scRNA sequencing analysis workflow

Introduction

Cellsnake can be run directly using the snakemake workflow. We recommend the wrapper but the snakemake workflow give more control in some use cases.

The main cellsnake repo is here : https://github.com/sinanugur/cellsnake

Installation

You may pull the workflow from the GitHub repo and create a clean environment. Mamba installation is highly recommended.

conda install mamba -c conda-forge # to install Mamba

git clone https://github.com/sinanugur/scrna-workflow.git
cd scrna-workflow
mamba env create --name scrna-workflow --file environment.yml
conda activate scrna-workflow

For Apple Silicon (i.e. M1, M2 etc.) architecture, you have to put CONDA_SUBDIR=osx-64 before creating the environment.

CONDA_SUBDIR=osx-64 mamba env create --name scrna-workflow --file environment.yml
conda activate scrna-workflow

After the environment created and activated successfully, to install all the required R packages, you should run the installation script, this may take some time:

bash install_r_packages.sh

Quick Start Example

You can start a minimal run by calling, sample runs are expected in data folder.

snakemake -j 10 --config datafolder=data option=minimal

Then we can run integration.

snakemake -j 10 --config option=integration

Now it is time to work on the integrated sample. We can run standard workflow on the integrated object which is always generates at the same location.

snakemake -j 10 --config  datafolder=analyses_integrated/seurat/integrated.rds option=standard is_integrated_sample=True --rerun-incomplete

You may change some of the options or you may provide a config file as well, for example.

snakemake -j 10 --config  datafolder=analyses_integrated/seurat/integrated.rds option=standard is_integrated_sample=True --configfile=config.yaml --rerun-incomplete