Nothing
R/do_FeaturePlot.R
In SCpubr: Generate Publication Ready Visualizations of Single Cell Transcriptomics Data
Defines functions
do_FeaturePlot
Documented in
do_FeaturePlot
#' Wrapper for \link[Seurat]{FeaturePlot}.
#'
#' @inheritParams doc_function
#' @param idents.keep \strong{\code{\link[base]{character}}} | Vector of identities to plot. The gradient scale will also be subset to only the values of such identities.
#' @param individual.titles,individual.subtitles,individual.captions \strong{\code{\link[base]{character}}} | Titles or subtitles. for each feature if needed. Either NULL or a vector of equal length of features.
#' @param order \strong{\code{\link[base]{logical}}} | Whether to order the cells based on expression.
#' @param group.by \strong{\code{\link[base]{character}}} | Metadata variable based on which cells are grouped. This will effectively introduce a big dot in the center of each cluster, colored using a categorical color scale or with the values provided by the user in \strong{\code{group.by.colors.use}}. It will also displays a legend.
#' @param group.by.legend \strong{\code{\link[base]{character}}} | Title for the legend when \strong{\code{group.by}} is used. Use \strong{\code{NA}} to disable it and \strong{\code{NULL}} to use the default column title provided in \strong{\code{group.by}}.
#' @param group.by.dot.size \strong{\code{\link[base]{numeric}}} | Size of the dots placed in the middle of the groups.
#' @param group.by.cell_borders \strong{\code{\link[base]{logical}}} | Plots another border around the cells displaying the same color code of the dots displayed with \strong{\code{group.by}}. Legend is shown always with alpha = 1 regardless of the alpha settings.
#' @param group.by.colors.use \strong{\code{\link[base]{character}}} | Colors to use for the group dots.
#' @param group.by.show.dots \strong{\code{\link[base]{logical}}} | Controls whether to place in the middle of the groups.
#' @param group.by.cell_borders.alpha \strong{\code{\link[base]{numeric}}} | Controls the transparency of the new borders drawn by \strong{\code{group.by.cell_borders}}.
#' @param symmetry.type \strong{\code{\link[base]{character}}} | Type of symmetry to be enforced. One of:
#' \itemize{
#' \item \emph{\code{absolute}}: The highest absolute value will be taken into a account to generate the color scale. Works after \strong{\code{min.cutoff}} and \strong{\code{max.cutoff}}.
#' \item \emph{\code{centered}}: Centers the scale around the provided value in \strong{\code{symmetry.center}}. Works after \strong{\code{min.cutoff}} and \strong{\code{max.cutoff}}.
#' }
#' @param symmetry.center \strong{\code{\link[base]{numeric}}} | Value upon which the scale will be centered.
#' @return A ggplot2 object containing a Feature Plot.
#' @export
#'
#' @example /man/examples/examples_do_FeaturePlot.R
do_FeaturePlot <- function(sample,
features,
assay = NULL,
reduction = NULL,
slot = NULL,
order = FALSE,
group.by = NULL,
group.by.colors.use = NULL,
group.by.legend = NULL,
group.by.show.dots = TRUE,
group.by.dot.size = 8,
group.by.cell_borders = FALSE,
group.by.cell_borders.alpha = 0.1,
split.by = NULL,
idents.keep = NULL,
cells.highlight = NULL,
idents.highlight = NULL,
dims = c(1, 2),
enforce_symmetry = FALSE,
symmetry.type = "absolute",
symmetry.center = NA,
pt.size = 1,
font.size = 14,
font.type = "sans",
legend.title = NULL,
legend.type = "colorbar",
legend.position = "bottom",
legend.framewidth = 0.5,
legend.tickwidth = 0.5,
legend.length = 20,
legend.width = 1,
legend.framecolor = "grey50",
legend.tickcolor = "white",
legend.ncol = NULL,
legend.nrow = NULL,
legend.byrow = FALSE,
plot.title = NULL,
plot.subtitle = NULL,
plot.caption = NULL,
individual.titles = NULL,
individual.subtitles = NULL,
individual.captions = NULL,
ncol = NULL,
use_viridis = FALSE,
viridis.palette = "G",
viridis.direction = 1,
raster = FALSE,
raster.dpi = 1024,
plot_cell_borders = TRUE,
border.size = 2,
border.color = "black",
border.density = 1,
na.value = "grey75",
verbose = TRUE,
plot.axes = FALSE,
min.cutoff = rep(NA, length(features)),
max.cutoff = rep(NA, length(features)),
plot_density_contour = FALSE,
contour.position = "bottom",
contour.color = "grey90",
contour.lineend = "butt",
contour.linejoin = "round",
contour_expand_axes = 0.25,
label = FALSE,
label.color = "black",
label.size = 4,
number.breaks = 5,
diverging.palette = "RdBu",
diverging.direction = -1,
sequential.palette = "YlGnBu",
sequential.direction = 1,
plot.title.face = "bold",
plot.subtitle.face = "plain",
plot.caption.face = "italic",
axis.title.face = "bold",
axis.text.face = "plain",
legend.title.face = "bold",
legend.text.face = "plain"){
# Add lengthy error messages.
withr::local_options(.new = list("warning.length" = 8170))
check_suggests(function_name = "do_FeaturePlot")
# Check if the sample provided is a Seurat object.
check_Seurat(sample = sample)
# Check the assay.
out <- check_and_set_assay(sample = sample, assay = assay)
sample <- out[["sample"]]
assay <- out[["assay"]]
sample <- check_Assay5(sample, assay = assay)
# Check the reduction.
reduction <- check_and_set_reduction(sample = sample, reduction = reduction)
# Check the dimensions.
dims <- check_and_set_dimensions(sample = sample, reduction = reduction, dims = dims)
# Check logical parameters.
logical_list <- list("verbose" = verbose,
"raster" = raster,
"plot_cell_borders" = plot_cell_borders,
"order" = order,
"enforce_symmetry" = enforce_symmetry,
"plot.axes" = plot.axes,
"plot_density_contour" = plot_density_contour,
"label" = label,
"legend.byrow" = legend.byrow,
"group.by.cell_borders" = group.by.cell_borders,
"group.by.show.dots" = group.by.show.dots,
"use_viridis" = use_viridis)
check_type(parameters = logical_list, required_type = "logical", test_function = is.logical)
# Check numeric parameters.
numeric_list <- list("pt.size" = pt.size,
"ncol" = ncol,
"font.size" = font.size,
"raster.dpi" = raster.dpi,
"legend.framewidth" = legend.framewidth,
"legend.tickwidth" = legend.tickwidth,
"legend.length" = legend.length,
"legend.width" = legend.width,
"border.size" = border.size,
"viridis.direction" = viridis.direction,
"min.cutoff" = min.cutoff,
"max.cutoff" = max.cutoff,
"contour_expand_axes" = contour_expand_axes,
"label.size" = label.size,
"number.breaks" = number.breaks,
"border.density" = border.density,
"legend.nrow" = legend.nrow,
"legend.ncol" = legend.ncol,
"group.by.dot.size" = group.by.dot.size,
"group.by.cell_borders.alpha" = group.by.cell_borders.alpha,
"sequential.direction" = sequential.direction,
"diverging.direction" = diverging.direction,
"symmetry.center" = symmetry.center)
check_type(parameters = numeric_list, required_type = "numeric", test_function = is.numeric)
# Check character parameters.
# Workaround for features.
if (is.list(features)){
warning(paste0(add_warning(), crayon_body("Features provided as a list. Unlisting the list. Please use a character vector next time."), call. = FALSE))
features <- unique(unlist(features))
}
character_list <- list("legend.position" = legend.position,
"features" = features,
"cells.highlight" = cells.highlight,
"idents.highlight" = idents.highlight,
"slot" = slot,
"group.by" = group.by,
"split.by" = split.by,
"plot.title" = plot.title,
"plot.subtitle" = plot.subtitle,
"plot.caption" = plot.caption,
"idents.keep" = idents.keep,
"viridis.palette" = viridis.palette,
"individual.titles" = individual.titles,
"individual.subtitles" = individual.subtitles,
"individual.captions" = individual.captions,
"legend.framecolor" = legend.framecolor,
"legend.tickcolor" = legend.tickcolor,
"legend.type" = legend.type,
"font.type" = font.type,
"border.color" = border.color,
"legend.title" = legend.title,
"na.value" = na.value,
"contour.position" = contour.position,
"contour.color" = contour.color,
"contour.lineend" = contour.lineend,
"contour.linejoin" = contour.linejoin,
"label.color" = label.color,
"group.by.colors.use" = group.by.colors.use,
"diverging.palette" = diverging.palette,
"sequential.palette" = sequential.palette,
"plot.title.face" = plot.title.face,
"plot.subtitle.face" = plot.subtitle.face,
"plot.caption.face" = plot.caption.face,
"axis.title.face" = axis.title.face,
"axis.text.face" = axis.text.face,
"legend.title.face" = legend.title.face,
"legend.text.face" = legend.text.face,
"symmetry.type" = symmetry.type)
check_type(parameters = character_list, required_type = "character", test_function = is.character)
# Check slot.
slot <- check_and_set_slot(slot = slot)
# Check split.by is on metadata.
if (!(is.null(split.by))){check_feature(sample = sample, features = split.by, enforce_check = "metadata", enforce_parameter = "split.by")}
# Check individual titles.
if (length(features) > 1 & !is.null(individual.titles)){
assertthat::assert_that(length(features) == length(individual.titles),
msg = paste0(add_cross(), crayon_body("The total number of "),
crayon_key("individual titles"),
crayon_body(" does not match the number of "),
crayon_key("features"),
crayon_body(" provided.")))
