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Cytomapper: an R/bioconductor package for visualisation of highly multiplexed imaging data


Eling, Nils; Damond, Nicolas; Hoch, Tobias; Bodenmiller, Bernd (2020). Cytomapper: an R/bioconductor package for visualisation of highly multiplexed imaging data. bioRxiv 287516, Cold Spring Harbor Laboratory.

Abstract

Highly multiplexed imaging technologies enable spatial profiling of dozens of biomarkersin situ. Standard data processing pipelines quantify cell-specific features and generate object segmentation masks as well as multi-channel images. Therefore, multiplexed imaging data can be visualised across two layers of information: pixel-intensities represent the spatial expression of biomarkers across an image while segmented objects visualise cellular morphology, interactions and cell phenotypes in their microenvironment.Here we describecytomapper, a computational tool that enables visualisation of pixel- and cell-level information obtained by multiplexed imaging. The package is written in the statistical programming language R, integrates with the image and single-cell analysis infrastructure of the Bioconductor project, and allows visualisation of single to hundreds of images in parallel. Usingcytomapper, expression of multiple markers is displayed as composite images, segmentation masks are coloured based on cellular features, and selected cells can be outlined in images based on their cell type, among other functions. We illustrate the utility ofcytomapperby analysing 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients and healthy individuals. In addition,cytomapperincludes a Shiny application that allows hierarchical gating of cells based on marker expression and visualisation of selected cells in corresponding images. Together,cytomapperoffers tools for diverse image and single-cell visualisation approaches and supports robust cell phenotyping via gating.

Abstract

Highly multiplexed imaging technologies enable spatial profiling of dozens of biomarkersin situ. Standard data processing pipelines quantify cell-specific features and generate object segmentation masks as well as multi-channel images. Therefore, multiplexed imaging data can be visualised across two layers of information: pixel-intensities represent the spatial expression of biomarkers across an image while segmented objects visualise cellular morphology, interactions and cell phenotypes in their microenvironment.Here we describecytomapper, a computational tool that enables visualisation of pixel- and cell-level information obtained by multiplexed imaging. The package is written in the statistical programming language R, integrates with the image and single-cell analysis infrastructure of the Bioconductor project, and allows visualisation of single to hundreds of images in parallel. Usingcytomapper, expression of multiple markers is displayed as composite images, segmentation masks are coloured based on cellular features, and selected cells can be outlined in images based on their cell type, among other functions. We illustrate the utility ofcytomapperby analysing 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients and healthy individuals. In addition,cytomapperincludes a Shiny application that allows hierarchical gating of cells based on marker expression and visualisation of selected cells in corresponding images. Together,cytomapperoffers tools for diverse image and single-cell visualisation approaches and supports robust cell phenotyping via gating.

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Additional indexing

Item Type:Working Paper
Communities & Collections:07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Life Sciences > Biochemistry
Life Sciences > Molecular Biology
Physical Sciences > Computer Science Applications
Physical Sciences > Computational Theory and Mathematics
Physical Sciences > Computational Mathematics
Language:English
Date:9 September 2020
Deposited On:07 Jul 2022 13:13
Last Modified:24 Sep 2023 07:12
Series Name:bioRxiv
ISSN:2164-7844
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1101/2020.09.08.287516
Related URLs:https://www.zora.uzh.ch/id/eprint/195753/
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)