#' Plot RDA or CCA object
#'
#' \code{plotRDA} and \code{plotCCA} create an RDA/CCA plot starting from the
#' output of \code{\link[mia:runCCA]{CCA and RDA}} functions, two common methods
#' for supervised ordination of microbiome data.
#'
#' @param x a
#' \code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-constructor]{TreeSummarizedExperiment}}
#' or a matrix of weights. The latter is returned as output from \code{\link[mia:runCCA]{getRDA}}.
#'
#' @param dimred \code{Character scalar} or \code{integer scalar}. Determines the reduced dimension to
#' plot. This is the output of \code{\link[mia:runCCA]{addRDA}} and resides in
#' \code{reducedDim(tse, dimred)}.
#'
#' @param add.ellipse One of \code{c(TRUE, FALSE, "fill", "colour", "color")},
#' indicating whether ellipses should be present, absent, filled or colored.
#' (default: \code{ellipse.fill = TRUE})
#'
#' @param ellipse.alpha \code{Numeric scalar}. Between 0 and 1. Adjusts the opacity of ellipses.
#' (Default: \code{0.2})
#'
#' @param ellipse.linewidth \code{Numeric scalar}. Specifies the size of ellipses.
#' (Default: \code{0.1})
#'
#' @param ellipse.linetype \code{Integer scalar}. Specifies the style of ellipses.
#' (Default: \code{1})
#'
#' @param confidence.level \code{Numeric scalar}. Between 0 and 1. Adjusts confidence level.
#' (Default: \code{0.95})
#'
#' @param add.vectors \code{Logical scalar}. Should vectors appear in the plot.
#' (Default: \code{TRUE})
#'
#' @param vec.size \code{Numeric scalar}. Specifies the size of vectors.
#' (Default: \code{0.5})
#'
#' @param vec.colour \code{Character scalar}. Specifies the colour of vectors.
#' (Default: \code{"black"})
#'
#' @param vec.color Alias for `vec.colour`.
#'
#' @param vec.linetype \code{Integer scalar}. Specifies the style of vector lines.
#' (Default: \code{1})
#'
#' @param arrow.size \code{Numeric scalar}. Specifies the size of arrows.
#' (defaults: \code{arrow.size = 0.25})
#'
#' @param label.size \code{Numeric scalar}. Specifies the size of text and labels.
#' (Default: \code{4})
#'
#' @param label.colour \code{Character scalar}. Specifies the colour of text and labels.
#' (Default: \code{"black"})
#'
#' @param label.color Alias for `label.colour`.
#'
#' @param sep.group \code{Character scalar}. Specifies the separator used in the labels.
#' (Default: \code{"\U2014"})
#'
#' @param repl.underscore \code{Character scalar}. Used to replace underscores in the labels.
#' (Default: \code{" "})
#'
#' @param vec.text \code{Logical scalar}. Should text instead of labels be used to label vectors.
#' (Default: \code{TRUE})
#'
#' @param repel.labels \code{Logical scalar}. Should labels be repelled.
#' (Default: \code{TRUE})
#'
#' @param parse.labels \code{Logical scalar}. Should labels be parsed.
#' (Default: \code{TRUE})
#'
#' @param add.significance \code{Logical scalar}. Should explained variance and p-value
#' appear in the labels. (Default: \code{TRUE})
#'
#' @param add.expl.var \code{Logical scalar}. Should explained variance appear on the
#' coordinate axes. (Default: \code{TRUE})
#'
#' @param ... additional parameters for plotting, inherited from
#' \code{\link[scater:plotReducedDim]{plotReducedDim}},
#' \code{\link[ggplot2:geom_label]{geom_label}} and
#' \code{\link[ggrepel:geom_label_repel]{geom_label_repel}}.
