Tan, Y.; Kempchen, T. N.; Becker, M.; Haist, M.; Feyaerts, D.; Xiao, Y.; Su, G.; Rech, A. J.; Fan, R.; Hickey, J. W.; Nolan, G. P.

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.