R/panel_SampleAssayPlot.R
In csoneson/SEE: Interactive SummarizedExperiment Explorer
Defines functions
SampleAssayPlot
Documented in
SampleAssayPlot
#' The SampleAssayPlot panel
#'
#' The SampleAssayPlot is a panel class for creating a \linkS4class{RowDotPlot} where the y-axis represents the expression of a sample of interest, using the \code{\link{assay}} values of the \linkS4class{SummarizedExperiment}.
#' It provides slots and methods for specifying the sample and what to plot on the x-axis, as well as a method to actually create a data.frame containing those pieces of data in preparation for plotting.
#'
#' @section Slot overview:
#' The following slots control the values on the y-axis:
#' \itemize{
#' \item \code{YAxisSampleName}, a string specifying the name of the sample to plot on the y-axis.
#' If \code{NA}, defaults to the first column name of the SummarizedExperiment object.
#' \item \code{Assay}, string specifying the name of the assay to use for obtaining expression values.
#' Defaults to \code{"logcounts"} in \code{\link{getPanelDefault}}, falling back to the name of the first valid assay
#' (see \code{?"\link{.cacheCommonInfo,DotPlot-method}"} for the definition of validity).
#' \item \code{YAxisSampleSource}, string specifying the encoded name of the transmitting panel to obtain a single selection that replaces \code{YAxisSampleName}.
#' Defaults to \code{"---"}, i.e., no transmission is performed.
#' \item \code{YAxisSampleDynamicSource}, a logical scalar indicating whether \code{x} should dynamically change its selection source for the y-axis.
#' Defaults to \code{FALSE} in \code{\link{getPanelDefault}}.
#' }
#'
#' The following slots control the values on the x-axis:
#' \itemize{
#' \item \code{XAxis}, string specifying what should be plotted on the x-axis.
#' This can be any one of \code{"None"}, \code{"Sample name"}, \code{"Row data"} or \code{"Row selection"}.
#' Defaults to \code{"None"}.
#' \item \code{XAxisColumnData}, string specifying which column of the \code{\link{colData}} should be shown on the x-axis,
#' if \code{XAxis="Column data"}.
#' Defaults to the first valid \code{\link{colData}} field (see \code{?"\link{.refineParameters,ColumnDotPlot-method}"} for details).
#' \item \code{XAaxisSampleName}, string specifying the name of the sample to plot on the x-axis,
#' if \code{XAxis="Sample name"}.
#' Defaults to the first column name.
#' \item \code{XAxisSampleSource}, string specifying the encoded name of the transmitting panel to obtain a single selection that replaces \code{XAxisSampleName}.
#' Defaults to \code{"---"}, i.e., no transmission is performed.
#' \item \code{XAxisSampleDynamicSource}, a logical scalar indicating whether \code{x} should dynamically change its selection source for the x-axis.
#' Defaults to \code{FALSE} in \code{\link{getPanelDefault}}.
#' }
#'
#' In addition, this class inherits all slots from its parent \linkS4class{ColumnDotPlot}, \linkS4class{DotPlot} and \linkS4class{Panel} classes.
#'
#' @section Constructor:
#' \code{SampleAssayPlot(...)} creates an instance of a SampleAssayPlot class, where any slot and its value can be passed to \code{...} as a named argument.
#'
#' @section Supported methods:
#' In the following code snippets, \code{x} is an instance of a \linkS4class{SampleAssayPlot} class.
#' Refer to the documentation for each method for more details on the remaining arguments.
#'
#' For setting up data values:
#' \itemize{
#' \item \code{\link{.refineParameters}(x, se)} replaces any \code{NA} values in \code{XAxisSampleName} and \code{YAxisSampleName} with the first column name; any \code{NA} value in \code{Assay} with the first valid assay name; and any \code{NA} value in \code{XAxisColumnData} with the first valid column metadata field.
#' This will also call the equivalent \linkS4class{ColumnDotPlot} method for further refinements to \code{x}.
#' If no columns or assays are present, \code{NULL} is returned instead.
#' }
#'
#' For defining the interface:
#' \itemize{
#' \item \code{\link{.defineDataInterface}(x, se, select_info)} returns a list of interface elements for manipulating all slots described above.
#' \item \code{\link{.panelColor}(x)} will return the specified default color for this panel class.
#' }
#'
#' For monitoring reactive expressions:
#' \itemize{
#' \item \code{\link{.createObservers}(x, se, input, session, pObjects, rObjects)} sets up observers for all slots described above and in the parent classes.
#' This will also call the equivalent \linkS4class{ColumnDotPlot} method.
#' }
#'
#' For defining the panel name:
#' \itemize{
#' \item \code{\link{.fullName}(x)} will return \code{"Sample assay plot"}.
#' }
#'
#' For creating the plot:
#' \itemize{
#' \item \code{\link{.generateDotPlotData}(x, envir)} will create a data.frame of sample assay values in \code{envir}.
#' It will return the commands required to do so as well as a list of labels.
#' }
#'
#' For managing selections:
#' \itemize{
#' \item \code{\link{.singleSelectionSlots}(x)} will return a list specifying the slots that can be updated by single selections in transmitter panels,
#' mostly related to the choice of sample on the x- and y-axes.
#' This includes the output of the method for the parent \linkS4class{RowDotPlot} class.
#' \item \code{\link{.multiSelectionInvalidated}(x)} returns \code{TRUE} if the x-axis uses multiple row selections,
#' such that the point coordinates may change upon updates to upstream selections in transmitting panels.