#'
#' @details
#' \code{plotRDA} and \code{plotCCA} create an RDA/CCA plot starting from the
#' output of \code{\link[mia:runCCA]{CCA and RDA}} functions, two common methods
#' for supervised ordination of microbiome data. Either a
#' \code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-constructor]{TreeSummarizedExperiment}}
#' or a matrix object is supported as input. When the input is a
#' TreeSummarizedExperiment, this should contain the output of addRDA
#' in the reducedDim slot and the argument \code{dimred} needs to be defined.
#' When the input is a matrix, this should be returned as output from
#' getRDA. However, the first method is recommended because it provides
#' the option to adjust aesthetics to the colData variables through the
#' arguments inherited from \code{\link[scater:plotReducedDim]{plotReducedDim}}.
#'
#' @return
#' A \code{ggplot2} object
#'
#' @name plotCCA
#'
#' @examples
#' # Load dataset
#' library(miaViz)
#' data("enterotype", package = "mia")
#' tse <- enterotype
#'
#' # Run RDA and store results into TreeSE
#' tse <- addRDA(tse,
#' formula = assay ~ ClinicalStatus + Gender + Age,
#' FUN = vegan::vegdist,
#' distance = "bray",
#' na.action = na.exclude)
#'
#' # Create RDA plot coloured by variable
#' plotRDA(tse, "RDA",
#' colour.by = "ClinicalStatus")
#'
#' # Create RDA plot with empty ellipses
#' plotRDA(tse, "RDA",
#' colour.by = "ClinicalStatus",
#' add.ellipse = "colour")
#'
#' # Create RDA plot with text encased in labels
#' plotRDA(tse, "RDA",
#' colour.by = "ClinicalStatus",
#' vec.text = FALSE)
#'
#' # Create RDA plot without repelling text
#' plotRDA(tse, "RDA",
#' colour.by = "ClinicalStatus",
#' repel.labels = FALSE)
#'
#' # Create RDA plot without vectors
#' plotRDA(tse, "RDA",
#' colour.by = "ClinicalStatus",
#' add.vectors = FALSE)
#'
#' # Calculate RDA as a separate object
#' rda_mat <- getRDA(tse,
#' formula = assay ~ ClinicalStatus + Gender + Age,
#' FUN = vegan::vegdist,
#' distance = "bray",
#' na.action = na.exclude)
#'
#' # Create RDA plot from RDA matrix
#' plotRDA(rda_mat)
NULL
#' @rdname plotCCA
#' @aliases plotRDA
#' @export
setGeneric("plotCCA", signature = c("x"),
function(x, ...) standardGeneric("plotCCA"))
#' @rdname plotCCA
#' @aliases plotRDA
#' @export
setMethod("plotCCA", signature = c(x = "SingleCellExperiment"),
function(x, dimred, ...){
# Reproduce plotRDA function
return(plotRDA(x, dimred, ...))
}
)
#' @rdname plotCCA
#' @aliases plotRDA
#' @export
setMethod("plotCCA", signature = c(x = "matrix"),
function(x, ...){
# Reproduce plotRDA function
return(plotRDA(x, ...))