}
if (length(features) > 1 & !is.null(individual.subtitles)){
assertthat::assert_that(length(features) == length(individual.subtitles),
msg = paste0(add_cross(), crayon_body("The total number of "),
crayon_key("individual subtitles"),
crayon_body(" does not match the number of "),
crayon_key("features"),
crayon_body(" provided.")))
}
if (length(features) > 1 & !is.null(individual.captions)){
assertthat::assert_that(length(features) == length(individual.captions),
msg = paste0(add_cross(), crayon_body("The total number of "),
crayon_key("individual captions"),
crayon_body(" does not match the number of "),
crayon_key("features"),
crayon_body(" provided.")))
}
## Check that the contour_expand_axes is between 0 and 1.
assertthat::assert_that(contour_expand_axes <= 1,
msg = paste0(add_cross(), crayon_body("Please provide a value to "),
crayon_key("countour_expand_axes"),
crayon_body(" lower or equal than "),
crayon_key("1"),
crayon_body(".")))
assertthat::assert_that(contour_expand_axes >= 0,
msg = paste0(add_cross(), crayon_body("Please provide a value to "),
crayon_key("countour_expand_axes"),
crayon_body(" higher or equal than "),
crayon_key("0"),
crayon_body(".")))
check_colors(border.color, parameter_name = "border.color")
check_colors(na.value, parameter_name = "na.value")
check_colors(legend.framecolor, parameter_name = "legend.framecolor")
check_colors(legend.tickcolor, parameter_name = "legend.tickcolor")
check_colors(contour.color, parameter_name = "contour.color")
check_colors(label.color, parameter_name = "label.color")
if (!is.null(group.by)){
if (!is.null(group.by.colors.use)){
check_colors(group.by.colors.use, parameter_name = "group.by.colors.use")
check_consistency_colors_and_names(sample = sample,
colors = group.by.colors.use,
grouping_variable = group.by)
} else {
data.use <- sample@meta.data[, group.by, drop = FALSE]
# If the variable is a factor, use the levels as order. If not, order the values alphabetically.
names.use <- if (is.factor(data.use[, 1])){levels(data.use[, 1])} else {sort(unique(data.use[, 1]))}
# Generate the color scale to be used based on the unique values of group.by.
group.by.colors.use <- generate_color_scale(names.use)
}
}
check_parameters(parameter = font.type, parameter_name = "font.type")
check_parameters(parameter = legend.type, parameter_name = "legend.type")
check_parameters(parameter = legend.position, parameter_name = "legend.position")
check_parameters(parameter = viridis.palette, parameter_name = "viridis.palette")
check_parameters(parameter = contour.lineend, parameter_name = "contour.lineend")
check_parameters(parameter = contour.linejoin, parameter_name = "contour.linejoin")
check_parameters(parameter = contour.position, parameter_name = "contour.position")
check_parameters(parameter = number.breaks, parameter_name = "number.breaks")
check_parameters(parameter = border.density, parameter_name = "border.density")
check_parameters(parameter = diverging.palette, parameter_name = "diverging.palette")
check_parameters(parameter = sequential.palette, parameter_name = "sequential.palette")
check_parameters(plot.title.face, parameter_name = "plot.title.face")
check_parameters(plot.subtitle.face, parameter_name = "plot.subtitle.face")
check_parameters(plot.caption.face, parameter_name = "plot.caption.face")
check_parameters(axis.title.face, parameter_name = "axis.title.face")
check_parameters(axis.text.face, parameter_name = "axis.text.face")
check_parameters(legend.title.face, parameter_name = "legend.title.face")
check_parameters(legend.text.face, parameter_name = "legend.text.face")
check_parameters(viridis.direction, parameter_name = "viridis.direction")
check_parameters(sequential.direction, parameter_name = "sequential.direction")
check_parameters(diverging.direction, parameter_name = "diverging.direction")
check_parameters(parameter = symmetry.type, parameter_name = "symmetry.type")
if (length(min.cutoff) != length(features)){
warning(paste0(add_warning(), crayon_body("Please provide as many values to "),
crayon_key("min.cutoff"),
crayon_body(" as "),
crayon_key("features"),
crayon_body(" provided. The values will be used in order and, when outside of the range, no cutoffs will be applied.")), call. = FALSE)
}
if (length(max.cutoff) != length(features)){
warning(paste0(add_warning(), crayon_body("Please provide as many values to "),
crayon_key("max.cutoff"),
crayon_body(" as "),
crayon_key("features"),
crayon_body(" provided. The values will be used in order and, when outside of the range, no cutoffs will be applied.")), call. = FALSE)
}
# Generate the continuous color palette.
if (isTRUE(enforce_symmetry)){
colors.gradient <- compute_continuous_palette(name = diverging.palette,
use_viridis = FALSE,
direction = diverging.direction,
enforce_symmetry = enforce_symmetry)
} else {
colors.gradient <- compute_continuous_palette(name = ifelse(isTRUE(use_viridis), viridis.palette, sequential.palette),
use_viridis = use_viridis,
direction = ifelse(isTRUE(use_viridis), viridis.direction, sequential.direction),
enforce_symmetry = enforce_symmetry)
}
# Generate base layer.
if (isTRUE(plot_cell_borders)){
out <- compute_umap_layer(sample = sample,
labels = colnames(sample@reductions[[reduction]][[]])[dims],
pt.size = pt.size,
dot.size = group.by.dot.size,
border.density = border.density,
border.size = border.size,
border.color = border.color,
raster = raster,
raster.dpi = raster.dpi,
reduction = reduction,
group.by = group.by,
split.by = split.by,
na.value = na.value,
alpha = group.by.cell_borders.alpha,
n = 100)
base_layer <- out$base_layer
na_layer <- out$na_layer
if (!is.null(group.by)){
center_layers <- out$center_layers
center_layer_1 <- center_layers$center_layer_1
center_layer_2 <- center_layers$center_layer_2
color_layer <- out$color_layer
}
}
# CONDITION: Regular FeaturePlot, under default parameters.
default_parameters <- is.null(split.by) & is.null(cells.highlight) & is.null(idents.highlight)
# If only default parameters are used.
if (default_parameters){
# Check if the feature is actually in the object.
features <- check_feature(sample = sample,
features = features,
permissive = TRUE)
# Remove duplicated features.
features <- remove_duplicated_features(features = features)
if (utils::packageVersion("Seurat") >= "4.1.0"){
p <- Seurat::FeaturePlot(sample,
features,
slot = slot,
reduction = reduction,
order = order,
dims = dims,
pt.size = pt.size,
ncol = ncol,
raster = raster,
raster.dpi = c(raster.dpi, raster.dpi),
min.cutoff = min.cutoff,
max.cutoff = max.cutoff,
label = label,
label.size = label.size,
label.color = label.color)
} else { # nocov start
p <- Seurat::FeaturePlot(sample,
features,
slot = slot,
reduction = reduction,
order = order,
dims = dims,
pt.size = pt.size,
ncol = ncol,
raster = raster,
min.cutoff = min.cutoff,
max.cutoff = max.cutoff,
label = label,
label.size = label.size,
label.color = label.color)
} # nocov end
p$layers[[length(p$layers)]]$aes_params$fontface <- "bold"
p <- p &
# Remove Seurat::FeaturePlot() default plot title.
ggplot2::ggtitle("")