#' Otherwise, it dispatches to the \linkS4class{RowDotPlot} method.
#' }
#'
#' For documentation:
#' \itemize{
#' \item \code{\link{.definePanelTour}(x)} returns an data.frame containing a panel-specific tour.
#' }
#'
#' @author Aaron Lun
#'
#' @seealso
#' \linkS4class{ColumnDotPlot}, for the immediate parent class.
#'
#' @examples
#' #################
#' # For end-users #
#' #################
#'
#' x <- SampleAssayPlot()
#' x[["XAxis"]]
#' x[["Assay"]] <- "logcounts"
#' x[["XAxisRowData"]] <- "stuff"
#'
#' ##################
#' # For developers #
#' ##################
#'
#' library(scater)
#' sce <- mockSCE()
#' sce <- logNormCounts(sce)
#'
#' old_assay_names <- assayNames(sce)
#' assayNames(sce) <- character(length(old_assay_names))
#'
#' # Spits out a NULL and a warning if no assays are named.
#' sce0 <- .cacheCommonInfo(x, sce)
#' .refineParameters(x, sce0)
#'
#' # Replaces the default with something sensible.
#' assayNames(sce) <- old_assay_names
#' sce0 <- .cacheCommonInfo(x, sce)
#' .refineParameters(x, sce0)
#'
#' @docType methods
#' @aliases SampleAssayPlot SampleAssayPlot-class
#' initialize,SampleAssayPlot-method
#' .refineParameters,SampleAssayPlot-method
#' .defineDataInterface,SampleAssayPlot-method
#' .createObservers,SampleAssayPlot-method
#' .singleSelectionSlots,SampleAssayPlot-method
#' .multiSelectionInvalidated,SampleAssayPlot-method
#' .fullName,SampleAssayPlot-method
#' .panelColor,SampleAssayPlot-method
#' .generateDotPlotData,SampleAssayPlot-method
#' .definePanelTour,SampleAssayPlot-method
#'
#' @name SampleAssayPlot-class
NULL
#' @export
SampleAssayPlot <- function(...) {
new("SampleAssayPlot", ...)
}
#' @export
#' @importFrom methods callNextMethod
setMethod("initialize", "SampleAssayPlot", function(.Object, ...) {
args <- list(...)
args <- .emptyDefault(args, .sampAssayAssay, getPanelDefault(.sampAssayAssay))
args <- .emptyDefault(args, .sampAssayXAxis, .sampAssayXAxisNothingTitle)
args <- .emptyDefault(args, .sampAssayXAxisRowData, NA_character_)
args <- .emptyDefault(args, .sampAssayXAxisColTable, .noSelection)
args <- .emptyDefault(args, .sampAssayXAxisSampName, NA_character_)
args <- .emptyDefault(args, .sampAssayXAxisSampDynamic, getPanelDefault("SingleSelectionDynamicSource"))
args <- .emptyDefault(args, .sampAssayYAxisColTable, .noSelection)
args <- .emptyDefault(args, .sampAssayYAxisSampName, NA_character_)
args <- .emptyDefault(args, .sampAssayYAxisSampDynamic, getPanelDefault("SingleSelectionDynamicSource"))
do.call(callNextMethod, c(list(.Object), args))
})
#' @export
#' @importFrom SingleCellExperiment reducedDim
#' @importFrom methods callNextMethod
setMethod(".refineParameters", "SampleAssayPlot", function(x, se) {
x <- callNextMethod()
if (is.null(x)) {
return(NULL)
}
if (ncol(se)==0L) {
warning(sprintf("no columns for plotting '%s'", class(x)[1]))
return(NULL)
}
all_assays <- .getCachedCommonInfo(se, "DotPlot")$valid.assay.names
if (length(all_assays)==0L) {
warning(sprintf("no named 'assays' for plotting '%s'", class(x)[1]))
return(NULL)
}
x <- .replaceMissingWithFirst(x, .sampAssayAssay, all_assays)
for (field in c(.sampAssayXAxisSampName, .sampAssayYAxisSampName)) {
x <- .replaceMissingWithFirst(x, field, colnames(se))
}
row_covariates <- .getCachedCommonInfo(se, "RowDotPlot")$valid.rowData.names
if (length(row_covariates)==0L) {
if (slot(x, .sampAssayXAxis)==.sampAssayXAxisRowDataTitle) {
slot(x, .sampAssayXAxis) <- .sampAssayXAxisNothingTitle
}
} else {
x <- .replaceMissingWithFirst(x, .sampAssayXAxisRowData, row_covariates)
}
x
})
.sampAssayXAxisNothingTitle <- "None"
.sampAssayXAxisRowDataTitle <- "Row data"
.sampAssayXAxisSampNameTitle <- "Sample name"
.sampAssayXAxisSelectionsTitle <- "Row selection"
#' @importFrom S4Vectors setValidity2
setValidity2("SampleAssayPlot", function(object) {
msg <- character(0)
msg <- .allowableChoiceError(msg, object, .sampAssayXAxis,
c(.sampAssayXAxisNothingTitle, .sampAssayXAxisRowDataTitle, .sampAssayXAxisSampNameTitle, .sampAssayXAxisSelectionsTitle))
msg <- .singleStringError(msg, object,
c(.sampAssayAssay, .sampAssayXAxisRowData, .sampAssayXAxisColTable,
.sampAssayXAxisSampName, .sampAssayYAxisColTable, .sampAssayYAxisSampName))
if (length(msg)) {
return(msg)
}
TRUE
})
#' @export
#' @importFrom shiny selectInput radioButtons
#' @importFrom methods callNextMethod
setMethod(".defineDataInterface", "SampleAssayPlot", function(x, se, select_info) {
panel_name <- .getEncodedName(x)
.input_FUN <- function(field) { paste0(panel_name, "_", field) }
row_covariates <- .getCachedCommonInfo(se, "RowDotPlot")$valid.rowData.names
all_assays <- .getCachedCommonInfo(se, "DotPlot")$valid.assay.names
tab_by_col <- select_info$single$sample
xaxis_choices <- c(.sampAssayXAxisNothingTitle)
if (length(row_covariates)) { # As it is possible for this plot to be _feasible_ but for no row data to exist.