}
)
#' @rdname plotCCA
#' @aliases plotCCA
#' @export
setGeneric("plotRDA", signature = c("x"),
function(x, ...) standardGeneric("plotRDA"))
#' @rdname plotCCA
#' @aliases plotCCA
#' @export
setMethod("plotRDA", signature = c(x = "SingleCellExperiment"),
function(x, dimred,
add.ellipse = TRUE, ellipse.alpha = 0.2, ellipse.linewidth = 0.1, ellipse.linetype = 1,
confidence.level = 0.95, vec.size = 0.5, vec.color = vec.colour, vec.colour = "black", vec.linetype = 1,
arrow.size = 0.25, label.color = label.colour, label.colour = "black", label.size = 4,
vec.text = TRUE, repel.labels = TRUE, sep.group = "\U2014", repl.underscore = " ",
add.significance = TRUE, add.expl.var = TRUE, add.vectors = TRUE, parse.labels = TRUE, ...){
###################### Input check ########################
if( !(add.ellipse %in% c(TRUE, FALSE, "fill", "color", "colour")) ){
stop("'add.ellipse' must be one of c(TRUE, FALSE, 'fill', 'color', 'colour').", call. = FALSE)
}
if ( !.is_a_bool(add.vectors) ){
stop("'add.vectors must be TRUE or FALSE.", call. = FALSE)
}
if ( !add.vectors ){
warning("'add.vectors' is FALSE, so other arguments for vectors and labels will be disregarded.", call. = FALSE)
}
if( !.is_a_bool(vec.text) ){
stop("'vec.text' must be TRUE or FALSE.", call. = FALSE)
}
if( !.is_a_bool(repel.labels) ){
stop("'repel.labels' must be TRUE or FALSE.", call. = FALSE)
}
if( !.is_a_bool(parse.labels) ){
stop("'parse.labels' must be TRUE or FALSE.", call. = FALSE)
}
if( !.is_a_bool(add.significance) ){
stop("'add.significance' must be TRUE or FALSE.", call. = FALSE)
}
if( parse.labels && !add.significance ){
parse.labels <- FALSE
warning("'parse.labels' was turned off because 'add.significance' is FALSE.", call. = FALSE)
}
if( !.is_a_bool(add.expl.var) ){
stop("'add.expl.var' must be TRUE or FALSE.", call. = FALSE)
}
if( !.are_whole_numbers(ellipse.linetype) ){
stop("'ellipse.linetype' must be a whole number.", call. = FALSE)
}
if( !.are_whole_numbers(ellipse.linetype) ){
stop("'vec.linetype' must be a whole number.", call. = FALSE)
}
if ( !(is.numeric(ellipse.alpha) && ellipse.alpha > 0 && ellipse.alpha < 1 ) ) {
stop("'ellipse.alpha' must be a number between 0 and 1.", call. = FALSE)
}
if ( !(is.numeric(ellipse.linewidth) && ellipse.linewidth > 0) ) {
stop("'ellipse.linewidth' must be a positive number.", call. = FALSE)
}
if( !(is.numeric(confidence.level) && confidence.level > 0 && confidence.level < 1) ) {
stop("'confidence.level' must be a number between 0 and 1.", call. = FALSE)
}
if ( !(is.numeric(vec.size) && vec.size > 0) ) {
stop("'vec.size' must be a positive number.", call. = FALSE)
}
if ( !(is.numeric(arrow.size) && arrow.size > 0) ) {
stop("'arrow.size' must be a positive number.", call. = FALSE)
}
if ( !(is.numeric(label.size) && label.size > 0) ) {
stop("'label.size' must be a positive number.", call. = FALSE)
}
if ( !.is_non_empty_string(vec.color) ) {
stop("'vec.color' must be a non-empty string specifying a colour", call. = FALSE)
}
if ( !.is_non_empty_string(label.color) ) {
stop("'label.color' must be a non-empty string specifying a colour", call. = FALSE)
}
if ( !.is_a_string(sep.group) ) {
stop("'sep.group' must be a string specifying a separator.", call. = FALSE)
}
if ( !.is_a_string(repl.underscore) ) {
stop("'repl.underscore' must be a string.", call. = FALSE)
}
###################### Input check end ####################
# Get data for plotting
plot_data <- .incorporate_rda_vis(
x, dimred, sep.group = sep.group, repl.underscore = repl.underscore,
add.significance = add.significance, add.expl.var = add.expl.var,
add.ellipse = add.ellipse, add.vectors = add.vectors, ...
)
# Create a plot
plot <- .rda_plotter(
plot_data, ellipse.alpha = ellipse.alpha, ellipse.linewidth = ellipse.linewidth,
ellipse.linetype = ellipse.linetype, confidence.level = confidence.level, vec.size = vec.size,
vec.color = vec.color, vec.colour = vec.colour, vec.linetype = vec.linetype,
arrow.size = arrow.size, label.color = label.color, label.colour = label.colour,
label.size = label.size, vec.text = vec.text, add.ellipse = add.ellipse,
repel.labels = repel.labels, parse.labels = parse.labels, ...
