Evaluate cfClone on simulated cfDNA samples

A Snakemake workflow that generates in-silico mixtures and runs cfClone.

Getting started

This pipeline requires that conda and Snakemake be installed; the Bioconda package channel must also be configured.

Dependencies

• conda, version >24.7.1
• Snakemake, version >=9.14.8

Environment

1. Ensure that you have a working conda installation, you can do this by installing Miniforge.
2. Configure the Bioconda channel and set strict channel priority:

   conda config --add channels bioconda
   conda config --add channels conda-forge
   conda config --set channel_priority strict
   

3. Install Snakemake:

   conda create -c conda-forge -c bioconda --name snakemake snakemake'>=9.12'
   

Workflow

1. Create a working directory for the workflow:

   mkdir -p path/to/project-workdir
   cd path/to/project-workdir
   

2. Clone the workflow repository through git:

   git clone --depth 1 https://github.com/Roth-Lab/cfclone-inf-pool-power-calc-smk.git
   

Usage

Configuration

For a full description of all available pipeline options, please refer to the pipeline schema. Modify the configuration file, config.yaml to suit your dataset.

Run Workflow

1. Navigate to the project directory and activate the snakemake environment:

   cd path/to/project-workdir/cfclone-bams-mix-smk
   conda activate snakemake
   

2. Run a dry-run of the pipeline to confirm the ruleset and outputs are as you expect:

   snakemake --cores <number-of-CPU-cores-to-use> --configfile <path/to/config-file> -n 
   

3. Run the pipeline:

   snakemake --cores <number-of-CPU-cores-to-use> --configfile <path/to/config-file>
   

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Output

The main outputs of the pipeline are posterior distributions on tumour fraction and clone prevalences. More on the contents of these output files can be found in the cfClone repository.

Example workflow output folder structure:

<out-dir>
├── cfclone
│   └── coverage_0
│       └── tc_0
│           └── data_seed_0
│               └── out_dir
│                   ├── config.yaml
│                   ├── evidence.tsv
│                   ├── prevalence.tsv
│                   ├── summary.tsv
│                   ├── tumour_content.tsv
│                   └── restart_0
│                       ├── tables
│                       │   ├── ancestral_prevalence.tsv.gz
│                       │   ├── evidence.tsv
│                       │   ├── pairwise_ranks.tsv
│                       │   ├── summary.tsv
│                       │   └── tumour_content.tsv
│                       └── trees
│                           └── prevalence_tree.json
├── config.yaml
├── prevs_summary.tsv
└── summary.tsv