spatialproteomics 0.6.4

spatialproteomics provides tools for the analysis of highly multiplexed immunofluorescence data

spatialproteomics

spatialproteomics is a light weight wrapper around xarray with the intention to facilitate the processing, exploration and analysis of highly multiplexed immunohistochemistry data.

Principles

Multiplexed imaging data comprises at least 3 dimensions (i.e. channels, x, and y) and has often 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.

Installation

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

pip install spatialproteomics

Documentation

Check the documentation for further information https://sagar87.github.io/spatialproteomics/.

For a more interactive learning experience, you can also check out this workshop on spatialproteomics (based on v0.5.7, some syntax details might have changed since).