Getting started#
This repository provides two options for running the Snakemake pipeline: containers hosted on Docker Hub or Conda environments.
Option 1: Docker Images (Recommended)#
Environment Setup#
Install the base environment that includes Snakemake and Apptainer:
conda env create -f envs/cansig.yml
Activate the base environment:
conda activate cansig
Running the Snakemake Pipeline#
Execute the pipeline using Apptainer containers:
snakemake --configfile <path_to_your_config> --sdm apptainer -c <number_of_cores>
Reproducing Paper Results#
To reproduce the results from the paper using Docker containers:
snakemake --configfile config/config_gbm.yml --sdm apptainer -c <number_of_cores>
snakemake --configfile config/config_breast.yml --sdm apptainer -c <number_of_cores>
snakemake --configfile config/config_scc.yml --sdm apptainer -c <number_of_cores>
snakemake --configfile config/config_luad.yml --sdm apptainer -c <number_of_cores>
Option 2: Conda Environments#
Environment Setup#
Use the pre-configured environments from envs/without_build/:
conda env create -f envs/without_build/CanSig-R.yml
conda env create -f envs/without_build/CanSig-python.yml
conda env create -f envs/without_build/cansig-benchmark.yml
If you have trouble installing the enviorments with fixed builds, we also provide just the versions at envs/with_build/.
The entire benchmark runs within the cansig-benchmark environment, while Snakemake automatically activates the other environments as needed:
conda activate cansig-benchmark
Running the Snakemake Pipeline#
Execute the pipeline with Conda environments:
snakemake --configfile <path_to_your_config> --use-conda -c <number_of_cores>
Reproducing Paper Results#
To reproduce the results from the paper, run the following commands:
snakemake --configfile config/config_gbm.yml -c <number_of_cores>
snakemake --configfile config/config_breast.yml -c <number_of_cores>
snakemake --configfile config/config_scc.yml -c <number_of_cores>
snakemake --configfile config/config_luad.yml -c <number_of_cores>
Replace <number_of_cores> with the desired number of CPU cores for parallel processing.