Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis

Multiplexed imaging technologies provide insights into complex tissue
architectures. However, challenges arise due to software fragmentation
with cumbersome data handoffs, inefficiencies in processing large images
(8 to 40 gigabytes per image), and limited spatial analysis capabilities.
To efficiently analyze multiplexed imaging data, we developed SPACEc, a
scalable end-to-end Python solution, that handles image extraction, cell
segmentation, and data preprocessing and incorporates
machine-learning-enabled, multi-scaled, spatial analysis, operated through
a user-friendly and interactive interface. The demonstration dataset was
derived from a previous analysis and contains TMA cores from a human
tonsil and tonsillitis sample that were acquired with the Akoya
PhenocyclerFusion platform. The dataset can be used to test the workflow
and establish it on a user’s system or to familiarize oneself with the
pipeline.