)
return(plot)
}
)
#' @rdname plotCCA
#' @aliases plotCCA
#' @export
setMethod("plotRDA", signature = c(x = "matrix"),
function(x, ...){
# Construct TreeSE from rda/cca object
x <- .rda2tse(x)
# Run plotRDA method for TreeSE
return(plotRDA(x, "RDA", ...))
}
)
################## HELP FUNCTIONS ##########################
# Construct TreeSE from rda/cca object to pass it to downstream functions
.rda2tse <- function(object) {
# Convert rda/cca object to TreeSE
object <- TreeSummarizedExperiment(
assays = matrix(ncol = nrow(object), dimnames = list(NULL, rownames(object))),
reducedDims = list(RDA = object)
)
return(object)
}
# Get data for plotting
#' @importFrom scater plotReducedDim retrieveCellInfo
#' @importFrom SingleCellExperiment reducedDim reducedDimNames
.incorporate_rda_vis <- function(
tse, dimred, ncomponents = 2,
colour_by = color_by, color_by = colour.by,
colour.by = color.by, color.by = NULL,
add.significance = TRUE, add.expl.var = TRUE, add.ellipse = TRUE,
add.vectors = TRUE, vec.lab = NULL,
expl.var = expl_var, expl_var = NULL,
sep.group = "\U2012", repl.underscore = " ", ...){
# Check dimred
if( !dimred %in% reducedDimNames(tse) ){
stop("'dimred' must specify reducedDim.", call. = FALSE)
}
# Get reducedDim
reduced_dim <- reducedDim(tse, dimred)
# Check that there are at least 2 coordinates
if( ncol(reduced_dim) < 2 ){
stop("reducedDim specified by 'dimred' must have at least 2 columns.", call. = FALSE)
}
# Only 2 dimensions are supported currently
ncomponents <- 2
# If specified, get explained variance
if( add.expl.var ){
# Check if data is available
ind <- names(attributes(reduced_dim)) %in% c("rda", "cca")
# If it can be found
if( any(ind) ){
# Add explained variance
rda <- attributes(reduced_dim)[ind][[1]]
expl_var <- summary(rda)$concont$importance[2, ]*100
} else{
# If it cannot be found, give warning
warning(
paste("RDA/CCA object was not found. Please compute",
"RDA/CCA by using addCCA or getCCA."), call. = FALSE)
}
}
# Get scatter plot with plotReducedDim --> keep theme similar between ordination methods
plot <- plotReducedDim(
tse, dimred = dimred, ncomponents = ncomponents, colour_by = colour_by,
percentVar = expl_var, ...)