# Add color scales.
num_plots <- length(features)
for (counter in seq(1, num_plots)){
if (num_plots == 1){
scale.setup <- compute_scales(sample = sample,
feature = features,
assay = assay,
reduction = NULL,
slot = slot,
number.breaks = number.breaks,
min.cutoff = min.cutoff,
max.cutoff = max.cutoff,
flavor = "Seurat",
enforce_symmetry = enforce_symmetry,
center_on_value = if(symmetry.type == "absolute"){FALSE} else {TRUE},
value_center = symmetry.center)
p <- add_scale(p = p,
function_use = ggplot2::scale_color_gradientn(colors = colors.gradient,
na.value = na.value,
name = legend.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits),
scale = "color")
} else if (num_plots > 1){
feature.use <- features[counter]
scale.setup <- compute_scales(sample = sample,
feature = feature.use,
assay = assay,
reduction = NULL,
slot = slot,
number.breaks = number.breaks,
min.cutoff = min.cutoff[counter],
max.cutoff = max.cutoff[counter],
flavor = "Seurat",
enforce_symmetry = enforce_symmetry,
center_on_value = if(symmetry.type == "absolute"){FALSE} else {TRUE},
value_center = symmetry.center)
p[[counter]] <- add_scale(p = p[[counter]],
function_use = ggplot2::scale_color_gradientn(colors = colors.gradient,
na.value = na.value,
name = legend.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits),
scale = "color")
}
}
# Special patches for diffusion maps: Adding "DC" labels to the axis.
if (stringr::str_starts(reduction, "diff|DIFF")){
p <- p &
ggplot2::xlab(paste0("DC_", dims[1])) &
ggplot2::ylab(paste0("DC_", dims[2]))
}
# Add cell borders.
if (isTRUE(plot_cell_borders)){
counter <- 0
for (feature in features){
counter <- counter + 1
p[[counter]]$layers <- append(base_layer, p[[counter]]$layers)
if (!(is.null(group.by))){
if (isTRUE(group.by.show.dots)){
p[[counter]]$layers <- append(p[[counter]]$layers, c(center_layer_2, center_layer_1))
}
if (isTRUE(group.by.cell_borders)){
p[[counter]]$layers <- append(color_layer, p[[counter]]$layers)
}
if (is.null(group.by.legend)){
legend.name <- group.by
} else {
if (is.na(group.by.legend)){
legend.name <- ""
} else {
legend.name <- group.by.legend
}
}
p[[counter]] <- p[[counter]] +
ggplot2::scale_fill_manual(values = group.by.colors.use,
name = legend.name) +
ggplot2::guides(fill = ggplot2::guide_legend(title = legend.name,
title.position = "top",
title.hjust = 0.5,
ncol = legend.ncol,
nrow = legend.nrow,
byrow = legend.byrow,
override.aes = list(alpha = 1)))
}
}
}
if (isTRUE(plot_density_contour)){
counter <- 0
for (feature in features){
counter <- counter + 1
data <- ggplot2::ggplot_build(p[[counter]])
density_layer <- ggplot2::stat_density_2d(data = data$data[[1]],
mapping = ggplot2::aes(x = .data$x,
y = .data$y),
color = contour.color,
lineend = contour.lineend,
linejoin = contour.linejoin)
if (contour.position == "bottom"){
p[[counter]]$layers <- append(density_layer, p[[counter]]$layers)
} else if (contour.position == "top"){
p[[counter]]$layers <- append(p[[counter]]$layers, density_layer)
}
min_x <- min(data$data[[1]]$x) * (1 + contour_expand_axes)
max_x <- max(data$data[[1]]$x) * (1 + contour_expand_axes)
min_y <- min(data$data[[1]]$y) * (1 + contour_expand_axes)
max_y <- max(data$data[[1]]$y) * (1 + contour_expand_axes)
# Expand axes limits to allocate the new contours.
suppressMessages({
p[[counter]] <- p[[counter]] +
ggplot2::xlim(c(min_x, max_x)) +
ggplot2::ylim(c(min_y, max_y))
})
}
}
# Modified FeaturePlot including only a subset of cells.
} else {
# Check if the feature is actually in the object.
output_list <- check_feature(sample = sample,
features = features,
dump_reduction_names = TRUE,
permissive = TRUE)
features <- output_list[["features"]]
dim_colnames <- output_list[["reduction_names"]]
# Remove duplicated features.
features <- remove_duplicated_features(features = features)
# Get the subset of wanted cells according to the combination of idents.highlight and cells.highlight parameters.
if (is.null(idents.highlight) & !(is.null(cells.highlight))){
# Only if cells.highlight parameters is used.
cells.use <- cells.highlight
} else if (!(is.null(idents.highlight)) & is.null(cells.highlight)){
# Only if idents.highlight parameter is used.
# Check if the provided identities are part of the active identities in the object.
check_identity(sample = sample, identities = idents.highlight)
# Get the names of the cells to use.
cells.use <- names(Seurat::Idents(sample)[Seurat::Idents(sample) %in% idents.highlight])
} else if (!(is.null(idents.highlight)) & !(is.null(cells.highlight))){
# Check if the provided identities are part of the active identities in the object.
check_identity(sample = sample, identities = idents.highlight)
# Both idents.highlight and cells.highlight are used.
cells.1 <- cells.highlight
cells.2 <- names(Seurat::Idents(sample)[Seurat::Idents(sample) %in% idents.highlight])
# Get the names of the cells to use.
cells.use <- unique(c(cells.1, cells.2))
# If split.by is used.
} else if (!(is.null(split.by))){
# No identities selected by the user.
if (is.null(idents.keep)){
cells.use <- colnames(sample)
# Identitites selected by the user.
} else {
# Check if the identitites are duplicated. If so, remove them.
if (sum(duplicated(idents.keep)) != 0){
message(paste0(add_info(), crayon_body("Found and removed duplicated values in idents.keep.")))
idents.keep <- idents.keep[!duplicated(idents.keep)]
}
# Get the names of the cells to plot.
cells.use <- names(Seurat::Idents(sample)[Seurat::Idents(sample) %in% idents.keep])
}
}
# Plots are generated independently if more than one feature is provided.
list.plots <- list()
# Counter depicting the feature used. Will increase each feature used.
count_iteration <- 0
cutoff.counter <- 0
# Iterate over the features.
for (feature in features){
cutoff.counter <- cutoff.counter + 1
min.cutoff.use <- min.cutoff[cutoff.counter]
max.cutoff.use <- max.cutoff[cutoff.counter]
# A "dummy" metadata column is generated using the values of the selected feature.
## Based on whether the feature is in the metadata of the object.
if (feature %in% colnames(sample@meta.data)) {
sample$dummy <- sample@meta.data[, feature]
## Or is a gene in the object.
} else if (feature %in% rownames(sample)){
suppressWarnings({
sample$dummy <- SeuratObject::GetAssayData(object = sample, slot = slot, assay = assay)[feature, ]
})
## Or is a dimensional reduction component.
} else if (feature %in% dim_colnames){
# Iterate over each dimensional reduction in the object.
for(red in Seurat::Reductions(object = sample)){
# If the feature to plot is one of the dimensional reduction components.
if (feature %in% colnames(sample@reductions[[red]][[]])){
red.feature <- red
sample$dummy <- sample@reductions[[red.feature]][[]][, feature]
}
}
}
# Assign NAs to the values corresponding to the cells not selected.
sample$dummy[!(names(sample$dummy) %in% cells.use)] <- NA
# If split.by is not used.
if (is.null(split.by)){
feature.use <- "dummy"
sample.use <- sample[, cells.use]
if (isTRUE(plot_cell_borders)){
out <- compute_umap_layer(sample = sample.use,
labels = colnames(sample.use@reductions[[reduction]][[]])[dims],
pt.size = pt.size,
border.density = border.density,
border.size = border.size,
border.color = border.color,
raster = raster,
raster.dpi = raster.dpi,
reduction = reduction,
group.by = group.by,
split.by = split.by,
n = 100)
base_layer_subset <- out$base_layer
}
if (utils::packageVersion("Seurat") >= "4.1.0"){
p.loop <- Seurat::FeaturePlot(sample.use,
feature.use,
reduction = reduction,
slot = slot,
order = order,
dims = dims,
pt.size = pt.size,
raster = raster,
raster.dpi = c(raster.dpi, raster.dpi),
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
label = label,
label.size = label.size,
label.color = label.color)
} else { # nocov start
p.loop <- Seurat::FeaturePlot(sample.use,
feature.use,
reduction = reduction,
slot = slot,
order = order,
dims = dims,
pt.size = pt.size,
raster = raster,
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
label = label,
label.size = label.size,
label.color = label.color)
} # nocov end
p.loop$layers[[length(p.loop$layers)]]$aes_params$fontface <- "bold"
# Add scale.
scale.setup <- compute_scales(sample = sample,
feature = feature,
assay = assay,
reduction = NULL,
slot = slot,
number.breaks = number.breaks,
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
flavor = "Seurat",
enforce_symmetry = enforce_symmetry,
center_on_value = if(symmetry.type == "absolute"){FALSE} else {TRUE},
value_center = symmetry.center)
p.loop <- add_scale(p = p.loop,
function_use = ggplot2::scale_color_gradientn(colors = colors.gradient,
na.value = na.value,
name = legend.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits),
scale = "color")
p.loop <- p.loop +
ggplot2::ggtitle("")
