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

seurat

scanpy

both

human

mouse

harmony

rpca

cca

scanorama

scvi

Output Structure

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)

Clustering Resolution Guidelines

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

Batch Correction Recommendations

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

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 → 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