xaxis_choices <- c(xaxis_choices, .sampAssayXAxisRowDataTitle)
}
xaxis_choices <- c(xaxis_choices, .sampAssayXAxisSampNameTitle, .sampAssayXAxisSelectionsTitle)
.addSpecificTour(class(x)[1], .sampAssayYAxisSampName, function(plot_name) {
data.frame(
rbind(
c(
element=paste0("#", plot_name, "_", .sampAssayYAxisSampName, " + .selectize-control"),
intro="Here, we choose the sample to show on the y-axis.
This is based on the column names of the input <code>SummarizedExperiment</code>."
),
c(
element=paste0("#", plot_name, "_", .sampAssayAssay, " + .selectize-control"),
intro="This specifies the assay values to be shown."
),
c(
element=paste0("#", plot_name, "_", .sampAssayYAxisColTable, " + .selectize-control"),
intro="We can configure the plot so that the sample on the y-axis automatically changes based on a sample selection in another panel.
A common use case is to configure this panel so that we receive a selection from a <em>Column Data Table</em>,
such that users browsing the table can immediately examine the assay values for a sample of interest."
),
c(
element=paste0("#", plot_name, "_", .sampAssayYAxisSampDynamic),
intro="And in fact, we don't have to even specify the \"other panel\" ourselves.
If this box is checked, any column-based selection in any other panel of the <strong>iSEE</strong> application will be used to specify the sample on the y-axis in this panel.
This is achieved by dynamically changing the identity of the designated panel from which we receive the selection."
)
)
)
})
.addSpecificTour(class(x)[1], .sampAssayXAxis, function(plot_name) {
data.frame(
rbind(
c(
element=paste0("#", plot_name, "_", .sampAssayXAxis),
intro="Here, we can choose what to show on the x-axis."
),
if (length(row_covariates)) {
rbind(
c(
element=paste0("#", plot_name, "_", .sampAssayXAxis),
intro="If we <strong>select <em>Row data</em></strong>..."
),
c(
element=paste0("#", plot_name, "_", .sampAssayXAxisRowData, " + .selectize-control"),
intro="... we can stratify points on the x-axis based on a field of interest in the <code>rowData</code>."
)
)
},
c(
element=paste0("#", plot_name, "_", .sampAssayXAxis),
intro="If we <strong>select <em>Sample name</em></strong>..."
),
c(
element=paste0("#", plot_name, "_", .sampAssayXAxisSampName, " + .selectize-control"),
intro="... we can show the assay values of another sample of interest on the x-axis.
In other words, plotting one sample against another for the same set of assay values."
),
c(
element=paste0("#", plot_name, "_", .sampAssayXAxisColTable, " + .selectize-control"),
intro="Just like the sample on the y-axis, the x-axis sample can automatically change in response to a sample selection made in another panel.
We can either choose the \"other panel\" manually with this dropdown..."
),
c(
element=paste0("#", plot_name, "_", .sampAssayXAxisSampDynamic),
intro="... or we can dynamically change the identity of the other panel.
If this box is checked, a column-based selection in any other panel of the <strong>iSEE</strong> application will be used to specify the sample on the x-axis in this panel."
),
c(
element=paste0("#", plot_name, "_", .sampAssayXAxis),
intro="Finally, we can stratify points based on whether they are included in a multiple row selection made in another panel.
For example, if our \"other panel\" is a row-based plot containing a brush, we would see two violin plots in this panel;
one corresponding to the selected points inside the brush, and another corresponding to the unselected points."
