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bioRxiv · 10.1101/2021.11.29.470281

A framework for multiplex imaging optimization and reproducible analysis

Eng, J. R.·Bucher, E.·Hu, Z.·Zheng, T.·Gibbs, S.·Chin, K.·Gray, J. W.
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Abstract

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, mplexable, 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.

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Eng, J. R., Bucher, E., Hu, Z., Zheng, T., Gibbs, S., Chin, K., Gray, J. W.. 2021-11-30. A framework for multiplex imaging optimization and reproducible analysis. https://doi.org/10.1101/2021.11.29.470281

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