MAPlot-class: The MAPlot class
In iSEEu: iSEE Universe

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

The MAPlot is a RowDataPlot subclass that is dedicated to creating a MA plot. It retrieves the log-fold change and average abundance and creates a row-based plot where each point represents a feature. Users are expected to load relevant statistics into the rowData of a SummarizedExperiment.

The following slots control the thresholds used in the visualization:

PValueField, a string specifying the field of rowData containing the p-values.
PValueThreshold, a numeric scalar in (0, 1] specifying the threshold to use on the (adjusted) p-value. Defaults to 0.05.
LogFCThreshold, a non-negative numeric scalar specifying the threshold to use on the log-fold change. Defaults to 0.
PValueCorrection, a string specifying the multiple testing correction to apply. Defaults to "BH", but can take any value from p.adjust.methods.

The following slots control the choice of columns in the user interface:

PValuePattern, a character vector specifying the patterns of all potential columns containing p-values, see getPValuePattern.
LogFCPattern, a character vector specifying the patterns of all potential columns containing log-fold changes, see getLogFCPattern.
AveAbPattern, a character vector specifying the patterns of all potential columns containing average abundances, see getAveAbPattern.

In addition, this class inherits all slots from its parent RowDataPlot, RowDotPlot, DotPlot and Panel classes.

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

Initial values for PValuePattern, AveAbPattern and LogFCPattern are set to the outputs of getPValuePattern, getAveAbPattern and getLogFCPattern, respectively. These parameters are considered to be global constants and cannot be changed inside the running iSEE application. Similarly, it is not possible for multiple MAPlots in the same application to have different values for these slots; within the app, all values are set to those of the first encountered MAPlot to ensure consistency.

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

For setting up data values:

.cacheCommonInfo(x, se) returns se after being loaded with class-specific constants. This includes "valid.p.fields", "valid.ab.fields" and "valid.lfc.fields", which are character vectors containing the names of valid rowData columns for the p-values, average abundances and log-fold changes, respectively.
.refineParameters(x, se) returns x after setting XAxis="Row data" and the various *Pattern fields to their cached values. This will also call the equivalent RowDataPlot method for further refinements to x. If valid p-value, abundance and log-fold change fields are not available, NULL is returned instead.

For defining the interface:

.defineDataInterface(x, se, select_info) returns a list of interface elements for manipulating all slots described above.
.panelColor(x) will return the specified default color for this panel class.
.allowableXAxisChoices(x, se) returns a character vector specifying the acceptable average abundance-related variables in rowData(se) that can be used as choices for the x-axis.
.allowableYAxisChoices(x, se) returns a character vector specifying the acceptable log-fold change-related variables in rowData(se) that can be used as choices for the y-axis.
.hideInterface(x, field) will return TRUE for field="XAxis", otherwise it will call the RowDataPlot method.
.fullName(x) will return "MA plot".

For monitoring reactive expressions:

.createObservers(x, se, input, session, pObjects, rObjects) sets up observers for all new slots described above, as well as in the parent classes via the RowDataPlot method.

For creating the plot:

.generateDotPlotData(x, envir) will create a data.frame of row metadata variables in envir. This should contain average abundances on the x-axis and log-fold changes on the y-axis, in addition to an extra field specifying whether or not the feature was considered to be significantly up or down. The method will return the commands required to do so as well as a list of labels.
.prioritizeDotPlotData(x, envir) will create variables in envir marking the priority of points. Significant features receive higher priority (i.e., are plotted over their non-significant counterparts) and are less aggressively downsampled when Downsample=TRUE. The method will return the commands required to do this as well as a logical scalar indicating that rescaling of downsampling resolution is performed.
.colorByNoneDotPlotField(x) will return a string specifying the field of the data.frame (generated by .generateDotPlotData) containing the significance information. This is to be used for coloring when ColorBy="None".
.colorByNoneDotPlotScale(x) will return a string containing a ggplot2 command to add a default color scale when ColorBy="None".
.generateDotPlot(x, labels, envir) returns a list containing plot and commands, using the inital ColumnDataPlot ggplot and adding horizontal lines demarcating the log-fold change threshold.

For documentation:

.definePanelTour(x) returns an data.frame containing the steps of a panel-specific tour.

Aaron Lun

RowDataPlot, for the base class.

# Making up some results:
se <- SummarizedExperiment(matrix(rnorm(10000), 1000, 10))
rownames(se) <- paste0("GENE_", seq_len(nrow(se)))
rowData(se)$PValue <- runif(nrow(se))
rowData(se)$LogFC <- rnorm(nrow(se))
rowData(se)$AveExpr <- rnorm(nrow(se))

if (interactive()) {
    iSEE(se, initial=list(MAPlot()))
}