)
)
)
})
list(
.selectizeInput.iSEE(
x, .sampAssayYAxisSampName,
label="Sample of interest (Y-axis):",
choices=NULL, selected=NULL, multiple=FALSE),
selectInput(
.input_FUN(.sampAssayYAxisColTable), label=NULL, choices=tab_by_col,
selected=.choose_link(slot(x, .sampAssayYAxisColTable), tab_by_col)),
checkboxInput(.input_FUN(.sampAssayYAxisSampDynamic),
label="Use dynamic sample selection for the y-axis",
value=slot(x, .sampAssayYAxisSampDynamic)),
selectInput(paste0(.getEncodedName(x), "_", .sampAssayAssay), label=NULL,
choices=all_assays, selected=slot(x, .sampAssayAssay)),
.radioButtons.iSEE(
x, .sampAssayXAxis, label="X-axis:", inline=TRUE,
choices=xaxis_choices, selected=slot(x, .sampAssayXAxis)),
.conditionalOnRadio(
.input_FUN(.sampAssayXAxis),
.sampAssayXAxisRowDataTitle,
selectInput(
.input_FUN(.sampAssayXAxisRowData),
label="Row data of interest (X-axis):",
choices=row_covariates, selected=slot(x, .sampAssayXAxisRowData))),
.conditionalOnRadio(
.input_FUN(.sampAssayXAxis),
.sampAssayXAxisSampNameTitle,
selectizeInput(
.input_FUN(.sampAssayXAxisSampName),
label="Sample of interest (X-axis):",
choices=NULL, selected=NULL, multiple=FALSE),
selectInput(.input_FUN(.sampAssayXAxisColTable), label=NULL,
choices=tab_by_col, selected=slot(x, .sampAssayXAxisColTable)),
checkboxInput(.input_FUN(.sampAssayXAxisSampDynamic),
label="Use dynamic sample selection for the x-axis",
value=slot(x, .sampAssayXAxisSampDynamic))
)
)
})
#' @export
#' @importFrom shiny updateSelectInput
#' @importFrom methods callNextMethod
setMethod(".createObservers", "SampleAssayPlot", function(x, se, input, session, pObjects, rObjects) {
callNextMethod()
plot_name <- .getEncodedName(x)
.createProtectedParameterObservers(plot_name,
fields=c(.sampAssayAssay, .sampAssayXAxisRowData),
input=input, pObjects=pObjects, rObjects=rObjects)
})
#' @export
setMethod(".singleSelectionSlots", "SampleAssayPlot", function(x) {
c(callNextMethod(),
list(
list(
parameter=.sampAssayXAxisSampName,
source=.sampAssayXAxisColTable,
dimension="sample",
dynamic=.sampAssayXAxisSampDynamic,
use_mode=.sampAssayXAxis,
use_value=.sampAssayXAxisSampNameTitle,
protected=TRUE
),
list(
parameter=.sampAssayYAxisSampName,
source=.sampAssayYAxisColTable,
dimension="sample",
dynamic=.sampAssayYAxisSampDynamic,
use_mode=NA,
use_value=NA,
protected=TRUE
)
)
)
})
#' @export
setMethod(".multiSelectionInvalidated", "SampleAssayPlot", function(x) {
slot(x, .sampAssayXAxis) == .sampAssayXAxisSelectionsTitle || callNextMethod()
})
#' @export
setMethod(".fullName", "SampleAssayPlot", function(x) "Sample assay plot")
#' @export
setMethod(".panelColor", "SampleAssayPlot", function(x) "#07A274")
#' @export
setMethod(".generateDotPlotData", "SampleAssayPlot", function(x, envir) {
data_cmds <- list()
samp_selected_y <- slot(x, .sampAssayYAxisSampName)
assay_choice <- slot(x, .sampAssayAssay)
plot_title <- samp_selected_y
y_lab <- sprintf("%s (%s)", samp_selected_y, assay_choice)
data_cmds[["y"]] <- sprintf(
"plot.data <- data.frame(Y=assay(se, %s)[,%s], row.names=rownames(se));",
deparse(assay_choice), deparse(samp_selected_y)
)
# Prepare X-axis data.
x_choice <- slot(x, .sampAssayXAxis)
if (x_choice == .sampAssayXAxisNothingTitle) {
x_lab <- ''
data_cmds[["x"]] <- "plot.data$X <- factor(character(nrow(se)));"
} else if (x_choice == .sampAssayXAxisRowDataTitle) {
x_lab <- slot(x, .sampAssayXAxisRowData)
plot_title <- paste(plot_title, "vs", x_lab)
data_cmds[["x"]] <- sprintf("plot.data$X <- rowData(se)[, %s];", deparse(x_lab))
} else if (x_choice == .sampAssayXAxisSelectionsTitle) {
x_lab <- "Row selection"
plot_title <- paste(plot_title, "vs row selection")
if (exists("row_selected", envir=envir, inherits=FALSE)) {
target <- "row_selected"
} else {
target <- "list()"
}
data_cmds[["x"]] <- sprintf(
"plot.data$X <- iSEE::multiSelectionToFactor(%s, rownames(se));",
target
)
} else {
samp_selected_x <- slot(x, .sampAssayXAxisSampName)
plot_title <- paste(plot_title, "vs", samp_selected_x)
x_lab <- sprintf("%s (%s)", samp_selected_x, assay_choice)
data_cmds[["x"]] <- sprintf(
"plot.data$X <- assay(se, %s)[, %s];",
deparse(assay_choice), deparse(samp_selected_x)
)
}
data_cmds <- unlist(data_cmds)
.textEval(data_cmds, envir)
list(commands=data_cmds, labels=list(title=plot_title, X=x_lab, Y=y_lab))
})
#' @export
setMethod(".definePanelTour", "SampleAssayPlot", function(x) {
collated <- rbind(
c(paste0("#", .getEncodedName(x)), sprintf("The <font color=\"%s\">Sample assay plot</font> panel shows assay values for a particular sample (i.e., column) of a <code>SummarizedExperiment</code> object or one of its subclasses. Here, each point corresponds to a row (usually a feature) of the <code>SummarizedExperiment</code> object, and the y-axis represents the assay values.", .getPanelColor(x))),
.addTourStep(x, .dataParamBoxOpen, "The <i>Data parameters</i> box shows the available parameters that can be tweaked in this plot.<br/><br/><strong>Action:</strong> click on this box to open up available options.")
)
rbind(
data.frame(element=collated[,1], intro=collated[,2], stringsAsFactors=FALSE),
callNextMethod()
)
})
csoneson/SEE documentation built on May 6, 2024, 1:55 a.m.