# Get data for ellipse
ellipse_data <- NULL
if( add.ellipse != FALSE && !is.null(colour_by) ){
ellipse_data <- reduced_dim
ellipse_data <- as.data.frame(ellipse_data)
ellipse_data[[colour_by]] <- retrieveCellInfo(tse, colour_by)[["value"]]
attributes(ellipse_data)$colour_by <- colour_by
}
# Get data for vectors
vector_data <- NULL
if( add.vectors ){
# Check if data is available
ind <- names(attributes(reduced_dim)) %in% c("rda", "cca")
# If it can be found
if( any(ind) ){
# Get biplot from cca object
rda <- attributes(reduced_dim)[ind][[1]]
vector_data <- rda$CCA$biplot
vector_data <- as.data.frame(vector_data)
vector_data[["group"]] <- rownames(vector_data)
} else{
# If it cannot be found, give warning
warning(
paste("RDA/CCA object was not found. Please compute RDA/CCA",
"by using addCCA or getCCA."), call. = FALSE)
}
}
# Get variable names from sample metadata
coldata <- colData(tse)
variable_names <- colnames(coldata)
# Check if variable names can be found metadata
all_var_found <- FALSE
if( !is.null(vector_data) && length(variable_names) > 0 ){
all_var_found <- all(colSums(
sapply(rownames(vector_data), function(x)
sapply(variable_names, function(y) grepl(y, x)) )) == 1)
}
# Get vector labels
if( !is.null(vector_data) ){
# Get those names that are present in data
# If user has not provided vector labels
if( is.null(vec.lab) ){
vector_label <- rownames(vector_data)
# Make labels more tidy
if( all_var_found ){
vector_label <- .tidy_vector_labels(
vector_label, coldata,
sep.group = sep.group,
repl.underscore = repl.underscore
)
}
# Add to df
vector_data$vector_label <- vector_label
} else{
# Check that user-provided labels are correct length
if( length(vec.lab) != nrow(vector_data) ){
stop("Number of labels in 'vec_lab' do not match with number of vectors.",
call. = FALSE)
}
# If they are, add labels to data
vector_data$vector_label <- vec.lab
}
}
# Get significance data
signif_data <- NULL
if( add.significance && !is.null(vector_data) && all_var_found ){
# Check if data is available
ind <- names(attributes(reduced_dim)) %in% c("significance")
# If it can be found
if( any(ind) ){
# Get biplot from cca object
signif_data <- attributes(reduced_dim)[ind][[1]]
signif_data <- signif_data[["permanova"]]
signif_data <- as.data.frame(signif_data)
# Add significance to vector labels
# Get vector labels
vector_label <- vector_data[["vector_label"]]
# Add significance to vector labels
vector_label <- .add_signif_to_vector_labels(
vector_label, variable_names, signif_data, repl.underscore)
vector_data[["vector_label"]] <- vector_label
} else{
# If it cannot be found, give warning
warning(
paste("Significance data was not found. please compute",
"CCA/RDA by using addCCA or getCCA."), call. = FALSE)
}
}
# Create a list to return
result <- list(
plot = plot,
ellipse_data = ellipse_data,
vector_data = vector_data
)
return(result)
}
# Make vector labels more tidy, i.e, separate variable and group names.
# Replace also underscores with space
.tidy_vector_labels <- function(
vector_label, coldata, sep.group = "\U2014", repl.underscore = " ", ...){
# Get variable names from sample metadata
var_names <- colnames(coldata)
# Loop through vector labels
vector_label <- sapply(vector_label, FUN = function(name){
# Get the real variable name from sample metadata
var_name <- var_names[ sapply(var_names, function(x) grepl(x, name)) ]
# If the vector label includes also group name
if( !name %in% var_names ){
# Get the group name
group_name <- unique( coldata[[var_name]] )[
which( paste0(var_name, unique( coldata[[var_name]] )) == name ) ]
# Modify vector so that group is separated from variable name
new_name <- paste0(var_name, " ", sep.group, " ", group_name)
} else{
new_name <- name
}
# Replace underscores with space
new_name <- gsub("_", repl.underscore, new_name)
return(new_name)
})
return(vector_label)
}
# This function adds significance info to vector labels
.add_signif_to_vector_labels <- function(
