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bioRxiv · 10.1101/2020.12.11.422048

Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms

Burlingame, E. A.·Eng, J.·Thibault, G.·Chin, K.·Gray, J. W.·Chang, Y. H.
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Abstract

The emergence of megascale single-cell multiplex tissue imaging (MTI) datasets necessitates reproducible, scalable, and robust tools for cell phenotyping and spatial analysis. We developed open-source, graphics processing unit (GPU)-accelerated tools for intensity normalization, phenotyping, and microenvironment characterization. We deploy the toolkit on a human breast cancer (BC) tissue microarray stained by cyclic immunofluorescence and benchmark our cell phenotypes against a published MTI dataset. Finally, we demonstrate an integrative analysis revealing BC subtype-specific features.

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Burlingame, E. A., Eng, J., Thibault, G., Chin, K., Gray, J. W., Chang, Y. H.. 2020-12-11. Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms. https://doi.org/10.1101/2020.12.11.422048

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