AggregatedDotPlot: The AggregatedDotPlot class
In iSEEu: iSEE Universe

Description Slot overview Constructor Supported methods Author(s) See Also Examples

Implements an aggregated dot plot where each feature/group combination is represented by a dot. The color of the dot scales with the mean assay value across all samples for a given group, while the size of the dot scales with the proportion of non-zero values across samples in that group.

The following slots control the choice of features:

CustomRows, a logical scalar indicating whether custom rows in CustomRowsText should be used. If TRUE, the feature identities are extracted from the CustomRowsText slot; otherwise they are defined from a transmitted row selection. Defaults to TRUE.
CustomRowsText, a string containing the names of the features of interest, typically corresponding to the row names of the SummarizedExperiment. Names should be new-line separated within this string. Defaults to the name of the first row in the SummarizedExperiment.

The following slots control the specification of groups:

ColumnDataLabel, a string specifying the name of the colData field to use to group cells. The chosen field should correspond to a categorical factor. Defaults to the first categorical field.
ColumnDataFacet, a string specifying the name of the colData field to use for faceting. The chosen field should correspond to a categorical factor. Defaults to "---", i.e., no faceting.

The following slots control the choice of assay values:

Assay, a string specifying the name of the assay containing continuous values, to use for calculating the mean and the proportion of non-zero values. Defaults to the first valid assay name.

The following slots control the visualization parameters:

VisualBoxOpen, a logical scalar indicating whether the visual parameter box should be open on initialization. Defaults to FALSE.
VisualChoices, a character vector specifying the visualization options to show. Defaults to "Color" but can also include "Transform" and "Legend".

The following slots control the transformation of the mean values:

MeanNonZeroes, a logical scalar indicating whether the mean should only be computed over non-zero values. Defaults to FALSE.
Center, a logical scalar indicating whether the means for each feature should be centered across all groups. Defaults to FALSE.
Scale, a logical scalar indicating whether the standard deviation for each feature across all groups should be scaled to unity. Defaults to FALSE.

The following slots control the color:

UseCustomColormap, a logical scalar indicating whether to use a custom color scale. Defaults to FALSE, in which case the application-wide color scale defined by ExperimentColorMap is used.
CustomColorLow, a string specifying the low color (i.e., at an average of zero) for a custom scale. Defaults to "grey".
CustomColorHigh, a string specifying the high color for a custom scale. Defaults to "red".
CenteredColormap, a string specifying the divergent colormap to use when Center is TRUE. Defaults to "blue < grey < orange"; other choices are "purple < black < yellow", "blue < grey < red" and "green < grey < red".

In addition, this class inherits all slots from its parent Panel class.

AggregatedDotPlot(...) creates an instance of a AggregatedDotPlot class, where any slot and its value can be passed to ... as a named argument.

In the following code snippets, x is an instance of an AggregatedDotPlot class. Refer to the documentation for each method for more details on the remaining arguments.

For setting up data values:

.cacheCommonInfo(x) adds a "AggregatedDotPlot" entry containing continuous.assay.names and discrete.colData.names.
.refineParameters(x, se) returns x after setting "Assay", "ColumnDataLabel" and "ColumnDataFacet" to valid values. If continuous assays or discrete colData variables are not available, NULL is returned instead.

For defining the interface:

.defineInterface(x, se, select_info) creates an interface to modify the various parameters in the slots, mostly by calling the parent method and adding another visualization parameter box.
.defineDataInterface(x, se, select_info) creates an interface to modify the data-related parameters, i.e., those that affect the position of the points.
.defineOutput(x) defines the output HTML element.
.panelColor(x) will return the specified default color for this panel class.
.fullName(x) will return "Aggregated dot plot".
.hideInterface(x) will return TRUE for UI elements related to multiple row selections.

For monitoring reactive expressions:

.createObservers(x, se, input, session, pObjects, rObjects) will create all relevant observers for the UI elements.

For generating output:

.generateOutput(x, se, all_memory, all_contents) will return the aggregated dot plot as a ggplot object, along with the commands used for its creation.
.renderOutput(x, se, output, pObjects, rObjects) will render the aggregated dot plot onto the interface.
.exportOutput(x, se, all_memory, all_contents) will save the aggregated dot plot to a PDF file named after x, returning the path to the new file.

For providing documentation:

.definePanelTour(x) will return a data.frame to be used in rintrojs as a panel-specific tour.

Aaron Lun

Panel, for the immediate parent class.

ComplexHeatmapPlot, for another panel with multi-row visualization capability.

library(scRNAseq)

# Example data ----
sce <- ReprocessedAllenData(assays="tophat_counts")
class(sce)

library(scater)
sce <- logNormCounts(sce, exprs_values="tophat_counts")

# launch the app itself ----
if (interactive()) {
    iSEE(sce, initial=list(
        AggregatedDotPlot(ColumnDataLabel="Primary.Type")
    ))
}