# Add cell borders.
if (isTRUE(plot_cell_borders)){
p.loop$layers <- append(base_layer_subset, p.loop$layers)
p.loop$layers <- append(na_layer, p.loop$layers)
p.loop$layers <- append(base_layer, p.loop$layers)
suppressMessages({
p.loop <- p.loop +
ggplot2::scale_x_continuous(limits = c(min(p.loop$layers[[1]]$data$x),
max(p.loop$layers[[1]]$data$x))) +
ggplot2::scale_y_continuous(limits = c(min(p.loop$layers[[1]]$data$y),
max(p.loop$layers[[1]]$data$y)))
})
if (!(is.null(group.by))){
if (isTRUE(group.by.show.dots)){
p.loop$layers <- append(p.loop$layers, c(center_layer_2, center_layer_1))
}
if (isTRUE(group.by.cell_borders)){
p.loop$layers <- append(color_layer, p.loop$layers)
}
if (is.null(group.by.legend)){
legend.name <- group.by
} else {
if (is.na(group.by.legend)){
legend.name <- ""
} else {
legend.name <- group.by.legend
}
}
p.loop <- p.loop +
ggplot2::scale_fill_manual(values = group.by.colors.use,
name = legend.name) +
ggplot2::guides(fill = ggplot2::guide_legend(title = legend.name,
title.position = "top",
title.hjust = 0.5,
ncol = legend.ncol,
nrow = legend.nrow,
byrow = legend.byrow,
override.aes = list(alpha = 1)))
}
}
if (isTRUE(plot_density_contour)){
data <- ggplot2::ggplot_build(p.loop)
density_layer <- ggplot2::stat_density_2d(data = data$data[[1]],
mapping = ggplot2::aes(x = .data$x,
y = .data$y),
color = contour.color,
lineend = contour.lineend,
linejoin = contour.linejoin)
if (contour.position == "bottom"){
p.loop$layers <- append(density_layer, p.loop$layers)
} else if (contour.position == "top"){
p.loop$layers <- append(p.loop$layers, density_layer)
}
min_x <- min(data$data[[1]]$x) * (1 + contour_expand_axes)
max_x <- max(data$data[[1]]$x) * (1 + contour_expand_axes)
min_y <- min(data$data[[1]]$y) * (1 + contour_expand_axes)
max_y <- max(data$data[[1]]$y) * (1 + contour_expand_axes)
# Expand axes limits to allocate the new contours.
suppressMessages({
p.loop <- p.loop +
ggplot2::xlim(c(min_x, max_x)) +
ggplot2::ylim(c(min_y, max_y))
})
}
} else if (!(is.null(split.by))){
# Recover all metadata.
data <- sample[[]]
# Retrieve the metadata column belonging to the split.by parameter.
data.use <- data[, split.by, drop = FALSE]
# Retrieve the plotting order, keep factor levels if the column is a factor.
if (is.null(idents.keep)){
plot_order <- if (is.factor(data.use[, 1])){levels(data.use[, 1])} else {sort(unique(data.use[, 1]))}
} else {
plot_order <- sort(idents.keep)
}
list.plots.split.by <- list()
count_plot <- 0 # Will update for each unique value in split.by.
count_iteration <- count_iteration + 1 # Will update for each feature.
# Compute the limits of the variable.
## This should be replaced by a function in utils.R
limits <- c(min(sample$dummy[!is.na(sample$dummy)]), max(sample$dummy[!is.na(sample$dummy)]))
if (!is.na(min.cutoff.use)){
limits[1] <- min.cutoff.use
}
if (!is.na(max.cutoff.use)){
limits[2] <- max.cutoff.use
}
# For each value in split.by.
for (iteration in plot_order){
feature.use <- "dummy2"
count_plot <- count_plot + 1
# Create a second dummy variable storing the values of the feature but only for the selected subset.
sample$dummy2 <- sample$dummy
# Get the names of the cells used in this subset.
cells.iteration <- sample@meta.data[, split.by] == iteration
# Assign the cells that are not part of the iteration to NA.
sample$dummy2[!(cells.iteration)] <- NA
sample.use <- sample[, cells.iteration]
if (isTRUE(plot_cell_borders)){
out <- compute_umap_layer(sample = sample.use,
labels = colnames(sample.use@reductions[[reduction]][[]])[dims],
pt.size = pt.size,
border.density = border.density,
border.size = border.size,
border.color = border.color,
raster = raster,
raster.dpi = raster.dpi,
reduction = reduction,
group.by = group.by,
split.by = split.by,
n = 100)
base_layer_subset <- out$base_layer
}
if (utils::packageVersion("Seurat") >= "4.1.0"){
p.loop <- Seurat::FeaturePlot(sample.use,
feature.use,
slot = slot,
reduction = reduction,
order = order,
dims = dims,
pt.size = pt.size,
raster = raster,
raster.dpi = c(raster.dpi, raster.dpi),
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
label = label,
label.size = label.size,
label.color = label.color)
} else { # nocov start
p.loop <- Seurat::FeaturePlot(sample.use,
feature.use,
slot = slot,
reduction = reduction,
order = order,
dims = dims,
pt.size = pt.size,
raster = raster,
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
label = label,
label.size = label.size,
label.color = label.color)
} # nocov end
p.loop$layers[[length(p.loop$layers)]]$aes_params$fontface <- "bold"
scale.setup <- compute_scales(sample = sample,
feature = feature,
assay = assay,
reduction = NULL,
slot = slot,
number.breaks = number.breaks,
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
flavor = "Seurat",
enforce_symmetry = enforce_symmetry,
center_on_value = if(symmetry.type == "absolute"){FALSE} else {TRUE},
value_center = symmetry.center)
p.loop <- add_scale(p = p.loop,
function_use = ggplot2::scale_color_gradientn(colors = colors.gradient,
na.value = na.value,
name = legend.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits),
scale = "color")
p.loop <- p.loop +
ggplot2::ggtitle(iteration)
if (legend.position != "none"){
p.loop <- modify_continuous_legend(p = p.loop,
legend.title = if (is.null(legend.title)){feature} else {legend.title},
legend.aes = "color",
legend.type = legend.type,
legend.position = legend.position,
legend.length = legend.length,
legend.width = legend.width,
legend.framecolor = legend.framecolor,
legend.tickcolor = legend.tickcolor,
legend.framewidth = legend.framewidth,
legend.tickwidth = legend.tickwidth)
}
if (iteration != plot_order[length(plot_order)]){
p.loop <- p.loop & Seurat::NoLegend()
}
# Add cell borders.
if (isTRUE(plot_cell_borders)){
p.loop$layers <- append(base_layer_subset, p.loop$layers)
p.loop$layers <- append(na_layer, p.loop$layers)
p.loop$layers <- append(base_layer, p.loop$layers)
suppressMessages({
p.loop <- p.loop +
ggplot2::scale_x_continuous(limits = c(min(p.loop$layers[[1]]$data$x),
max(p.loop$layers[[1]]$data$x))) +
ggplot2::scale_y_continuous(limits = c(min(p.loop$layers[[1]]$data$y),
max(p.loop$layers[[1]]$data$y)))
})
if (!(is.null(group.by))){
if (isTRUE(group.by.show.dots)){
p.loop$layers <- append(p.loop$layers, c(center_layer_2, center_layer_1))
}
if (isTRUE(group.by.cell_borders)){
p.loop$layers <- append(color_layer, p.loop$layers)
}
if (is.null(group.by.legend)){
legend.name <- group.by
} else {
if (is.na(group.by.legend)){
legend.name <- ""
} else {
legend.name <- group.by.legend
}
}
p.loop <- p.loop +
ggplot2::scale_fill_manual(values = group.by.colors.use,
name = legend.name) +
ggplot2::guides(fill = ggplot2::guide_legend(title = legend.name,
title.position = "top",
title.hjust = 0.5,
ncol = legend.ncol,
nrow = legend.nrow,
byrow = legend.byrow,
override.aes = list(alpha = 1)))
}
}
if (isTRUE(plot_density_contour)){
data <- ggplot2::ggplot_build(p.loop)
density_layer <- ggplot2::stat_density_2d(data = data$data[[1]],
mapping = ggplot2::aes(x = .data$x,
y = .data$y),
color = contour.color,
lineend = contour.lineend,
linejoin = contour.linejoin)
if (contour.position == "bottom"){
p.loop$layers <- append(density_layer, p.loop$layers)
} else if (contour.position == "top"){
p.loop$layers <- append(p.loop$layers, density_layer)
}
min_x <- min(data$data[[1]]$x) * (1 + contour_expand_axes)
max_x <- max(data$data[[1]]$x) * (1 + contour_expand_axes)
min_y <- min(data$data[[1]]$y) * (1 + contour_expand_axes)
max_y <- max(data$data[[1]]$y) * (1 + contour_expand_axes)
