Abstract
Multiplex spatial proteomic methodologies can provide a unique perspective on the molecular and cellular composition of complex biological systems. Several challenges are associated to the analysis of imaging data, specifically in regard to the normalization of signal-to-noise ratios across images and subtracting background noise. However, there is a lack of user-friendly solutions for denoising multiplex imaging data that can be applied to large datasets. We have developed PENGUIN –Percentile Normalization GUI Image deNoising: a straightforward image preprocessing tool for multiplexed spatial proteomics data. Compared to existing approaches, PENGUIN distinguishes itself by eliminating the need for manual annotation or machine learning models. It effectively preserves signal intensity differences while reducing noise, improving downstream tasks such as cell segmentation and phenotyping. PENGUIN's simplicity, speed, and intuitive interface, available as both a script and a Jupyter notebook, make it easy to adjust image processing parameters, providing a user-friendly experience. We further demonstrate the effectiveness of PENGUIN by comparing it to conventional image processing techniques and solutions tailored for multiplex imaging data.