputs 2. QC Metrics: Mitochondrial content, gene counts, doublet detection 3. Normalization: Log-normalization, SCTransform 4. Integration: Harmony, RPCA, CCA for batch correction 5. Clustering: Graph-based clustering with optimization 6. Visualization: UMAP, t-SNE, feature plots, dot plots 7. Marker Analysis: Wilcoxon tests, conserved markers 8. Differential Expression: FindAllMarkers, FindConservedMarkers 9. Cell Typing: Reference-based annotation with SingleR/Azimuth

    Scanpy (Python) Templates

    1. Data Loading: AnnData, 10x, CSV, loom files 2. QC Workflow: Comprehensive filtering and metrics 3. Normalization: Log1p, scran, Combat batch correction 4. Integration: scVI, Scanorama, BBKNN 5. Clustering: Leiden/Louvain with resolution sweep 6. Visualization: UMAP, PAGA, embeddings 7. Marker Analysis: rank_genes_groups, filter markers 8. Trajectory: PAGA, diffusion pseudotime (DPT) 9. 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

    | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | --tool | string | Yes | Analysis tool: seurat, scanpy, or both | | --output | string | Yes | Output file or directory path | | --species | string | No | Species: human or mouse (default: human) | | --batch-correction | string | No | Method: harmony, rpca, cca, scanorama, scvi | | --trajectory | bool | No | Include trajectory analysis (default: false) | | --cell-communication | bool | No | Include cell-cell communication (default: false) | | --de-analysis | bool | No | Include differential expression (default: false) | | --spatial | bool | No | Include spatial transcriptomics (default: false) |

    Output Structure

    output/
β”œβ”€β”€ seurat/
β”‚   β”œβ”€β”€ 01_load_and_qc.R
β”‚   β”œβ”€β”€ 02_normalize_integrate.R
β”‚   β”œβ”€β”€ 03_cluster_annotate.R
β”‚   β”œβ”€β”€ 04_visualize.R
β”‚   └── 05_de_analysis.R (if --de-analysis)
β”œβ”€β”€ scanpy/
β”‚   β”œβ”€β”€ 01_load_qc.py
β”‚   β”œβ”€β”€ 02_normalize_integrate.py
β”‚   β”œβ”€β”€ 03_cluster_annotate.py
β”‚   β”œβ”€β”€ 04_visualize.py
β”‚   └── 05_trajectory.py (if --trajectory)
└── README.md

    Technical Details

    Supported Input Formats

  • 10x Genomics Cell Ranger outputs (barcodes.tsv, features.tsv, matrix.mtx)
  • H5AD (AnnData h5 format)
  • Seurat RDS objects
  • CSV/TSV count matrices
  • HDF5 files

    • QC Parameters (Default)

    | Metric | Human | Mouse | |--------|-------|-------| | min_genes | 200 | 200 | | max_genes | 25000 | 25000 | | min_cells | 3 | 3 | | max_mt_percent | 20% | 20% | | doublet_threshold | Auto | Auto |

    Clustering Resolution Guidelines

  • 0.4-0.6: Broad cell types
  • 0.8-1.2: Subtypes
  • 1.5-2.0: Fine populations

    • Batch Correction Recommendations

    | Scenario | Seurat | Scanpy | |----------|--------|--------| | Small batches (<5) | Harmony | Harmony | | Large batches | RPCA | Scanorama | | Complex variation | CCA | scVI |

    Code Examples

    Seurat Quick Start

    # Load data
seurat_obj <- CreateSeuratObject(counts = raw_data, project = "Sample")
    QC
    
seurat_obj[["percent.mt"]] <- PercentageFeatureSet(seurat_obj, pattern = "^MT-")
seurat_obj <- subset(seurat_obj, subset = nFeature_RNA > 200 & percent.mt < 20)
    Normalize
    
seurat_obj <- NormalizeData(seurat_obj)
seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = "vst", nfeatures = 2000)
    Scale and PCA
    
seurat_obj <- ScaleData(seurat_obj)
seurat_obj <- RunPCA(seurat_obj, features = VariableFeatures(object = seurat_obj))
    Cluster
    