# Expand axes limits to allocate the new contours.
suppressMessages({
p.loop <- p.loop +
ggplot2::xlim(c(min_x, max_x)) +
ggplot2::ylim(c(min_y, max_y))
})
}
list.plots.split.by[[iteration]] <- p.loop
}
p.loop <- patchwork::wrap_plots(list.plots.split.by,
ncol = ncol,
guides = "collect") +
patchwork::plot_annotation(title = ifelse(typeof(individual.titles) == "character", individual.titles[[count_iteration]], ""),
subtitle = ifelse(typeof(individual.subtitles) == "character", individual.subtitles[[count_iteration]], ""),
caption = ifelse(typeof(individual.captions) == "character", individual.captions[[count_iteration]], ""))
}
# Patch for diffusion maps.
if (stringr::str_starts(reduction, "diff|DIFF")){
# Add "DC" labels.
p.loop <- p.loop &
ggplot2::xlab(paste0("DC_", dims[1])) &
ggplot2::ylab(paste0("DC_", dims[2]))
} else if (reduction == "pca"){
p.loop <- p.loop &
ggplot2::xlab(paste0("PC_", dims[1])) &
ggplot2::ylab(paste0("PC_", dims[2]))
}
# Add the plot to the list.
list.plots[[feature]] <- p.loop
}
# Generate the final plot with patchwork and use the "ncol" parameter value for the number of columns.
p <- patchwork::wrap_plots(list.plots, nrow = 1)
}
# Fix the extra space and add theme parameters.
p <- p &
ggplot2::theme_minimal(base_size = font.size) &
ggplot2::theme(plot.margin = ggplot2::margin(t = 0, r = 0, b = 0, l = 0),
plot.title = ggplot2::element_text(face = plot.title.face,
hjust = ifelse(!(is.null(split.by)), 0.5, 0)),
plot.subtitle = ggplot2::element_text(face = plot.subtitle.face, hjust = 0),
plot.caption = ggplot2::element_text(face = plot.caption.face, hjust = 1),
panel.grid = ggplot2::element_blank(),
plot.title.position = "plot",
plot.caption.position = "plot",
text = ggplot2::element_text(family = font.type),
legend.position = legend.position,
legend.text = ggplot2::element_text(face = legend.text.face),
legend.title = ggplot2::element_text(face = legend.title.face),
legend.justification = "center",
plot.background = ggplot2::element_rect(fill = "white", color = "white"),
panel.background = ggplot2::element_rect(fill = "white", color = "white"),
legend.background = ggplot2::element_rect(fill = "white", color = "white"))
if (is.null(split.by) & legend.position != "none"){
counter <- 0
for (feature in features){
counter <- counter + 1
p[[counter]] <- modify_continuous_legend(p = p[[counter]],
legend.title = legend.title,
legend.aes = "color",
legend.type = legend.type,
legend.position = legend.position,
legend.length = legend.length,
legend.width = legend.width,
legend.framecolor = legend.framecolor,
legend.tickcolor = legend.tickcolor,
legend.framewidth = legend.framewidth,
legend.tickwidth = legend.tickwidth)
}
}
# Add custom title.
if (!is.null(plot.title)){
if (length(features) > 1 | !(is.null(split.by)) | !(is.null(cells.highlight)) | !(is.null(idents.highlight))){
p <- p +
patchwork::plot_annotation(title = plot.title)
} else {
p <- p +
ggplot2::labs(title = plot.title)
}
}
# Add custom subtitle.
if (!is.null(plot.subtitle)){
if (length(features) > 1 | !(is.null(split.by)) | !(is.null(cells.highlight)) | !(is.null(idents.highlight))){
p <- p +
patchwork::plot_annotation(subtitle = plot.subtitle)
} else {
p <- p +
ggplot2::labs(subtitle = plot.subtitle)
}
}
# Add custom caption
if (!is.null(plot.caption)){
if (length(features) > 1 | !(is.null(split.by)) | !(is.null(cells.highlight)) | !(is.null(idents.highlight))){
p <- p +
patchwork::plot_annotation(caption = plot.caption)
} else {
p <- p +
ggplot2::labs(caption = plot.caption)
}
}
# Add individual titles.
if (!is.null(individual.titles)){
for (counter in seq(1, length(features))){
if (!(is.na(individual.titles[counter]))){
if (is.null(split.by)){
p[[counter]]$labels$title <- individual.titles[counter]
}
}
}
}
# Add individual subtitles.
if (!is.null(individual.subtitles)){
for (counter in seq(1, length(features))){
if (!(is.na(individual.subtitles[counter]))){
if (is.null(split.by)){
p[[counter]]$labels$subtitle <- individual.subtitles[counter]
}
}
}
}
# Add individual captions
if (!is.null(individual.captions)){
for (counter in seq(1, length(features))){
if (!(is.na(individual.captions[counter]))){
if (is.null(split.by)){
p[[counter]]$labels$caption <- individual.captions[counter]
}
}
}
}
if (base::isFALSE(plot.axes)){
p <- p &
ggplot2::theme(axis.title = ggplot2::element_blank(),
axis.text = ggplot2::element_blank(),
axis.ticks = ggplot2::element_blank(),
axis.line = ggplot2::element_blank())
} else {
p <- p &
ggplot2::theme(axis.title = ggplot2::element_text(face = axis.title.face),
axis.text = ggplot2::element_text(face = axis.text.face),
axis.ticks = ggplot2::element_line(color = "black"),
axis.line = ggplot2::element_line(color = "black"))
}
# Further patch for diffusion maps.
if (stringr::str_starts(reduction, "diff|DIFF")){
labels <- colnames(sample@reductions[[reduction]][[]])[dims]
# Fix the axis scale so that the highest and lowest values are in the range of the DCs (previously was around +-1.5, while DCs might range to +-0.004 or so).
p <- suppressMessages({
p &
ggplot2::xlim(c(min(sample@reductions[[reduction]][[]][, labels[1]], na.rm = TRUE),
max(sample@reductions[[reduction]][[]][, labels[1]], na.rm = TRUE))) &
ggplot2::ylim(c(min(sample@reductions[[reduction]][[]][, labels[2]], na.rm = TRUE),
max(sample@reductions[[reduction]][[]][, labels[2]], na.rm = TRUE)))
})
}
# Add legend titles.
if (is.null(split.by) & is.null(legend.title)){
counter <- 0
for (feature in features){
counter <- counter + 1
p[[counter]]$guides$colour$title <- feature
}
}
# Return the plot.
return(p)
}
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Defines functions do_FeaturePlot
Documented in do_FeaturePlot
#' Wrapper for \link[Seurat]{FeaturePlot}.
#'
#' @inheritParams doc_function
#' @param idents.keep \strong{\code{\link[base]{character}}} | Vector of identities to plot. The gradient scale will also be subset to only the values of such identities.
#' @param individual.titles,individual.subtitles,individual.captions \strong{\code{\link[base]{character}}} | Titles or subtitles. for each feature if needed. Either NULL or a vector of equal length of features.
#' @param order \strong{\code{\link[base]{logical}}} | Whether to order the cells based on expression.
#' @param group.by \strong{\code{\link[base]{character}}} | Metadata variable based on which cells are grouped. This will effectively introduce a big dot in the center of each cluster, colored using a categorical color scale or with the values provided by the user in \strong{\code{group.by.colors.use}}. It will also displays a legend.
#' @param group.by.legend \strong{\code{\link[base]{character}}} | Title for the legend when \strong{\code{group.by}} is used. Use \strong{\code{NA}} to disable it and \strong{\code{NULL}} to use the default column title provided in \strong{\code{group.by}}.
#' @param group.by.dot.size \strong{\code{\link[base]{numeric}}} | Size of the dots placed in the middle of the groups.
#' @param group.by.cell_borders \strong{\code{\link[base]{logical}}} | Plots another border around the cells displaying the same color code of the dots displayed with \strong{\code{group.by}}. Legend is shown always with alpha = 1 regardless of the alpha settings.
#' @param group.by.colors.use \strong{\code{\link[base]{character}}} | Colors to use for the group dots.
#' @param group.by.show.dots \strong{\code{\link[base]{logical}}} | Controls whether to place in the middle of the groups.
#' @param group.by.cell_borders.alpha \strong{\code{\link[base]{numeric}}} | Controls the transparency of the new borders drawn by \strong{\code{group.by.cell_borders}}.
#' @param symmetry.type \strong{\code{\link[base]{character}}} | Type of symmetry to be enforced. One of:
#' \itemize{
#' \item \emph{\code{absolute}}: The highest absolute value will be taken into a account to generate the color scale. Works after \strong{\code{min.cutoff}} and \strong{\code{max.cutoff}}.