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Defines functions SampleAssayPlot
Documented in SampleAssayPlot
#' The SampleAssayPlot panel
#'
#' The SampleAssayPlot is a panel class for creating a \linkS4class{RowDotPlot} where the y-axis represents the expression of a sample of interest, using the \code{\link{assay}} values of the \linkS4class{SummarizedExperiment}.
#' It provides slots and methods for specifying the sample and what to plot on the x-axis, as well as a method to actually create a data.frame containing those pieces of data in preparation for plotting.
#'
#' @section Slot overview:
#' The following slots control the values on the y-axis:
#' \itemize{
#' \item \code{YAxisSampleName}, a string specifying the name of the sample to plot on the y-axis.
#' If \code{NA}, defaults to the first column name of the SummarizedExperiment object.
#' \item \code{Assay}, string specifying the name of the assay to use for obtaining expression values.
#' Defaults to \code{"logcounts"} in \code{\link{getPanelDefault}}, falling back to the name of the first valid assay
#' (see \code{?"\link{.cacheCommonInfo,DotPlot-method}"} for the definition of validity).
#' \item \code{YAxisSampleSource}, string specifying the encoded name of the transmitting panel to obtain a single selection that replaces \code{YAxisSampleName}.
#' Defaults to \code{"---"}, i.e., no transmission is performed.
#' \item \code{YAxisSampleDynamicSource}, a logical scalar indicating whether \code{x} should dynamically change its selection source for the y-axis.
#' Defaults to \code{FALSE} in \code{\link{getPanelDefault}}.
#' }
#'
#' The following slots control the values on the x-axis:
#' \itemize{
#' \item \code{XAxis}, string specifying what should be plotted on the x-axis.
#' This can be any one of \code{"None"}, \code{"Sample name"}, \code{"Row data"} or \code{"Row selection"}.
#' Defaults to \code{"None"}.
#' \item \code{XAxisColumnData}, string specifying which column of the \code{\link{colData}} should be shown on the x-axis,
#' if \code{XAxis="Column data"}.
#' Defaults to the first valid \code{\link{colData}} field (see \code{?"\link{.refineParameters,ColumnDotPlot-method}"} for details).
#' \item \code{XAaxisSampleName}, string specifying the name of the sample to plot on the x-axis,
#' if \code{XAxis="Sample name"}.
#' Defaults to the first column name.
#' \item \code{XAxisSampleSource}, string specifying the encoded name of the transmitting panel to obtain a single selection that replaces \code{XAxisSampleName}.
#' Defaults to \code{"---"}, i.e., no transmission is performed.
#' \item \code{XAxisSampleDynamicSource}, a logical scalar indicating whether \code{x} should dynamically change its selection source for the x-axis.
#' Defaults to \code{FALSE} in \code{\link{getPanelDefault}}.
#' }
#'
#' In addition, this class inherits all slots from its parent \linkS4class{ColumnDotPlot}, \linkS4class{DotPlot} and \linkS4class{Panel} classes.
#'
#' @section Constructor:
#' \code{SampleAssayPlot(...)} creates an instance of a SampleAssayPlot class, where any slot and its value can be passed to \code{...} as a named argument.
#'
#' @section Supported methods:
#' In the following code snippets, \code{x} is an instance of a \linkS4class{SampleAssayPlot} class.
#' Refer to the documentation for each method for more details on the remaining arguments.
#'
#' For setting up data values:
#' \itemize{
#' \item \code{\link{.refineParameters}(x, se)} replaces any \code{NA} values in \code{XAxisSampleName} and \code{YAxisSampleName} with the first column name; any \code{NA} value in \code{Assay} with the first valid assay name; and any \code{NA} value in \code{XAxisColumnData} with the first valid column metadata field.
#' This will also call the equivalent \linkS4class{ColumnDotPlot} method for further refinements to \code{x}.
#' If no columns or assays are present, \code{NULL} is returned instead.
#' }
#'
#' For defining the interface:
#' \itemize{
#' \item \code{\link{.defineDataInterface}(x, se, select_info)} returns a list of interface elements for manipulating all slots described above.
#' \item \code{\link{.panelColor}(x)} will return the specified default color for this panel class.
#' }
#'
#' For monitoring reactive expressions:
#' \itemize{
#' \item \code{\link{.createObservers}(x, se, input, session, pObjects, rObjects)} sets up observers for all slots described above and in the parent classes.
#' This will also call the equivalent \linkS4class{ColumnDotPlot} method.
#' }
#'
#' For defining the panel name:
#' \itemize{
#' \item \code{\link{.fullName}(x)} will return \code{"Sample assay plot"}.
#' }
#'
#' For creating the plot:
#' \itemize{
#' \item \code{\link{.generateDotPlotData}(x, envir)} will create a data.frame of sample assay values in \code{envir}.
#' It will return the commands required to do so as well as a list of labels.
#' }
#'
#' For managing selections:
#' \itemize{
#' \item \code{\link{.singleSelectionSlots}(x)} will return a list specifying the slots that can be updated by single selections in transmitter panels,
#' mostly related to the choice of sample on the x- and y-axes.
#' This includes the output of the method for the parent \linkS4class{RowDotPlot} class.
#' \item \code{\link{.multiSelectionInvalidated}(x)} returns \code{TRUE} if the x-axis uses multiple row selections,
#' such that the point coordinates may change upon updates to upstream selections in transmitting panels.
