Snakemake workflow: single-cell-cellphonedb

Snakemake workflow for running cellphonedb on a combined annotated dataset (AnnData object) saved as h5ad file. Workflow structure is modelled after cookiecutter snakemake template.

Authors

• Rachel Ng (@racng)

Usage

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) repository.

Step 1: Obtain a copy of this workflow

1. Create a new github repository using this workflow as a template.
2. Clone the newly created repository to your local system, into the place where you want to perform the data analysis.

Step 2: Configure workflow

Configure the workflow according to your needs via editing config.yaml.

• outdir: Output directory (default: output)
• adata: Path to h5ad file
• gene: Key in adata.var that contains gene ids. Put index if using adata.var_names
• celltype:key: Key in adata.obs that contains cell type labels
• celltype:groups: Cell types to use (Put [] if using all)
• sample:key: Key in adata.obs that contains sample labels
• sample:groups: Samples to analyze (Put [] if using all)
• cellphonedb:args:: Additional arguments for the cellphonedb cli (counts-data, iterations, threshold, threads)

Step 3: Install Snakemake

Install Snakemake using conda and mamba:

conda install -c conda-forge mamba
mamba create -c conda-forge -c bioconda -n snakemake snakemake

For installation details, see the instructions in the Snakemake documentation.

Step 4: Execute workflow

Activate the conda environment:

conda activate snakemake

Test your configuration by performing a dry-run via

snakemake --use-conda -n

Execute the workflow locally using $N cores (or 'all' cores)

snakemake --use-conda --cores $N

Use alternate config files using the --configfile option.

See the Snakemake documentation for further details.

Step 5: Investigate results

Cellphonedb outputs are organized by sample subfolders under the specified outdir.