cytomapper/cytoviewer: R/Bioconductor packages for visualization and exploration of highly multiplexed imaging data
Author(s): Lasse Meyer,Nils Eling
Affiliation(s): Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
Highly multiplexed imaging allows simultaneous spatially and single-cell resolved detection of dozens of biological molecules (e.g. proteins) in their native tissue context. As a result, these technologies allow an in-depth analysis of complex systems and diseases such as cancer. Here, we showcase two related R/Bioconductor packages, cytomapper and cytoviewer, that contain user-friendly functions to visualize and explore the multiplexed read-outs and cell-level information obtained by highly multiplexed imaging data. Firstly, the cytomapper package allows visualization of multi-channel pixel-level information as well as display of single cell-level information on segmentation masks. In addition, it includes a Shiny application to enable hierarchical gating and visualization of selected cells. Secondly, the cytoviewer package builds on top of the cytomapper package and extends the static visualization strategies via an interactive Shiny application. The cytoviewer interface is divided into image-level and cell-level visualization. Users can overlay individual images with segmentation masks, visualize cell-specific metadata and download images. Both packages integrate well into the Bioconductor framework for single-cell and image analysis leveraging the image handling and analysis strategies from the EBImage Bioconductor package and building on commonly used Bioconductor classes such as SingleCellExperiment, SpatialExperiment and CytoImageList. Taken together, the Bioconductor packages cytomapper and cytoviewer provide a versatile and well-integrated toolbox for highly multiplexed imaging data visualization in R.