Installation and Setup¶

This tutorial walks you through installing ONTraC (Ordered Niche Trajectory Construction) and confuguring your environment for spatial omics data analysis.

System Requirements¶

ONTraC supports following systems:

Operating Systems: Linux, macOS, and Windows
Python Versions: 3.10, 3.11, and 3.12
Optional: CUDA-capable GPU for faster processing (recommended for large datasets)

        flowchart LR
  subgraph A["System Requirements"]
    B("Operating Systems:

    • Linux
    • macOS
    • Windows")

    C("Python Versions:

    • 3.10
    • 3.11
    • 3.12")

    D("Hardware Recommendations:

    • CUDA-capable GPU (optional but recommended)
    • Sufficient RAM for large datasets")

  end

GPU Configuration¶

ONTraC can utilize GPU acceleration via CUDA for faster processing. If a CUDA-capable GPU is not available, ONTraC will run on the CPU.

The following PyTorch CUDA versions are supported:

cu118 (CUDA 11.8)
cu126 (CUDA 12.6)
cu128 (CUDA 12.8)

Please refer to the official CUDA website for CUDA installation instructions.

Installation¶

ONTraC can be installed using pip. Choose the installation method that best suits your workflow.

Step 0: Clear Cache (Optional but Recommended)¶

pip cache purge
conda clean -a -y

Step 1: Create and Activate a Conda Environment (Optional but Recommended)¶

conda create -y -n ONTraC python=3.11
conda activate ONTraC

Install Optional Dependencies for PyG (Only works on CUDA-capable GPU)¶

pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.7.0+cu126.html
# or
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.7.0+cu128.html
# based on your CUDA version
# or
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.7.0+cpu.html
# for macOS and non-CUDA-capable GPU system

Step 2: Install ONTraC¶

Option 1: Install Stable Version using pip¶

For basic functionality:

pip install ONTraC

For visualization capabilities:

pip install "ONTraC[analysis]"

For test capabilities:

pip install "ONTraC[test]"

For develop capabilities:

pip install "ONTraC[dev]"

For all capabilities:

pip install "ONTraC[all]"

Option 2: Install the Development Version from GitHub¶

For the latest developing version:

git clone git@github.com:gyuanlab/ONTraC.git .
cd ONTraC
pip install .
# Or with visualization capabilities:
pip install ".[analysis]"
# Or with test capabilities:
pip install ".[test]"
# Or with develop capabilities:
pip install ".[dev]"
# Or with all capabilities:
pip install ".[all]"

Step 3: Set Up Jupyter (Optional but Recommended)¶

If you plan to use ONTraC with Jupyter notebooks, add the ONTraC environment as a new kernel:

pip install ipykernel
python -m ipykernel install --user --name ONTraC --display-name "Python 3.11 (ONTraC)"

Step 4: Test¶

CUDA Availability Test (Optional)¶

python -c "import torch; print(torch.cuda.is_available())"

Note

Please refer to the official PyTorch website for PyTorch installation instructions.

ONTraC Installation Test¶

python -c "import ONTraC; ONTraC.utils.write_version_info()"

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                        version: 1.2.0

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