spatialproteomics

Spatialproteomics is an interoperable toolbox for analyzing highly multiplexed fluorescence image data. This analysis involves a sequence of steps, including segmentation, image processing, marker quantification, cell type classification, and neighborhood analysis.

Principles

Multiplexed imaging data comprises at least 3 dimensions (i.e. channels, x, and y) and often contains additional data such as segmentation masks or cell type annotations associated with it. In spatialproteomics, we use xarray to create a data structure that keeps all of these data dimension in sync. This data structure can then be used to apply all sorts of operations to the data. Users can segment cells, perform different image processing steps, quantify protein expression, predict cell types, and plot their data in various ways. By providing researchers with those tools, spatialproteomics can be used to quickly explore highly multiplexed spatial proteomics data directly within jupyter notebooks.

Getting Started

Please refer to the documentation for details on the API and tutorials.

If you work with an AI coding assistant, point it to llms.txt (an index of the documentation in Markdown) or llms-full.txt (the complete documentation in a single file). Contributors using coding agents will find repository instructions in AGENTS.md.

Installation

To install spatialproteomics, first create a python environment and install the package using

pip install spatialproteomics

The installation of the package should take less than a minute.

System Requirements

Hardware Requirements

spatialproteomics requires only a standard computer with enough RAM to support the in-memory operations. Certain steps of the pipeline, such as segmentation, benefit from using a GPU.

Software Requirements

The base version of spatialproteomics depends on the following packages:

xarray
zarr
numpy
pandas
scikit-image
scikit-learn
opencv-python
matplotlib

Citation

Meyer-Bender, M., Voehringer, H., Schniederjohann, C., Koziel, S., Chung, E., Popova, E., ... & Huber, W. (2026). Spatialproteomics: an interoperable toolbox for analyzing highly multiplexed fluorescence image data. Nature Methods, 1-10.