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)
cu124 (CUDA 12.4)
cu126 (CUDA 12.6)

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

Note

Please use nvidia-smi to check CUDA installation status.

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

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¶

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                        version: 1.2.0

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