y1zhou / GSE157220

Learning about scRNA-Seq analysis using Snakemake.

Snakemake workflow: single cell RNA-seq

Processing scRNA-Seq data from the following paper:

Kinker, G.S., Greenwald, A.C., Tal, R. et al. Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. Nat Genet 52, 1208–1218 (2020).

The layout of this workflow follows this template by Johannes Köster. Given a SRA project ID, the pipeline downloads BAM files, converts them to FASTQ files using bamtofastq, and uses Cell Ranger to align reads and generate expression matrices.

Usage

Step 1: Obtain a copy of this workflow

git clone https://github.com/y1zhou/GSE157220

Step 2: Configure workflow

Configure the workflow according to your needs via editing the file config.yaml. Afterwards, install all required packages and generate the design matrix:

# Prepare environment
conda env create -f=environment.yaml
conda activate scrna-cellranger

# Generate sample matrix
snakemake --cores 1 generate_srr_id

Step 3: Execute workflow

Test your configuration by performing a dry-run via

snakemake -np --reason

Execute the workflow locally via

snakemake --cores <num-cores> --reason

See the Snakemake documentation for further details.