UELer: a Jupyter-based framework for interactive exploration of multiplexed imaging datasets

UELer is an innovative Jupyter-based framework designed for exploratory analysis of complex multiplexed imaging datasets. These datasets, often generated by spatial proteomics, are notoriously difficult to manage due to their sheer size and complexity. Traditional approaches to handling such data often involve separating computational analysis from visual inspection, a process that can be cumbersome and inefficient.

The UELer framework aims to bridge this gap by integrating multi-channel image views with quantitative analysis results directly within Jupyter notebooks. This integration eliminates the need for additional infrastructure, as the entire process can be conducted within the notebook session. Users can select cells through computational analysis and view summary plots, with the ability to inspect their tissue context directly.

Similarly, selections made in the image can be made available for further analysis in any downstream process.

The UELer framework is built on the Python package ipywidgets and operates within Jupyter environments that support ipywidgets 8.1 or later. It has been tested in JupyterLab and Visual Studio Code across multiple operating systems including Linux, macOS, and Windows. The framework is freely available under the GPL-3.0 license and can be easily installed via pip.

The source code and comprehensive documentation are available on the Hartmann Lab's GitHub repository (https://github.com/HartmannLab/UELer) and website (https://hartmannlab.github.io/UELer/). For those who prefer not to install the framework, an online version is also available via BinderHub (https://mybinder.org/v2/gh/HartmannLab/UELer/main), accessible via the script/run_ueler_binder.ipynb notebook.