#' \item \emph{\code{centered}}: Centers the scale around the provided value in \strong{\code{symmetry.center}}. Works after \strong{\code{min.cutoff}} and \strong{\code{max.cutoff}}.
#' }
#' @param symmetry.center \strong{\code{\link[base]{numeric}}} | Value upon which the scale will be centered.
#' @return A ggplot2 object containing a Feature Plot.
#' @export
#'
#' @example /man/examples/examples_do_FeaturePlot.R
do_FeaturePlot <- function(sample,
features,
assay = NULL,
reduction = NULL,
slot = NULL,
order = FALSE,
group.by = NULL,
group.by.colors.use = NULL,
group.by.legend = NULL,
group.by.show.dots = TRUE,
group.by.dot.size = 8,
group.by.cell_borders = FALSE,
group.by.cell_borders.alpha = 0.1,
split.by = NULL,
idents.keep = NULL,
cells.highlight = NULL,
idents.highlight = NULL,
dims = c(1, 2),
enforce_symmetry = FALSE,
symmetry.type = "absolute",
symmetry.center = NA,
pt.size = 1,
font.size = 14,
font.type = "sans",
legend.title = NULL,
legend.type = "colorbar",
legend.position = "bottom",
legend.framewidth = 0.5,
legend.tickwidth = 0.5,
legend.length = 20,
legend.width = 1,
legend.framecolor = "grey50",
legend.tickcolor = "white",
legend.ncol = NULL,
legend.nrow = NULL,
legend.byrow = FALSE,
plot.title = NULL,
plot.subtitle = NULL,
plot.caption = NULL,
individual.titles = NULL,
individual.subtitles = NULL,
individual.captions = NULL,
ncol = NULL,
use_viridis = FALSE,
viridis.palette = "G",
viridis.direction = 1,
raster = FALSE,
raster.dpi = 1024,
plot_cell_borders = TRUE,
border.size = 2,
border.color = "black",
border.density = 1,
na.value = "grey75",
verbose = TRUE,
plot.axes = FALSE,
min.cutoff = rep(NA, length(features)),
max.cutoff = rep(NA, length(features)),
plot_density_contour = FALSE,
contour.position = "bottom",
contour.color = "grey90",
contour.lineend = "butt",
contour.linejoin = "round",
contour_expand_axes = 0.25,
label = FALSE,
label.color = "black",
label.size = 4,
number.breaks = 5,
diverging.palette = "RdBu",
diverging.direction = -1,
sequential.palette = "YlGnBu",
sequential.direction = 1,
plot.title.face = "bold",
plot.subtitle.face = "plain",
plot.caption.face = "italic",
axis.title.face = "bold",
axis.text.face = "plain",
legend.title.face = "bold",
legend.text.face = "plain"){
# Add lengthy error messages.
withr::local_options(.new = list("warning.length" = 8170))
check_suggests(function_name = "do_FeaturePlot")
# Check if the sample provided is a Seurat object.
check_Seurat(sample = sample)
# Check the assay.
out <- check_and_set_assay(sample = sample, assay = assay)
sample <- out[["sample"]]
assay <- out[["assay"]]
sample <- check_Assay5(sample, assay = assay)
# Check the reduction.
reduction <- check_and_set_reduction(sample = sample, reduction = reduction)
# Check the dimensions.
dims <- check_and_set_dimensions(sample = sample, reduction = reduction, dims = dims)
# Check logical parameters.
logical_list <- list("verbose" = verbose,
"raster" = raster,
"plot_cell_borders" = plot_cell_borders,
"order" = order,
"enforce_symmetry" = enforce_symmetry,
"plot.axes" = plot.axes,
"plot_density_contour" = plot_density_contour,
"label" = label,
"legend.byrow" = legend.byrow,
"group.by.cell_borders" = group.by.cell_borders,
"group.by.show.dots" = group.by.show.dots,
"use_viridis" = use_viridis)
check_type(parameters = logical_list, required_type = "logical", test_function = is.logical)
# Check numeric parameters.
numeric_list <- list("pt.size" = pt.size,
"ncol" = ncol,
"font.size" = font.size,
"raster.dpi" = raster.dpi,
"legend.framewidth" = legend.framewidth,
"legend.tickwidth" = legend.tickwidth,
"legend.length" = legend.length,
"legend.width" = legend.width,
"border.size" = border.size,
"viridis.direction" = viridis.direction,
"min.cutoff" = min.cutoff,
"max.cutoff" = max.cutoff,
"contour_expand_axes" = contour_expand_axes,
"label.size" = label.size,
"number.breaks" = number.breaks,
"border.density" = border.density,
"legend.nrow" = legend.nrow,
"legend.ncol" = legend.ncol,
"group.by.dot.size" = group.by.dot.size,
"group.by.cell_borders.alpha" = group.by.cell_borders.alpha,
"sequential.direction" = sequential.direction,
"diverging.direction" = diverging.direction,
"symmetry.center" = symmetry.center)
check_type(parameters = numeric_list, required_type = "numeric", test_function = is.numeric)
# Check character parameters.
# Workaround for features.
if (is.list(features)){
warning(paste0(add_warning(), crayon_body("Features provided as a list. Unlisting the list. Please use a character vector next time."), call. = FALSE))
features <- unique(unlist(features))
}
character_list <- list("legend.position" = legend.position,
"features" = features,
"cells.highlight" = cells.highlight,
"idents.highlight" = idents.highlight,
"slot" = slot,
"group.by" = group.by,
"split.by" = split.by,
"plot.title" = plot.title,
"plot.subtitle" = plot.subtitle,
"plot.caption" = plot.caption,
"idents.keep" = idents.keep,
"viridis.palette" = viridis.palette,
"individual.titles" = individual.titles,
"individual.subtitles" = individual.subtitles,
"individual.captions" = individual.captions,
"legend.framecolor" = legend.framecolor,
"legend.tickcolor" = legend.tickcolor,
"legend.type" = legend.type,
"font.type" = font.type,
"border.color" = border.color,
"legend.title" = legend.title,
"na.value" = na.value,
"contour.position" = contour.position,
"contour.color" = contour.color,
"contour.lineend" = contour.lineend,
"contour.linejoin" = contour.linejoin,
"label.color" = label.color,
"group.by.colors.use" = group.by.colors.use,
"diverging.palette" = diverging.palette,
"sequential.palette" = sequential.palette,
"plot.title.face" = plot.title.face,
"plot.subtitle.face" = plot.subtitle.face,
"plot.caption.face" = plot.caption.face,
"axis.title.face" = axis.title.face,
"axis.text.face" = axis.text.face,
"legend.title.face" = legend.title.face,
"legend.text.face" = legend.text.face,
"symmetry.type" = symmetry.type)
check_type(parameters = character_list, required_type = "character", test_function = is.character)
# Check slot.
slot <- check_and_set_slot(slot = slot)
# Check split.by is on metadata.
if (!(is.null(split.by))){check_feature(sample = sample, features = split.by, enforce_check = "metadata", enforce_parameter = "split.by")}
# Check individual titles.
if (length(features) > 1 & !is.null(individual.titles)){
assertthat::assert_that(length(features) == length(individual.titles),
msg = paste0(add_cross(), crayon_body("The total number of "),
crayon_key("individual titles"),
crayon_body(" does not match the number of "),
crayon_key("features"),
crayon_body(" provided.")))
}
if (length(features) > 1 & !is.null(individual.subtitles)){
assertthat::assert_that(length(features) == length(individual.subtitles),
msg = paste0(add_cross(), crayon_body("The total number of "),
crayon_key("individual subtitles"),
crayon_body(" does not match the number of "),
crayon_key("features"),
crayon_body(" provided.")))
}
if (length(features) > 1 & !is.null(individual.captions)){
assertthat::assert_that(length(features) == length(individual.captions),
msg = paste0(add_cross(), crayon_body("The total number of "),
crayon_key("individual captions"),
crayon_body(" does not match the number of "),
crayon_key("features"),
crayon_body(" provided.")))
