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

Erik A. Burlingame, Jennifer Eng, Guillaume Thibault, Koei Chin, Joe W. Gray, Young Hwan Chang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number100053
JournalCell Reports Methods
Volume1
Issue number4
DOIs
StatePublished - Aug 23 2021
Externally publishedYes

Keywords

  • breast cancer
  • GPU data science
  • image analytics
  • multiplex tissue imaging
  • systems biology

ASJC Scopus subject areas

  • Biochemistry
  • Biotechnology
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Genetics
  • Computer Science Applications
  • Radiology Nuclear Medicine and imaging

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