cytoviewer - Shiny application to interactively browse multi-channel images

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

cytoviewer(
  image = NULL,
  mask = NULL,
  object = NULL,
  cell_id = NULL,
  img_id = NULL,
  coords = NULL
)

Arguments

image: (optional) a CytoImageList object containing single or multi-channel Image objects.
mask: (optional) a CytoImageList containing single-channel Image objects.
object: (optional) a SingleCellExperiment or SpatialExperiment object.
cell_id: character specifying the colData(object) entry, in which the integer cell IDs are stored. These IDs should match the integer pixel values in the segmentation mask object (mask).
img_id: character specifying the colData(object) and mcols(mask) and/or mcols(image) entry, in which the image IDs are stored.
coords: character vector of length 2 specifying the names of the colData (for a SingleCellExperiment object) or the spatialCoords entries indicating the cells' x and y locations.

Value

A Shiny app object for interactive multi-channel image visualization and exploration

The input objects

The functionality of cytoviewer depends on which input objects are user-provided. Below we describe the four use cases in respect to input objects and functionality.

1. Usage of cytoviewer with images, masks and object

The full functionality of cytoviewer can be leveraged when image, mask and object are provided. This allows image-level visualization (Composite and Channels), cell-level visualization, point-level visualization, overlaying images with segmentation masks as well as metadata visualization.

2. Usage of cytoviewer with images only

If only image is specified, image-level visualization (Composite and Channels) is possible.

3. Usage of cytoviewer with images and masks

Image-level visualization (Composite and Channels), overlaying of images with masks and cell-level visualization is feasible when image and mask are provided.

4. Usage of cytoviewer with masks and object

If mask and object are specified, cell-level visualization, point-level visualization as well as metadata visualization is possible.

Author

Lasse Meyer (lasse.meyer@dqbm.uzh.ch)

Examples

# Load example datasets from cytomapper
library(cytomapper, quietly = TRUE)
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> 
#> Attaching package: ‘generics’
#> The following objects are masked from ‘package:base’:
#> 
#>     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
#>     setequal, union
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
#>     mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#>     rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
#>     unsplit, which.max, which.min
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
#> 
#> Attaching package: ‘IRanges’
#> The following objects are masked from ‘package:EBImage’:
#> 
#>     resize, tile
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#> 
#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     anyMissing, rowMedians
#> The following object is masked from ‘package:EBImage’:
#> 
#>     channel
#> 
#> Attaching package: ‘cytomapper’
#> The following objects are masked from ‘package:Biobase’:
#> 
#>     channelNames, channelNames<-
data("pancreasImages")
data("pancreasMasks")
data("pancreasSCE")

# 1. Use cytoviewer with images, masks and object
app <- cytoviewer(image = pancreasImages, 
                  mask = pancreasMasks, 
                  object = pancreasSCE, 
                  img_id = "ImageNb", 
                  cell_id = "CellNb",
                  coords = c("Pos_X", "Pos_Y"))
if (interactive()) {
shiny::runApp(app, launch.browser = TRUE)
}

## Other input variations (see "The input objects" section):

# 2. Use cytoviewer with images
app_1 <- cytoviewer(image = pancreasImages)
if (interactive()) {
shiny::runApp(app_1, launch.browser = TRUE)
}

# 3. Use cytoviewer with images and masks 
app_2 <- cytoviewer(image = pancreasImages, 
                  mask = pancreasMasks, 
                  img_id = "ImageNb")
if (interactive()) {
 shiny::runApp(app_2, launch.browser = TRUE)
}

# 4. Use cytoviewer with masks and object
app_3 <- cytoviewer(mask = pancreasMasks, 
                  object = pancreasSCE, 
                  img_id = "ImageNb", 
                  cell_id = "CellNb",
                  coords = c("Pos_X", "Pos_Y"))
if (interactive()) {
 shiny::runApp(app_3, launch.browser = TRUE)
}