}
## Check that the contour_expand_axes is between 0 and 1.
assertthat::assert_that(contour_expand_axes <= 1,
msg = paste0(add_cross(), crayon_body("Please provide a value to "),
crayon_key("countour_expand_axes"),
crayon_body(" lower or equal than "),
crayon_key("1"),
crayon_body(".")))
assertthat::assert_that(contour_expand_axes >= 0,
msg = paste0(add_cross(), crayon_body("Please provide a value to "),
crayon_key("countour_expand_axes"),
crayon_body(" higher or equal than "),
crayon_key("0"),
crayon_body(".")))
check_colors(border.color, parameter_name = "border.color")
check_colors(na.value, parameter_name = "na.value")
check_colors(legend.framecolor, parameter_name = "legend.framecolor")
check_colors(legend.tickcolor, parameter_name = "legend.tickcolor")
check_colors(contour.color, parameter_name = "contour.color")
check_colors(label.color, parameter_name = "label.color")
if (!is.null(group.by)){
if (!is.null(group.by.colors.use)){
check_colors(group.by.colors.use, parameter_name = "group.by.colors.use")
check_consistency_colors_and_names(sample = sample,
colors = group.by.colors.use,
grouping_variable = group.by)
} else {
data.use <- sample@meta.data[, group.by, drop = FALSE]
# If the variable is a factor, use the levels as order. If not, order the values alphabetically.
names.use <- if (is.factor(data.use[, 1])){levels(data.use[, 1])} else {sort(unique(data.use[, 1]))}
# Generate the color scale to be used based on the unique values of group.by.
group.by.colors.use <- generate_color_scale(names.use)
}
}
check_parameters(parameter = font.type, parameter_name = "font.type")
check_parameters(parameter = legend.type, parameter_name = "legend.type")
check_parameters(parameter = legend.position, parameter_name = "legend.position")
check_parameters(parameter = viridis.palette, parameter_name = "viridis.palette")
check_parameters(parameter = contour.lineend, parameter_name = "contour.lineend")
check_parameters(parameter = contour.linejoin, parameter_name = "contour.linejoin")
check_parameters(parameter = contour.position, parameter_name = "contour.position")
check_parameters(parameter = number.breaks, parameter_name = "number.breaks")
check_parameters(parameter = border.density, parameter_name = "border.density")
check_parameters(parameter = diverging.palette, parameter_name = "diverging.palette")
check_parameters(parameter = sequential.palette, parameter_name = "sequential.palette")
check_parameters(plot.title.face, parameter_name = "plot.title.face")
check_parameters(plot.subtitle.face, parameter_name = "plot.subtitle.face")
check_parameters(plot.caption.face, parameter_name = "plot.caption.face")
check_parameters(axis.title.face, parameter_name = "axis.title.face")
check_parameters(axis.text.face, parameter_name = "axis.text.face")
check_parameters(legend.title.face, parameter_name = "legend.title.face")
check_parameters(legend.text.face, parameter_name = "legend.text.face")
check_parameters(viridis.direction, parameter_name = "viridis.direction")
check_parameters(sequential.direction, parameter_name = "sequential.direction")
check_parameters(diverging.direction, parameter_name = "diverging.direction")
check_parameters(parameter = symmetry.type, parameter_name = "symmetry.type")
if (length(min.cutoff) != length(features)){
warning(paste0(add_warning(), crayon_body("Please provide as many values to "),
crayon_key("min.cutoff"),
crayon_body(" as "),
crayon_key("features"),
crayon_body(" provided. The values will be used in order and, when outside of the range, no cutoffs will be applied.")), call. = FALSE)
}
if (length(max.cutoff) != length(features)){
warning(paste0(add_warning(), crayon_body("Please provide as many values to "),
crayon_key("max.cutoff"),
crayon_body(" as "),
crayon_key("features"),
crayon_body(" provided. The values will be used in order and, when outside of the range, no cutoffs will be applied.")), call. = FALSE)
}
# Generate the continuous color palette.
if (isTRUE(enforce_symmetry)){
colors.gradient <- compute_continuous_palette(name = diverging.palette,
use_viridis = FALSE,
direction = diverging.direction,
enforce_symmetry = enforce_symmetry)
} else {
colors.gradient <- compute_continuous_palette(name = ifelse(isTRUE(use_viridis), viridis.palette, sequential.palette),
use_viridis = use_viridis,
direction = ifelse(isTRUE(use_viridis), viridis.direction, sequential.direction),
enforce_symmetry = enforce_symmetry)
}
# Generate base layer.
if (isTRUE(plot_cell_borders)){
out <- compute_umap_layer(sample = sample,
labels = colnames(sample@reductions[[reduction]][[]])[dims],
pt.size = pt.size,
dot.size = group.by.dot.size,
border.density = border.density,
border.size = border.size,
border.color = border.color,
raster = raster,
raster.dpi = raster.dpi,
reduction = reduction,
group.by = group.by,
split.by = split.by,
na.value = na.value,
alpha = group.by.cell_borders.alpha,
n = 100)
base_layer <- out$base_layer
na_layer <- out$na_layer
if (!is.null(group.by)){
center_layers <- out$center_layers
center_layer_1 <- center_layers$center_layer_1
center_layer_2 <- center_layers$center_layer_2
color_layer <- out$color_layer
}
}
# CONDITION: Regular FeaturePlot, under default parameters.
default_parameters <- is.null(split.by) & is.null(cells.highlight) & is.null(idents.highlight)
# If only default parameters are used.
if (default_parameters){
# Check if the feature is actually in the object.
features <- check_feature(sample = sample,
features = features,
permissive = TRUE)
# Remove duplicated features.
features <- remove_duplicated_features(features = features)
if (utils::packageVersion("Seurat") >= "4.1.0"){
p <- Seurat::FeaturePlot(sample,
features,
slot = slot,
reduction = reduction,
order = order,
dims = dims,
pt.size = pt.size,
ncol = ncol,
raster = raster,
raster.dpi = c(raster.dpi, raster.dpi),
min.cutoff = min.cutoff,
max.cutoff = max.cutoff,
label = label,
label.size = label.size,
label.color = label.color)
} else { # nocov start
p <- Seurat::FeaturePlot(sample,
features,
slot = slot,
reduction = reduction,
order = order,
dims = dims,
pt.size = pt.size,
ncol = ncol,
raster = raster,
min.cutoff = min.cutoff,
max.cutoff = max.cutoff,
label = label,
label.size = label.size,
label.color = label.color)
} # nocov end
p$layers[[length(p$layers)]]$aes_params$fontface <- "bold"
p <- p &
# Remove Seurat::FeaturePlot() default plot title.
ggplot2::ggtitle("")
# Add color scales.
num_plots <- length(features)
for (counter in seq(1, num_plots)){
if (num_plots == 1){
scale.setup <- compute_scales(sample = sample,
feature = features,
assay = assay,
reduction = NULL,
slot = slot,
number.breaks = number.breaks,
min.cutoff = min.cutoff,
max.cutoff = max.cutoff,
flavor = "Seurat",
enforce_symmetry = enforce_symmetry,
center_on_value = if(symmetry.type == "absolute"){FALSE} else {TRUE},
value_center = symmetry.center)
p <- add_scale(p = p,
function_use = ggplot2::scale_color_gradientn(colors = colors.gradient,
na.value = na.value,
name = legend.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits),
scale = "color")
} else if (num_plots > 1){
feature.use <- features[counter]
scale.setup <- compute_scales(sample = sample,
feature = feature.use,
assay = assay,
reduction = NULL,
slot = slot,
number.breaks = number.breaks,
min.cutoff = min.cutoff[counter],
max.cutoff = max.cutoff[counter],
flavor = "Seurat",
enforce_symmetry = enforce_symmetry,
center_on_value = if(symmetry.type == "absolute"){FALSE} else {TRUE},
value_center = symmetry.center)
p[[counter]] <- add_scale(p = p[[counter]],
function_use = ggplot2::scale_color_gradientn(colors = colors.gradient,
na.value = na.value,
name = legend.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits),
scale = "color")
}
}
# Special patches for diffusion maps: Adding "DC" labels to the axis.
if (stringr::str_starts(reduction, "diff|DIFF")){
p <- p &
ggplot2::xlab(paste0("DC_", dims[1])) &
ggplot2::ylab(paste0("DC_", dims[2]))
}
# Add cell borders.
if (isTRUE(plot_cell_borders)){
counter <- 0
for (feature in features){
counter <- counter + 1
p[[counter]]$layers <- append(base_layer, p[[counter]]$layers)
if (!(is.null(group.by))){
if (isTRUE(group.by.show.dots)){
p[[counter]]$layers <- append(p[[counter]]$layers, c(center_layer_2, center_layer_1))
}
if (isTRUE(group.by.cell_borders)){
p[[counter]]$layers <- append(color_layer, p[[counter]]$layers)
}
if (is.null(group.by.legend)){
legend.name <- group.by
} else {
if (is.na(group.by.legend)){
legend.name <- ""
} else {
legend.name <- group.by.legend
}
}
p[[counter]] <- p[[counter]] +
ggplot2::scale_fill_manual(values = group.by.colors.use,
name = legend.name) +
ggplot2::guides(fill = ggplot2::guide_legend(title = legend.name,
title.position = "top",
title.hjust = 0.5,
ncol = legend.ncol,
nrow = legend.nrow,
byrow = legend.byrow,
override.aes = list(alpha = 1)))
}
}
}
if (isTRUE(plot_density_contour)){
counter <- 0
for (feature in features){
counter <- counter + 1
data <- ggplot2::ggplot_build(p[[counter]])
density_layer <- ggplot2::stat_density_2d(data = data$data[[1]],
mapping = ggplot2::aes(x = .data$x,
y = .data$y),
color = contour.color,
lineend = contour.lineend,
linejoin = contour.linejoin)
if (contour.position == "bottom"){
p[[counter]]$layers <- append(density_layer, p[[counter]]$layers)
} else if (contour.position == "top"){
p[[counter]]$layers <- append(p[[counter]]$layers, density_layer)
}
min_x <- min(data$data[[1]]$x) * (1 + contour_expand_axes)
max_x <- max(data$data[[1]]$x) * (1 + contour_expand_axes)
min_y <- min(data$data[[1]]$y) * (1 + contour_expand_axes)
max_y <- max(data$data[[1]]$y) * (1 + contour_expand_axes)
# Expand axes limits to allocate the new contours.
suppressMessages({
p[[counter]] <- p[[counter]] +
ggplot2::xlim(c(min_x, max_x)) +
ggplot2::ylim(c(min_y, max_y))
})
