cytoviewer

An interactive multi-channel image viewer for R.

This shiny application allows users to interactively visualize multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques. The cytoviewer package is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment / SpatialExperiment and CytoImageList objects for metadata and image visualization and supports image downloads.

Read the BMC Bioinformatics paper here: doi.org/10.1186/s12859-023-05546-z.

Check status

| Bioc branch | Checks | |:------------------------------------------:|:--------------------------:| | Release | | | Devel | |

Requirements

The cytoviewer package requires R version >= 4.0. It builds on data objects and functions contained in the cytomapper package.

Installation

The cytoviewer package can be installed from Bioconductor via:

``` r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")

BiocManager::install("cytoviewer") ```

The development version of cytoviewer can be installed from Github via:

``` r if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")

remotes::install_github("BodenmillerGroup/cytoviewer") ```

To load the package in your R session, type the following:

r library(cytoviewer)

Basic usage

``` r library(cytoviewer)

Load example datasets

library(cytomapper) data("pancreasImages") data("pancreasMasks") data("pancreasSCE")

Use cytoviewer with images, masks and object

app <- cytoviewer(image = pancreasImages, mask = pancreasMasks, object = pancreasSCE, imgid = "ImageNb", cellid = "CellNb")

if (interactive()) {

shiny::runApp(app)

} ```

For more detailed information on package usage and functionality, please refer to https://bodenmillergroup.github.io/cytoviewer/.

Application overview

(A) The supported functionality (right) of cytoviewer depends on the data inputs (left). To match information between the objects, cell (cellid) and image (imgid) identifiers can be provided. SCE/SPE = SingleCellExperiment/SpatialExperiment.

(B) The graphical user interface of cytoviewer is divided into a body, header, and sidebar. The body of cytoviewer includes the image viewer, which has three tabs: Composite (Image-level), Channels (Image-level), and Mask (Cell-level). Zooming is supported for Composite and Mask tabs. The package version, R session information, help page, and a drop-down menu for image downloads are located in the header. The sidebar menu has controls for sample selection, image visualization, mask visualization, and general settings. Scale bar: 150 µm

(C) cytoviewer supports different viewing modes. Top: The "channels" tab of image-level visualization displays individual channels. Shown are Ecad (magenta), CD8a (cyan), and CD68 (yellow) marking tumor cells, CD8+ T cells, and myeloid cells, respectively. Center: The "composite" tab of image-level visualization visualizes image composites combining multiple channels. These composite images can be overlayed with cell outlines, which can be colored by cell-specific metadata. Shown here are cell outlines colored by cell area (continous value) and cell type (categorical value; tumor cells in white). Channel color settings are as follows for all markers: Contrast: 2,5; Brightness: 1; Gamma: 1.2. Bottom: The "mask" tab can be used to visualize segmentation masks that can be colored by cell-specific metadata. Shown here are segmentation masks colored by cell area (continuous) and cell type (categorical; tumor cells in magenta). Scale bars: 150 µm.

(D) "Image appearance" controls can be used to add legends or titles and to change the scale bar length for image-level (top) and cell level (bottom) visualization. The cell-level mask plot depicts tumor (magenta), myeloid (yellow), and CD8+ T cells (cyan). Scale bars: 100 µm.

Adapted from Meyer et al., 2024

Contributing

For feature requests, please open an issue here.

Alternatively, feel free to fork the repository, add your changes and issue a pull request.

Citation

If you are using cytoviewer in your work, please cite the paper as:

Meyer, L., Eling, N., & Bodenmiller, B. (2024). cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data. BMC Bioinformatics, doi.org/10.1186/s12859-023-05546-z

Authors

Lasse Meyer lasse.meyer 'at' dqbm.uzh.ch

Nils Eling nils.eling 'at' dqbm.uzh.ch