#' Otherwise, it dispatches to the \linkS4class{RowDotPlot} method.
#' }
#'
#' For documentation:
#' \itemize{
#' \item \code{\link{.definePanelTour}(x)} returns an data.frame containing a panel-specific tour.
#' }
#'
#' @author Aaron Lun
#'
#' @seealso
#' \linkS4class{ColumnDotPlot}, for the immediate parent class.
#'
#' @examples
#' #################
#' # For end-users #
#' #################
#'
#' x <- SampleAssayPlot()
#' x[["XAxis"]]
#' x[["Assay"]] <- "logcounts"
#' x[["XAxisRowData"]] <- "stuff"
#'
#' ##################
#' # For developers #
#' ##################
#'
#' library(scater)
#' sce <- mockSCE()
#' sce <- logNormCounts(sce)
#'
#' old_assay_names <- assayNames(sce)
#' assayNames(sce) <- character(length(old_assay_names))
#'
#' # Spits out a NULL and a warning if no assays are named.
#' sce0 <- .cacheCommonInfo(x, sce)
#' .refineParameters(x, sce0)
#'
#' # Replaces the default with something sensible.
#' assayNames(sce) <- old_assay_names
#' sce0 <- .cacheCommonInfo(x, sce)
#' .refineParameters(x, sce0)
#'
#' @docType methods
#' @aliases SampleAssayPlot SampleAssayPlot-class
#' initialize,SampleAssayPlot-method
#' .refineParameters,SampleAssayPlot-method
#' .defineDataInterface,SampleAssayPlot-method
#' .createObservers,SampleAssayPlot-method
#' .singleSelectionSlots,SampleAssayPlot-method
#' .multiSelectionInvalidated,SampleAssayPlot-method
#' .fullName,SampleAssayPlot-method
#' .panelColor,SampleAssayPlot-method
#' .generateDotPlotData,SampleAssayPlot-method
#' .definePanelTour,SampleAssayPlot-method
#'
#' @name SampleAssayPlot-class
NULL
#' @export
SampleAssayPlot <- function(...) {
new("SampleAssayPlot", ...)
}
#' @export
#' @importFrom methods callNextMethod
setMethod("initialize", "SampleAssayPlot", function(.Object, ...) {
args <- list(...)
args <- .emptyDefault(args, .sampAssayAssay, getPanelDefault(.sampAssayAssay))
args <- .emptyDefault(args, .sampAssayXAxis, .sampAssayXAxisNothingTitle)
args <- .emptyDefault(args, .sampAssayXAxisRowData, NA_character_)
args <- .emptyDefault(args, .sampAssayXAxisColTable, .noSelection)
args <- .emptyDefault(args, .sampAssayXAxisSampName, NA_character_)
args <- .emptyDefault(args, .sampAssayXAxisSampDynamic, getPanelDefault("SingleSelectionDynamicSource"))
args <- .emptyDefault(args, .sampAssayYAxisColTable, .noSelection)
args <- .emptyDefault(args, .sampAssayYAxisSampName, NA_character_)
args <- .emptyDefault(args, .sampAssayYAxisSampDynamic, getPanelDefault("SingleSelectionDynamicSource"))
do.call(callNextMethod, c(list(.Object), args))
})
#' @export
#' @importFrom SingleCellExperiment reducedDim
#' @importFrom methods callNextMethod
setMethod(".refineParameters", "SampleAssayPlot", function(x, se) {
x <- callNextMethod()
if (is.null(x)) {
return(NULL)
}
if (ncol(se)==0L) {
warning(sprintf("no columns for plotting '%s'", class(x)[1]))
return(NULL)
}
all_assays <- .getCachedCommonInfo(se, "DotPlot")$valid.assay.names
if (length(all_assays)==0L) {
warning(sprintf("no named 'assays' for plotting '%s'", class(x)[1]))
return(NULL)
}
x <- .replaceMissingWithFirst(x, .sampAssayAssay, all_assays)
for (field in c(.sampAssayXAxisSampName, .sampAssayYAxisSampName)) {
x <- .replaceMissingWithFirst(x, field, colnames(se))
}
row_covariates <- .getCachedCommonInfo(se, "RowDotPlot")$valid.rowData.names
if (length(row_covariates)==0L) {
if (slot(x, .sampAssayXAxis)==.sampAssayXAxisRowDataTitle) {
slot(x, .sampAssayXAxis) <- .sampAssayXAxisNothingTitle
}
} else {
x <- .replaceMissingWithFirst(x, .sampAssayXAxisRowData, row_covariates)
}
x
})
.sampAssayXAxisNothingTitle <- "None"
.sampAssayXAxisRowDataTitle <- "Row data"
.sampAssayXAxisSampNameTitle <- "Sample name"
.sampAssayXAxisSelectionsTitle <- "Row selection"
#' @importFrom S4Vectors setValidity2
setValidity2("SampleAssayPlot", function(object) {
msg <- character(0)
msg <- .allowableChoiceError(msg, object, .sampAssayXAxis,
c(.sampAssayXAxisNothingTitle, .sampAssayXAxisRowDataTitle, .sampAssayXAxisSampNameTitle, .sampAssayXAxisSelectionsTitle))
msg <- .singleStringError(msg, object,
c(.sampAssayAssay, .sampAssayXAxisRowData, .sampAssayXAxisColTable,
.sampAssayXAxisSampName, .sampAssayYAxisColTable, .sampAssayYAxisSampName))
if (length(msg)) {
return(msg)
}
TRUE
})
#' @export
#' @importFrom shiny selectInput radioButtons
#' @importFrom methods callNextMethod
setMethod(".defineDataInterface", "SampleAssayPlot", function(x, se, select_info) {
panel_name <- .getEncodedName(x)
.input_FUN <- function(field) { paste0(panel_name, "_", field) }
row_covariates <- .getCachedCommonInfo(se, "RowDotPlot")$valid.rowData.names
all_assays <- .getCachedCommonInfo(se, "DotPlot")$valid.assay.names
tab_by_col <- select_info$single$sample
xaxis_choices <- c(.sampAssayXAxisNothingTitle)
if (length(row_covariates)) { # As it is possible for this plot to be _feasible_ but for no row data to exist.
