spatiomic 0.4.2

A python toolbox for spatial omics analysis.

spatiomic

spatiomic is a computational library for the analysis of spatial proteomics (with some functions also being useful for other -omics).

The main goal of this package is to organize different packages and methods that are commonly used when dealing with high-dimensional imaging data behind a single API that allows for scalable high-performance computing applications, whenever possible on the GPU.

Installation

spatiomic is available through PyPi:

pip install spatiomic

For the best GPU-accelerated experience (optional), a CUDA-compatible GPU and installation of the cupy, cuml, cuGraph and cuCIM packages is necessary. Please consult the RAPIDS.AI installation guide for further information.

Installation time should not exceed 5 minutes on a standard desktop computer with an average network connection.

Documentation

Detailled documentation is made available at: https://spatiomic.org.

The documentation also contains a small simulated dataset used for clustering, for more information, please refer to the Pixel-based clustering section of the documentation.

Building the documentation

The documentation can be build locally by navigating to the docs folder and running: make html. This requires that the development requirements of the package as well as the package itself have been installed in the same virtual environment and that pandoc has been added, e.g. by running brew install pandoc on macOS operating systems.

System requirements

Hardware requirements

spatiomic does not come with any specific hardware requirements. For an optimal experience and analysis of very large datasets, a CUDA-enabled GPU and sufficient RAM (e.g., >= 48 Gb) is recommended.

Software requirements

Operating systems

Though it should run on all systems that can run Python, spatiomic has specifically been confirmed to work on the following operating systems:

• Ubuntu 22.04
• Ubuntu 24.04
• macOS Sequoia 15.1.1

Python version & dependencies

spatiomic requires Python version 3.10 or above (3.12 recommended).

Code editors

We recommend developers use Visual Studio Code with the recommended extensions and settings contained in the .vscode folder to edit this codebase.

GPUs

The use of a GPU is optional but greatly accelerates many common spatiomic analyses. While most recent CUDA-compatible devices are expected to work, the following GPUs have been tested:

• NVIDIA RTX 6000 Ada
• NVIDIA QUADRO RTX 8000
• NVIDIA V100

Using a modern computer (e.g., an M-series MacBook) without a CUDA-enabled GPU, the sample script provided in the Full example section of the documentation should take a few minutes, depending on your hardware, typically less than 3 minutes if all the data is already downloaded and the package is installed. With a CUDA-enabled GPU, it should be significantly faster.

Attribution & License

License

The software is provided under the GNU General Public License, version 3 (GPL-3.0). Please consult LICENSE.md for further information. The glasbey_light color palette available through so.plot.colormap is part of colorcet and distributed under the Creative Commons Attribution 4.0 International Public License (CC-BY).

Citation

spatiomic was developed for use with multiplexed immunofluorescence imaging data at Aarhus University by Malte Kuehl with valuable inputs, code additions and feedback from other lab members, supervisors and collaborators. If you use this package in an academic setting, please cite this repository according to the information in the CITATION.cff file.