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.

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Repository: GeorgeDoors888/GB-Power-Market-JJ
Last source activity: April 16, 2026 at 00:11
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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 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

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

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

python scripts/main.py --tool both --output scrna_pipeline --species human --batch-correction harmony --trajectory true

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/
├── 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

# 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"))

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')

install.packages(c("Seurat", "SeuratObject", "tidyverse", "patchwork"))
# Optional
remotes::install_github("satijalab/seurat-wrappers")
remotes::install_github("immunogenomics/harmony")
BiocManager::install("SingleR")

pip install scanpy leidenalg scvi-tools cellchatpy

cd scripts
python test_main.py

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

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

# Python dependencies
pip install -r requirements.txt