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Installation

ST-CNVBench separates the Python controller from the per-method runtime environments.

Choose A Runtime Mode

Runtime Best for
conda Local use and method debugging
docker Reproducible runs on systems with Docker support
apptainer HPC systems without Docker daemon access

Conda Or Mamba

If mamba is available, prefer it for faster dependency solving.

bash conda/install_all_envs.sh

Detailed per-method environment setup is in conda/README.md in the main repository.

Docker

Pull only the images for methods you enable.

docker pull hans0410/cnv-benchmark-copykat:1.1.0

Image names are listed in configs/templates/models.template.yaml. More runtime notes are in External Tools And Runtime Notes.

Apptainer

Create .sif files from the same Docker images and set apptainer_sif in models.yaml.

mkdir -p apptainer_sif
apptainer pull apptainer_sif/copykat.sif docker://hans0410/cnv-benchmark-copykat:1.1.0

Install The Controller

Clone the repository, then create the controller environment from the repository root.

git clone https://github.com/YangLabHKUST/ST-CNVBench.git
cd STCNV-Bench
conda create -n benchmark_env python=3.10 -y
conda activate benchmark_env
pip install -e .
st-cnvbench --help

The package requires Python >=3.10. The initial pip install -e . typically takes about 10-20 min, depending on your machine and network.

Install External Tools

Only a subset of wrappers need external source trees:

  • CalicoST
  • Clonalscope_NoWGS
  • Clonalscope_WGS
  • Numbat
  • Xclone

From the repository root, run:

mkdir -p external_tools
cd external_tools

wget https://storage.googleapis.com/broad-alkesgroup-public/Eagle/downloads/Eagle_v2.4.1.tar.gz
tar -xzf Eagle_v2.4.1.tar.gz
rm Eagle_v2.4.1.tar.gz

git clone https://github.com/raphael-group/CalicoST.git CalicoST
git clone https://github.com/seasoncloud/Clonalscope.git clonalscope
git clone https://github.com/kharchenkolab/numbat.git numbat

If you keep the default public layout under external_tools/, the public config templates already point to the expected locations. Detailed path mapping and model-specific notes are in External Tools And Runtime Notes.

Install Reference Data

Small hg38 annotation files are already bundled in git under refs/hg38_genome_info/.

Large population phasing references are required only for allele-aware wrappers:

  • CalicoST
  • Numbat
  • Xclone

Download the bundle from:

After download, extract it under:

refs/
└── population_phasing/

Detailed file layout and usage notes are in Reference Data.

Demo Bundle

Download the public cSCC demo bundle from:

After extraction, the expected example outputs should appear under demo_runs/cscc_demo/. See Quickstart Demo And Expected Outputs for the expected layout and demo commands.

After Installation