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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
Data Loading : 10x Genomics, H5AD, Cell Ranger outputsQC Metrics : Mitochondrial content, gene counts, doublet detectionNormalization : Log-normalization, SCTransformIntegration : Harmony, RPCA, CCA for batch correctionClustering : Graph-based clustering with optimizationVisualization : UMAP, t-SNE, feature plots, dot plotsMarker Analysis : Wilcoxon tests, conserved markersDifferential Expression : FindAllMarkers, FindConservedMarkersCell Typing : Reference-based annotation with SingleR/Azimuth
Scanpy (Python) Templates
Data Loading : AnnData, 10x, CSV, loom filesQC Workflow : Comprehensive filtering and metricsNormalization : Log1p, scran, Combat batch correctionIntegration : scVI, Scanorama, BBKNNClustering : Leiden/Louvain with resolution sweepVisualization : UMAP, PAGA, embeddingsMarker Analysis : rank_genes_groups, filter markersTrajectory : 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 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/
├── 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 types0.8-1.2 : Subtypes1.5-2.0 : 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
seurat_obj <- CreateSeuratObject( counts = raw_data, project = "Sample" )
seurat_obj[[ "percent.mt" ] ] <- PercentageFeatureSet( seurat_obj, pattern = "^MT-" )
seurat_obj <- subset( seurat_obj, subset = nFeature_RNA > 200 & percent.mt < 20 )
seurat_obj <- NormalizeData( seurat_obj)
seurat_obj <- FindVariableFeatures( seurat_obj, selection.method = "vst" , nfeatures = 2000 )
seurat_obj <- ScaleData( seurat_obj)
seurat_obj <- RunPCA( seurat_obj, features = VariableFeatures( object = seurat_obj) )
seurat_obj <- FindNeighbors( seurat_obj, dims = 1 : 30 )
seurat_obj <- FindClusters( seurat_obj, resolution = 1.0 )
seurat_obj <- RunUMAP( seurat_obj, dims = 1 : 30 )
DimPlot( seurat_obj, reduction = "umap" , label = TRUE )
FeaturePlot( seurat_obj, features = c ( "CD3E" , "CD14" , "CD79A" ) )
Scanpy Quick Start import scanpy as sc
adata = sc.read_10x_mtx("filtered_gene_bc_matrices/" )
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 , :]
sc.pp.normalize_total(adata, target_sum=1e4 )
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000 )
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 )
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 templatereferences/scanpy_template.py - Complete Scanpy analysis templatereferences/batch_correction_guide.md - Batch correction comparisonrequirements.txt - Python dependencies
Dependencies
Seurat (R) install.packages( c ( "Seurat" , "SeuratObject" , "tidyverse" , "patchwork" ) )
remotes:: install_github( "satijalab/seurat-wrappers" )
remotes:: install_github( "immunogenomics/harmony" )
BiocManager:: install( "SingleR" )
Scanpy (Python) pip install scanpy leidenalg scvi-tools cellchatpy
Testing 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
Prerequisites
pip install -r requirements.txt
Evaluation Criteria
Success Metrics
Test Cases
Basic Functionality : Standard input → Expected outputEdge Case : Invalid input → Graceful error handlingPerformance : Large dataset → Acceptable processing time
Lifecycle Status
Current Stage : DraftNext Review Date : 2026-03-06Known Issues : NonePlanned Improvements :
Performance optimization
Additional feature support
