Journal article
A framework for multiplex imaging optimization and reproducible analysis
Communications biology, v 5(1), pp 438-438
11 May 2022
PMID: 35545666
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, mplexable, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced.
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Details
- Title
- A framework for multiplex imaging optimization and reproducible analysis
- Creators
- Jennifer Eng - Oregon Health & Science UniversityElmar Bucher - Oregon Health & Science UniversityZhi Hu - Oregon Health & Science UniversityTing Zheng - Oregon Health & Science UniversitySummer L Gibbs - Oregon Health & Science UniversityKoei Chin - Oregon Health & Science UniversityJoe W Gray - Oregon Health & Science University
- Publication Details
- Communications biology, v 5(1), pp 438-438
- Publisher
- Springer Nature
- Grant note
- P30 CA069533 / NCI NIH HHS R44 CA224994 / NCI NIH HHS U2C CA233280 / NCI NIH HHS U54 CA209988 / NCI NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Pediatrics
- Web of Science ID
- WOS:000793858900004
- Scopus ID
- 2-s2.0-85129984640
- Other Identifier
- 991021838694804721
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- Biology