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

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 present the first cross-validation of breast cancer cell phenotypes derived by using two different MTI platforms. Finally, we demonstrate an integrative phenotypic and spatial analysis revealing BC subtype-specific features.

SUBMITTER: Burlingame EA

PROVIDER: S-EPMC8415641 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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

Burlingame Erik A EA Eng Jennifer J Thibault Guillaume G Chin Koei K Gray Joe W JW Chang Young Hwan YH

Cell reports methods 20210723 4

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 present the first cross-validation of breast cancer cell ...[more]

PMID: 34485971

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Dataset Information

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

Publications

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

Similar Datasets

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