xaxis_choices <- c(xaxis_choices, .sampAssayXAxisRowDataTitle)
}
xaxis_choices <- c(xaxis_choices, .sampAssayXAxisSampNameTitle, .sampAssayXAxisSelectionsTitle)
.addSpecificTour(class(x)[1], .sampAssayYAxisSampName, function(plot_name) {
data.frame(
rbind(
c(
element=paste0("#", plot_name, "_", .sampAssayYAxisSampName, " + .selectize-control"),
intro="Here, we choose the sample to show on the y-axis.
This is based on the column names of the input <code>SummarizedExperiment</code>."
),
c(
element=paste0("#", plot_name, "_", .sampAssayAssay, " + .selectize-control"),
intro="This specifies the assay values to be shown."
),
c(
element=paste0("#", plot_name, "_", .sampAssayYAxisColTable, " + .selectize-control"),
intro="We can configure the plot so that the sample on the y-axis automatically changes based on a sample selection in another panel.
A common use case is to configure this panel so that we receive a selection from a <em>Column Data Table</em>,
such that users browsing the table can immediately examine the assay values for a sample of interest."
),
c(
element=paste0("#", plot_name, "_", .sampAssayYAxisSampDynamic),
intro="And in fact, we don't have to even specify the \"other panel\" ourselves.
If this box is checked, any column-based selection in any other panel of the <strong>iSEE</strong> application will be used to specify the sample on the y-axis in this panel.
This is achieved by dynamically changing the identity of the designated panel from which we receive the selection."
)
)
)
})
.addSpecificTour(class(x)[1], .sampAssayXAxis, function(plot_name) {
data.frame(
rbind(
c(
element=paste0("#", plot_name, "_", .sampAssayXAxis),
intro="Here, we can choose what to show on the x-axis."
),
if (length(row_covariates)) {
rbind(
c(
element=paste0("#", plot_name, "_", .sampAssayXAxis),
intro="If we <strong>select <em>Row data</em></strong>..."
),
c(
element=paste0("#", plot_name, "_", .sampAssayXAxisRowData, " + .selectize-control"),
intro="... we can stratify points on the x-axis based on a field of interest in the <code>rowData</code>."
)
)
},
c(
element=paste0("#", plot_name, "_", .sampAssayXAxis),
intro="If we <strong>select <em>Sample name</em></strong>..."
),
c(
element=paste0("#", plot_name, "_", .sampAssayXAxisSampName, " + .selectize-control"),
intro="... we can show the assay values of another sample of interest on the x-axis.
In other words, plotting one sample against another for the same set of assay values."
),
c(
element=paste0("#", plot_name, "_", .sampAssayXAxisColTable, " + .selectize-control"),
intro="Just like the sample on the y-axis, the x-axis sample can automatically change in response to a sample selection made in another panel.
We can either choose the \"other panel\" manually with this dropdown..."
),
c(
element=paste0("#", plot_name, "_", .sampAssayXAxisSampDynamic),
intro="... or we can dynamically change the identity of the other panel.
If this box is checked, a column-based selection in any other panel of the <strong>iSEE</strong> application will be used to specify the sample on the x-axis in this panel."
),
c(
element=paste0("#", plot_name, "_", .sampAssayXAxis),
intro="Finally, we can stratify points based on whether they are included in a multiple row selection made in another panel.
For example, if our \"other panel\" is a row-based plot containing a brush, we would see two violin plots in this panel;
one corresponding to the selected points inside the brush, and another corresponding to the unselected points."