vector_label, var_names, signif_data, repl.underscore = " ", ...){
# Replace underscores from significance data and variable names to match labels
rownames(signif_data) <- sapply(rownames(signif_data), function(x) gsub("_", repl.underscore, x))
var_names <- sapply(var_names, function(x) gsub("_", repl.underscore, x))
# Loop through vector labels
vector_label <- sapply(vector_label, FUN = function(name){
# Get the real variable name from sample metadata
var_name <- var_names[ sapply(var_names, function(x) grepl(x, name)) ]
# Add percentage how much this variable explains, and p-value
new_name <- expr(
paste(!!name, " (",
!!format(
round( signif_data[var_name, "Explained variance"]*100, 1),
nsmall = 1), "%, ", italic("P"), " = ",
!!gsub("0\\.","\\.", format(
round( signif_data[var_name, "Pr(>F)"], 3),
nsmall = 3)), ")"))
return(new_name)
})
return(vector_label)
}
# Plot based on the data
#' @importFrom ggrepel geom_text_repel geom_label_repel
.rda_plotter <- function(
plot_data, ellipse.alpha = 0.2, ellipse.linewidth = 0.1, ellipse.linetype = 1, confidence.level = 0.95,
vec.size = 0.5, vec.color = vec.colour, vec.colour = "black",
vec.linetype = 1, arrow.size = 0.25, min.segment.length = 5,
label.color = label.colour, label.colour = "black", label.size = 4,
parse.labels = TRUE, vec.text = TRUE, repel.labels = TRUE, add.ellipse = TRUE,
position = NULL, nudge_x = NULL, nudge_y = NULL, direction = "both",
max.overlaps = 10, check_overlap = FALSE, ...){
# Get the scatter plot
plot <- plot_data[["plot"]]
# Add ellipse
if( !is.null(plot_data$ellipse_data) ){
# Get data and variabe names
data <- plot_data$ellipse_data
xvar <- colnames(data)[[1]]
yvar <- colnames(data)[[2]]
colour_var <- attributes(plot_data$ellipse_data)$colour_by
# Add ellipses to plot (fill or colour the edge)
if( add.ellipse %in% c(TRUE, "fill") ){
plot <- plot +
stat_ellipse(data = data,
aes(x = .data[[xvar]], y = .data[[yvar]],
color = .data[[colour_var]], fill = after_scale(color)),
geom = "polygon", alpha = ellipse.alpha,
linewidth = ellipse.linewidth, linetype = ellipse.linetype, level = confidence.level)
} else if ( add.ellipse %in% c("color", "colour") ){
plot <- plot +
stat_ellipse(data = data,
aes(x = .data[[xvar]], y = .data[[yvar]], color = .data[[colour_var]]),
geom = "polygon", alpha = 0, linetype = ellipse.linetype, level = confidence.level)
}
}
# Add vectors
if( !is.null(plot_data$vector_data) ){
# Get data and variabe names
data <- plot_data$vector_data
xvar <- colnames(data)[[1]]
yvar <- colnames(data)[[2]]
# Add vectors
plot <- plot +
geom_segment(data = data,
aes(x = 0, y = 0, xend = .data[[xvar]], yend = .data[[yvar]],
group = .data[["group"]]),
arrow = arrow(length = unit(arrow.size, "cm")),
color = vec.color, linetype = vec.linetype, size = vec.size)
# Add vector labels (text or label)
# Make list of arguments for geom_text/geom_label
label_args <- list(
data = data,
mapping = aes(x = .data[[xvar]], y = .data[[yvar]]),
label = data[["vector_label"]], parse = parse.labels,
color = label.color, size = label.size, stat = "identity",
nudge_x = nudge_x, nudge_y = nudge_y, show.legend = NA,
na.rm = FALSE, inherit.aes = TRUE
)
# Repel text
if( repel.labels ){
# Add arguments for geom_text_repel/geom_label_repel to list
label_args <- c(
label_args, min.segment.length = min.segment.length,
box.padding = 0.25, point.padding = 1e-06, force = 1, force_pull = 1,
max.time = 0.5, max.iter = 10000, max.overlaps = max.overlaps,
direction = direction, seed = NA, verbose = FALSE
)
# repelled text
if( vec.text ){
plot <- plot + do.call("geom_text_repel", label_args)
# repelled labels
} else{
plot <- plot + do.call("geom_label_repel", label_args)
}
# Do not repel text
} else{
# not repelled text
if( vec.text ){
label_args <- c(label_args, check_overlap = check_overlap)
plot <- plot + do.call("geom_text", label_args)
# not repelled labels
} else{
plot <- plot + do.call("geom_label", label_args)
}
}
}
return(plot)
}
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