Installation¶

This guide provides different ways to install spMetaTME, whether you are using it as a library in your Python scripts or as a standalone CLI tool.

Prerequisites¶

Before installing spMetaTME, ensure your system meets these requirements:

Python¶

Version: Python 3.9 or higher (3.10+ recommended).
Package Manager: pip or uv (recommended for faster dependency resolution).

Additional Tools (Recommended)¶

R Statistics: Required for spatial domain clustering (specifically used by the BayesSpace package).
NVIDIA GPU: Highly recommended for faster model inference and training (requires CUDA and compatible drivers).

Standard Installation¶

Using `uv` (Recommended)¶

If you are using the modern uv package manager:

uv add spmetatme

Using `pip`¶

To install spMetaTME directly from PyPI:

pip install spmetatme

Installing from Source¶

If you want to contribute to the package or use the latest developmental version, you can install it from GitHub.

Clone the repository:

git clone https://github.com/SurajRepo/spmetatme.git
cd spmetatme

Install in editable mode:
```
pip install -e .
```

Verifying Installation¶

Once installed, you can verify that the CLI is working by checking the version:

spmetatme --version

Or by listing the available metabolic models for inference:

spmetatme infer --list-models

Troubleshooting¶

R Dependency Issues¶

If spatial clustering fails, ensure R is correctly installed and accessible in your system’s PATH. You can verify this by typing R --version in your terminal.

GPU Support¶

spMetaTME uses PyTorch. If you have an NVIDIA GPU but it’s not being utilized, check your PyTorch installation:

import torch
print(torch.cuda.is_available())

If this returns False, you may need to install the version of torch that matches your CUDA version. See pytorch.org for instructions.