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30)
seurat_obj <- FindClusters(seurat_obj, resolution = 1.0)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30)
    Visualize
    
DimPlot(seurat_obj, reduction = "umap", label = TRUE)
FeaturePlot(seurat_obj, features = c("CD3E", "CD14", "CD79A"))

    Scanpy Quick Start

    import scanpy as sc
    Load data
    
adata = sc.read_10x_mtx("filtered_gene_bc_matrices/")
    QC
    
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata.var['mt'] = adata.var_names.str.startswith('MT-')
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, inplace=True)
adata = adata[adata.obs.pct_counts_mt < 20, :]
    Normalize
    
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
    PCA and UMAP
    
sc.pp.scale(adata)
sc.tl.pca(adata, svd_solver='arpack')
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30)
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=1.0)
    Visualize
    
sc.pl.umap(adata, color=['leiden', 'total_counts'])
sc.pl.dotplot(adata, var_names=['CD3E', 'CD14', 'CD79A'], groupby='leiden')

    References

  • references/seurat_template.R - Complete Seurat analysis template
  • references/scanpy_template.py - Complete Scanpy analysis template
  • references/batch_correction_guide.md - Batch correction comparison
  • requirements.txt - Python dependencies

    • Dependencies

    Seurat (R)

    install.packages(c("Seurat", "SeuratObject", "tidyverse", "patchwork"))

    Optional
    
remotes::install_github("satijalab/seurat-wrappers")
remotes::install_github("immunogenomics/harmony")
BiocManager::install("SingleR")

    Scanpy (Python)

    pip install scanpy leidenalg scvi-tools cellchatpy

    Testing

    Run basic validation: 

    cd scripts
python test_main.py

    Error Handling

    All errors return semantic messages:

    {
  "status": "error",
  "error": {
    "type": "invalid_parameter",
    "message": "Unsupported batch correction method: 'xyz'",
    "suggestion": "Use one of: harmony, rpca, cca, scanorama, scvi"
  }
}

    Safety & Compliance

  • No external API calls
  • All code templates are self-contained
  • No hardcoded credentials or paths
  • Templates use relative paths for data
  • Default parameters are conservative for safety

    • Citation

    If using generated templates in publications:

  • Seurat: Satija Lab, Nature Biotechnology 2015
  • Scanpy: Wolf et al., Genome Biology 2018
  • scVI: Lopez et al., Nature Methods 2018
  • Harmony: Korsunsky et al., Nature Methods 2019

    • Risk Assessment

    | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |

    Security Checklist

  • [ ] No hardcoded credentials or API keys
  • [ ] No unauthorized file system access (../)
  • [ ] Output does not expose sensitive information
  • [ ] Prompt injection protections in place
  • [ ] Input file paths validated (no ../ traversal)
  • [ ] Output directory restricted to workspace
  • [ ] Script execution in sandboxed environment
  • [ ] Error messages sanitized (no stack traces exposed)
  • [ ] Dependencies audited

    • Prerequisites

    # Python dependencies
pip install -r requirements.txt

    Evaluation Criteria

    Success Metrics

  • [ ] Successfully executes main functionality
  • [ ] Output meets quality standards
  • [ ] Handles edge cases gracefully
  • [ ] Performance is acceptable

    • Test Cases

    1. Basic Functionality: Standard input β†’ Expected output 2. Edge Case: Invalid input β†’ Graceful error handling 3. Performance: Large dataset β†’ Acceptable processing time

    Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
    • - Performance optimization - Additional feature support

    ⚑ When to Use

    TriggerAction
    - 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

    πŸ’‘ Examples

    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

    | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | --tool | string | Yes | Analysis tool: seurat, scanpy, or both | | --output | string | Yes | Output file or directory path | | --species | string | No | Species: human or mouse (default: human) | | --batch-correction | string | No | Method: harmony, rpca, cca, scanorama, scvi | | --trajectory | bool | No | Include trajectory analysis (default: false) | | --cell-communication | bool | No | Include cell-cell communication (default: false) | | --de-analysis | bool | No | Include differential expression (default: false) | | --spatial | bool | No | Include spatial transcriptomics (default: false) |

    βš™οΈ Configuration

    # Python dependencies
pip install -r requirements.txt