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RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging
Journal article   Open access   Peer reviewed

RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging

Young Hwan Chang, Koei Chin, Guillaume Thibault, Jennifer Eng, Erik Burlingame and Joe W. Gray
Communications biology, v 3(1), pp 111-111
09 Mar 2020
PMID: 32152447
url
https://doi.org/10.1038/s42003-020-0828-1View
Published, Version of Record (VoR) Open

Abstract

Biology Life Sciences & Biomedicine Life Sciences & Biomedicine - Other Topics Multidisciplinary Sciences Science & Technology Science & Technology - Other Topics
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. Chang et al. develop an analytical method called RESTORE to control for variations due to technical artifacts in multiplexed imaging. They test their method on a CycIF stained tissue microarray dataset and biopsies processed at different times. Their method can improve the applicability of imaging techniques in diagnostics and inference using unbiased clustering methods.

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