)
)
)
})
list(
.selectizeInput.iSEE(
x, .sampAssayYAxisSampName,
label="Sample of interest (Y-axis):",
choices=NULL, selected=NULL, multiple=FALSE),
selectInput(
.input_FUN(.sampAssayYAxisColTable), label=NULL, choices=tab_by_col,
selected=.choose_link(slot(x, .sampAssayYAxisColTable), tab_by_col)),
checkboxInput(.input_FUN(.sampAssayYAxisSampDynamic),
label="Use dynamic sample selection for the y-axis",
value=slot(x, .sampAssayYAxisSampDynamic)),
selectInput(paste0(.getEncodedName(x), "_", .sampAssayAssay), label=NULL,
choices=all_assays, selected=slot(x, .sampAssayAssay)),
.radioButtons.iSEE(
x, .sampAssayXAxis, label="X-axis:", inline=TRUE,
choices=xaxis_choices, selected=slot(x, .sampAssayXAxis)),
.conditionalOnRadio(
.input_FUN(.sampAssayXAxis),
.sampAssayXAxisRowDataTitle,
selectInput(
.input_FUN(.sampAssayXAxisRowData),
label="Row data of interest (X-axis):",
choices=row_covariates, selected=slot(x, .sampAssayXAxisRowData))),
.conditionalOnRadio(
.input_FUN(.sampAssayXAxis),
.sampAssayXAxisSampNameTitle,
selectizeInput(
.input_FUN(.sampAssayXAxisSampName),
label="Sample of interest (X-axis):",
choices=NULL, selected=NULL, multiple=FALSE),
selectInput(.input_FUN(.sampAssayXAxisColTable), label=NULL,
choices=tab_by_col, selected=slot(x, .sampAssayXAxisColTable)),
checkboxInput(.input_FUN(.sampAssayXAxisSampDynamic),
label="Use dynamic sample selection for the x-axis",
value=slot(x, .sampAssayXAxisSampDynamic))
)
)
})
#' @export
#' @importFrom shiny updateSelectInput
#' @importFrom methods callNextMethod
setMethod(".createObservers", "SampleAssayPlot", function(x, se, input, session, pObjects, rObjects) {
callNextMethod()
plot_name <- .getEncodedName(x)
.createProtectedParameterObservers(plot_name,
fields=c(.sampAssayAssay, .sampAssayXAxisRowData),
input=input, pObjects=pObjects, rObjects=rObjects)
})
#' @export
setMethod(".singleSelectionSlots", "SampleAssayPlot", function(x) {
c(callNextMethod(),
list(
list(
parameter=.sampAssayXAxisSampName,
source=.sampAssayXAxisColTable,
dimension="sample",
dynamic=.sampAssayXAxisSampDynamic,
use_mode=.sampAssayXAxis,
use_value=.sampAssayXAxisSampNameTitle,
protected=TRUE
),
list(
parameter=.sampAssayYAxisSampName,
source=.sampAssayYAxisColTable,
dimension="sample",
dynamic=.sampAssayYAxisSampDynamic,
use_mode=NA,
use_value=NA,
protected=TRUE
)
)
)
})
#' @export
setMethod(".multiSelectionInvalidated", "SampleAssayPlot", function(x) {
slot(x, .sampAssayXAxis) == .sampAssayXAxisSelectionsTitle || callNextMethod()
})
#' @export
setMethod(".fullName", "SampleAssayPlot", function(x) "Sample assay plot")
#' @export
setMethod(".panelColor", "SampleAssayPlot", function(x) "#07A274")
#' @export
setMethod(".generateDotPlotData", "SampleAssayPlot", function(x, envir) {
data_cmds <- list()
samp_selected_y <- slot(x, .sampAssayYAxisSampName)
assay_choice <- slot(x, .sampAssayAssay)
plot_title <- samp_selected_y
y_lab <- sprintf("%s (%s)", samp_selected_y, assay_choice)
data_cmds[["y"]] <- sprintf(
"plot.data <- data.frame(Y=assay(se, %s)[,%s], row.names=rownames(se));",
deparse(assay_choice), deparse(samp_selected_y)
)
# Prepare X-axis data.
x_choice <- slot(x, .sampAssayXAxis)
if (x_choice == .sampAssayXAxisNothingTitle) {
x_lab <- ''
data_cmds[["x"]] <- "plot.data$X <- factor(character(nrow(se)));"
} else if (x_choice == .sampAssayXAxisRowDataTitle) {
x_lab <- slot(x, .sampAssayXAxisRowData)
plot_title <- paste(plot_title, "vs", x_lab)
data_cmds[["x"]] <- sprintf("plot.data$X <- rowData(se)[, %s];", deparse(x_lab))
} else if (x_choice == .sampAssayXAxisSelectionsTitle) {
x_lab <- "Row selection"
plot_title <- paste(plot_title, "vs row selection")
if (exists("row_selected", envir=envir, inherits=FALSE)) {
target <- "row_selected"
} else {
target <- "list()"
}
data_cmds[["x"]] <- sprintf(
"plot.data$X <- iSEE::multiSelectionToFactor(%s, rownames(se));",
target
)
} else {
samp_selected_x <- slot(x, .sampAssayXAxisSampName)
plot_title <- paste(plot_title, "vs", samp_selected_x)
x_lab <- sprintf("%s (%s)", samp_selected_x, assay_choice)
data_cmds[["x"]] <- sprintf(
"plot.data$X <- assay(se, %s)[, %s];",
deparse(assay_choice), deparse(samp_selected_x)
)
}
data_cmds <- unlist(data_cmds)
.textEval(data_cmds, envir)
list(commands=data_cmds, labels=list(title=plot_title, X=x_lab, Y=y_lab))
})
#' @export
setMethod(".definePanelTour", "SampleAssayPlot", function(x) {
collated <- rbind(
c(paste0("#", .getEncodedName(x)), sprintf("The <font color=\"%s\">Sample assay plot</font> panel shows assay values for a particular sample (i.e., column) of a <code>SummarizedExperiment</code> object or one of its subclasses. Here, each point corresponds to a row (usually a feature) of the <code>SummarizedExperiment</code> object, and the y-axis represents the assay values.", .getPanelColor(x))),
.addTourStep(x, .dataParamBoxOpen, "The <i>Data parameters</i> box shows the available parameters that can be tweaked in this plot.<br/><br/><strong>Action:</strong> click on this box to open up available options.")
)
rbind(
data.frame(element=collated[,1], intro=collated[,2], stringsAsFactors=FALSE),
callNextMethod()
)
})
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