PT - JOURNAL ARTICLE AU - Eng, Jennifer AU - Bucher, Elmar AU - Hu, Zhi AU - Zheng, Ting AU - Gibbs, Summer L. AU - Chin, Koei AU - Gray, Joe W. TI - A framework for multiplex imaging optimization and reproducible analysis AID - 10.1101/2021.11.29.470281 DP - 2021 Jan 01 TA - bioRxiv PG - 2021.11.29.470281 4099 - http://biorxiv.org/content/early/2021/11/30/2021.11.29.470281.short 4100 - http://biorxiv.org/content/early/2021/11/30/2021.11.29.470281.full AB - Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, jinxif, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced.Competing Interest StatementThe authors have declared no competing interest.