Single cell RNA-seq analysis Snakemake

This repository contains a modular Snakemake pipeline for single-cell RNA sequencing (scRNA-seq) data analysis. It automates quality control, integration, cell type annotation (using CellTypist), and marker gene identification, ensuring reproducibility and scalability for large datasets.

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Pipeline Overview

The workflow performs the following steps:

1. Quality control (QC) per sample

   • Generates cleaned .h5ad files
   • Creates UMAP plots
   • Exports per-cluster marker genes

2. Integration of all cleaned samples into a single dataset using Harmony

3. Cell type annotation using CellTypist

4. Marker gene ranking for integrated clusters

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Directory Structure

Expected project layout:

project/
├─ Snakefile
├─ samples.csv # metadata with sample IDs and paths
├─ scripts/ # analysis scripts called by Snakemake
│ ├─ 02_qc.py
│ ├─ 03_integration.py
│ ├─ 04_celltyping.py
│ └─ 05_markers.py
├─ environments/ # conda environments
│ └─ sc.yml
├─ data/ # optional: raw input files (.h5ad)
├─ 02_output/ # cleaned per-sample data
├─ 03_output/ # integrated dataset
├─ 04_output/ # annotated data + markers
└─ qc/ # QC plots and markers per sample

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Input

The pipeline requires a samples.csv file with at least two columns:

• sample_id → short name for the sample (used in file naming)
• filtered_matrix_path → path to the raw/filtered .h5 file (output from Cell Ranger)

Example:

sample_id,filtered_matrix_path
S01,/path/to/S01_filtered.h5
S02,/data/run_A/S02_filtered.h5

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1. Setup conda environment and install Snakemake

conda install bioconda::snakemake

2. Then go to the directory with Snakefile and in terminal run (adjust cores as available)

snakemake --use-conda --cores 4

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📚 Acknowledgements

• Snakemake for workflow management
• Harmony for integration
• CellTypist for cell type annotation
• Developed as part of a project at the Kuppe Lab of Quantitative Cell Dynamics and Translational Systems Biology, UKAachen