Single-cell 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.

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

bash

python scripts/main.py --tool seurat --output seurat_analysis.R --species human

Generate Scanpy Template

bash

python scripts/main.py --tool scanpy --output scanpy_analysis.py --species mouse

Generate Both Templates

bash

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

text

output/
├── seurat/
│   ├── 01_load_and_qc.R
│   ├── 02_normalize_integrate.R
│   ├── 03_cluster_annotate.R
│   ├── 04_visualize.R
│   └── 05_de_analysis.R (if --de-analysis)
├── scanpy/
│   ├── 01_load_qc.py
│   ├── 02_normalize_integrate.py
│   ├── 03_cluster_annotate.py
│   ├── 04_visualize.py
│   └── 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

r

# 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

python

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)

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)

bash

pip install scanpy leidenalg scvi-tools cellchatpy

Testing

Run basic validation:

bash

cd scripts
python test_main.py

Error Handling

All errors return semantic messages:

json

{
  "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

bash

# 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 → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

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