This shiny application allows users to gate cells based on their raw or transformed expression values and visualises gated cells on their corresponding images.
cytomapperShiny(
object,
mask = NULL,
image = NULL,
cell_id = NULL,
img_id = NULL,
...
)a SingleCellExperiment object.
(optional) a CytoImageList containing
single-channel Image objects.
(optional) a CytoImageList object containing
single or multi-channel Image objects.
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).
character specifying the colData(object) and
mcols(mask) and/or mcols(image) entry,
in which the image IDs are stored.
parameters passed to the plotCells or
plotPixels function.
A Shiny app object for hierarchical gating of cells
This function requires at least a SingleCellExperiment
input object. Gating is performed on cell-specific marker counts stored in the
assay slots of the object. These can either be raw counts (usually
stored in the counts slot) or transformed/scaled counts stored in other
assay slots. Gating can only be performed sample-wise; therefore, even if
mask or image are not specified, img_id needs to point to
the colData entry storing unique sample IDs. Furthermore, the
cell_id entry is required to identify cells during hierarchical gating.
If mask is specified, marker expression values and selected cells will be
visualised by using the plotCells function. To visualise
pixel-level information with plotPixels, the user has to
further provide multi-channel images stored in a CytoImageList.
The UI's body is composed of two tabs (Scatter Plots and
Images). The side bar contains the General Control and
Plots panels. Both panels can be collapsed; however only one can be
expanded again.. Using the control panel, the user can set the number of
plots, which sample to display and which assay slot to use for plotting
expression. The Plots panel can be used to select up to two markers per
plot. A one-marker selection is displayed as violin and jittered points, two
markers are shown as scatter plot. To define a subset of cells, the user can
draw gates on the plots. When using multiple plots, the gate applied in a plot
will define the subset of cells displayed in the next plot. The number of
plots, the marker selection and the applied gates will be transferred when
switching between samples. When switching markers or assay slots, the gates
will be removed.
By switching to the Images tab, the user can inspect marker expression
levels and the result of the gating. This requires either an entry to
mask and (optionally) image. Depending on the input, the left
image visualises the expression of the selected markers by using
plotCells (if mask is supplied) or
plotPixels (if image is supplied). Displayed markers can
be changed using the dropdown menu. If images are provided, the user
can change the contrast of the selected markers. The right image colors
(masks) or outlines (images) gated cells. Both the left and the
right image come with a zoom-in functionality.
The header section of the shiny allows to hide the side bar, to download
selected cells (see below), or to display the sessionInfo output and
the Help section (see below).
The user can download the selected cells in form of a
SingleCellExperiment object. The output is a subset of
the input SingleCellExperiment object. The downloaded object (in form
of a .rds file) contains a new colData entry containing the label of
the subset defined by the user (using the Cell label argument). The
cell label is stored in colData(object)$cytomapper_CellLabel
The metadata slot of the SingleCellExperiment
object will be converted to a list. It contains the original metadata
(metadata(object)$metadata), gating parameters (e.g.
metadata(object)$cytomapper_gate_1), sessionInfo() output
(metadata(object)$cytomapper_SessionInfo), and the date
(metadata(object)$cytomapper_GatingDate).
To customise the visual output of cytomapperShiny, refer to
plotting-param. Further arguments can be passed to the
plotCells and plotPixels function. As an example:
To avoid interpolation of output images, set interpolate = FALSE.
Passing the parameter interpolate = FALSE will make images less blurry,
which might be useful while using the zoom-in functionality of
cytomapperShiny .
Please refer to the Help section found when clicking the '?' button in the upper right corner. The Help section contains a recommended workflow on how to use the app.
plotCells and plotPixels for the main
plotting functions
## Only run this example in interactive R sessions
if (interactive()) {
# Load example data sets
data("pancreasSCE")
data("pancreasImages")
data("pancreasMasks")
# Use shiny with SCE object, images and masks
cytomapperShiny(object = pancreasSCE, mask = pancreasMasks,
image = pancreasImages, cell_id = "CellNb",
img_id = "ImageNb")
# Use shiny with SCE object and masks
cytomapperShiny(object = pancreasSCE, mask = pancreasMasks,
cell_id = "CellNb", img_id = "ImageNb")
# Use shiny with SCE object only
cytomapperShiny(object = pancreasSCE,
cell_id = "CellNb", img_id = "ImageNb")
}