AggregatedDotPlot: The AggregatedDotPlot class

In csoneson/iSEEu: iSEE Universe

The AggregatedDotPlot class

Description

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.

Slot overview

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.

Constructor

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

Supported methods

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.

Author(s)

Aaron Lun

See Also

Panel, for the immediate parent class.

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

Examples

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")
    ))
}

csoneson/iSEEu documentation built on April 1, 2024, 9:35 a.m.