RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging

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

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Recent advances in multiplexed imaging technologies promise to improve the understanding of the functional states of individual cells and the interactions between the cells in tissues. This often requires compilation of results from multiple samples. However, quantitative integration of information between samples is complicated by variations in staining intensity and background fluorescence that obscure biological variations. Failure to remove these unwanted artifacts will complicate downstream analysis and diminish the value of multiplexed imaging for clinical applications. Here, to compensate for unwanted variations, we automatically identify negative control cells for each marker within the same tissue and use their expression levels to infer background signal level. The intensity profile is normalized by the inferred level of the negative control cells to remove between-sample variation. Using a tissue microarray data and a pair of longitudinal biopsy samples, we demonstrated that the proposed approach can remove unwanted variations effectively and shows robust performance.

Original languageEnglish (US)
Article number111
JournalCommunications Biology
Issue number1
StatePublished - Dec 1 2020

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)


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