SPACEc: A Streamlined, Interactive Python Workflow for Multiplexed Image Processing and Analysis
Yuqi Tan, Tim N. Kempchen, Martin Becker, Maximilian Haist, Dorien Feyaerts, Yang Xiao, Graham Su, Andrew J. Rech, Rong Fan, John W. Hickey, Garry P. Nolan
Abstract
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.
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