}
}
# Modified FeaturePlot including only a subset of cells.
} else {
# Check if the feature is actually in the object.
output_list <- check_feature(sample = sample,
features = features,
dump_reduction_names = TRUE,
permissive = TRUE)
features <- output_list[["features"]]
dim_colnames <- output_list[["reduction_names"]]
# Remove duplicated features.
features <- remove_duplicated_features(features = features)
# Get the subset of wanted cells according to the combination of idents.highlight and cells.highlight parameters.
if (is.null(idents.highlight) & !(is.null(cells.highlight))){
# Only if cells.highlight parameters is used.
cells.use <- cells.highlight
} else if (!(is.null(idents.highlight)) & is.null(cells.highlight)){
# Only if idents.highlight parameter is used.
# Check if the provided identities are part of the active identities in the object.
check_identity(sample = sample, identities = idents.highlight)
# Get the names of the cells to use.
cells.use <- names(Seurat::Idents(sample)[Seurat::Idents(sample) %in% idents.highlight])
} else if (!(is.null(idents.highlight)) & !(is.null(cells.highlight))){
# Check if the provided identities are part of the active identities in the object.
check_identity(sample = sample, identities = idents.highlight)
# Both idents.highlight and cells.highlight are used.
cells.1 <- cells.highlight
cells.2 <- names(Seurat::Idents(sample)[Seurat::Idents(sample) %in% idents.highlight])
# Get the names of the cells to use.
cells.use <- unique(c(cells.1, cells.2))
# If split.by is used.
} else if (!(is.null(split.by))){
# No identities selected by the user.
if (is.null(idents.keep)){
cells.use <- colnames(sample)
# Identitites selected by the user.
} else {
# Check if the identitites are duplicated. If so, remove them.
if (sum(duplicated(idents.keep)) != 0){
message(paste0(add_info(), crayon_body("Found and removed duplicated values in idents.keep.")))
idents.keep <- idents.keep[!duplicated(idents.keep)]
}
# Get the names of the cells to plot.
cells.use <- names(Seurat::Idents(sample)[Seurat::Idents(sample) %in% idents.keep])
}
}
# Plots are generated independently if more than one feature is provided.
list.plots <- list()
# Counter depicting the feature used. Will increase each feature used.
count_iteration <- 0
cutoff.counter <- 0
# Iterate over the features.
for (feature in features){
cutoff.counter <- cutoff.counter + 1
min.cutoff.use <- min.cutoff[cutoff.counter]
max.cutoff.use <- max.cutoff[cutoff.counter]
# A "dummy" metadata column is generated using the values of the selected feature.
## Based on whether the feature is in the metadata of the object.
if (feature %in% colnames(sample@meta.data)) {
sample$dummy <- sample@meta.data[, feature]
## Or is a gene in the object.
} else if (feature %in% rownames(sample)){
suppressWarnings({
sample$dummy <- SeuratObject::GetAssayData(object = sample, slot = slot, assay = assay)[feature, ]
})
## Or is a dimensional reduction component.
} else if (feature %in% dim_colnames){
# Iterate over each dimensional reduction in the object.
for(red in Seurat::Reductions(object = sample)){
# If the feature to plot is one of the dimensional reduction components.
if (feature %in% colnames(sample@reductions[[red]][[]])){
red.feature <- red
sample$dummy <- sample@reductions[[red.feature]][[]][, feature]
}
}
}
# Assign NAs to the values corresponding to the cells not selected.
sample$dummy[!(names(sample$dummy) %in% cells.use)] <- NA
# If split.by is not used.
if (is.null(split.by)){
feature.use <- "dummy"
sample.use <- sample[, cells.use]
if (isTRUE(plot_cell_borders)){
out <- compute_umap_layer(sample = sample.use,
labels = colnames(sample.use@reductions[[reduction]][[]])[dims],
pt.size = pt.size,
border.density = border.density,
border.size = border.size,
border.color = border.color,
raster = raster,
raster.dpi = raster.dpi,
reduction = reduction,
group.by = group.by,
split.by = split.by,
n = 100)
base_layer_subset <- out$base_layer
}
if (utils::packageVersion("Seurat") >= "4.1.0"){
p.loop <- Seurat::FeaturePlot(sample.use,
feature.use,
reduction = reduction,
slot = slot,
order = order,
dims = dims,
pt.size = pt.size,
raster = raster,
raster.dpi = c(raster.dpi, raster.dpi),
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
label = label,
label.size = label.size,
label.color = label.color)
} else { # nocov start
p.loop <- Seurat::FeaturePlot(sample.use,
feature.use,
reduction = reduction,
slot = slot,
order = order,
dims = dims,
pt.size = pt.size,
raster = raster,
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
label = label,
label.size = label.size,
label.color = label.color)
} # nocov end
p.loop$layers[[length(p.loop$layers)]]$aes_params$fontface <- "bold"
# Add scale.
scale.setup <- compute_scales(sample = sample,
feature = feature,
assay = assay,
reduction = NULL,
slot = slot,
number.breaks = number.breaks,
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
flavor = "Seurat",
enforce_symmetry = enforce_symmetry,
center_on_value = if(symmetry.type == "absolute"){FALSE} else {TRUE},
value_center = symmetry.center)
p.loop <- add_scale(p = p.loop,
function_use = ggplot2::scale_color_gradientn(colors = colors.gradient,
na.value = na.value,
name = legend.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits),
scale = "color")
p.loop <- p.loop +
ggplot2::ggtitle("")
# Add cell borders.
if (isTRUE(plot_cell_borders)){
p.loop$layers <- append(base_layer_subset, p.loop$layers)
p.loop$layers <- append(na_layer, p.loop$layers)
p.loop$layers <- append(base_layer, p.loop$layers)
suppressMessages({
p.loop <- p.loop +
ggplot2::scale_x_continuous(limits = c(min(p.loop$layers[[1]]$data$x),
max(p.loop$layers[[1]]$data$x))) +
ggplot2::scale_y_continuous(limits = c(min(p.loop$layers[[1]]$data$y),
max(p.loop$layers[[1]]$data$y)))
})
if (!(is.null(group.by))){
if (isTRUE(group.by.show.dots)){
p.loop$layers <- append(p.loop$layers, c(center_layer_2, center_layer_1))
}
if (isTRUE(group.by.cell_borders)){
p.loop$layers <- append(color_layer, p.loop$layers)
}
if (is.null(group.by.legend)){
legend.name <- group.by
} else {
if (is.na(group.by.legend)){
legend.name <- ""
} else {
legend.name <- group.by.legend
}
}
p.loop <- p.loop +
ggplot2::scale_fill_manual(values = group.by.colors.use,
name = legend.name) +
ggplot2::guides(fill = ggplot2::guide_legend(title = legend.name,
title.position = "top",
title.hjust = 0.5,
ncol = legend.ncol,
nrow = legend.nrow,
byrow = legend.byrow,
override.aes = list(alpha = 1)))
}
}
if (isTRUE(plot_density_contour)){
data <- ggplot2::ggplot_build(p.loop)
density_layer <- ggplot2::stat_density_2d(data = data$data[[1]],
mapping = ggplot2::aes(x = .data$x,
y = .data$y),
color = contour.color,
lineend = contour.lineend,
linejoin = contour.linejoin)
if (contour.position == "bottom"){
p.loop$layers <- append(density_layer, p.loop$layers)
} else if (contour.position == "top"){
p.loop$layers <- append(p.loop$layers, density_layer)
}
min_x <- min(data$data[[1]]$x) * (1 + contour_expand_axes)
max_x <- max(data$data[[1]]$x) * (1 + contour_expand_axes)
min_y <- min(data$data[[1]]$y) * (1 + contour_expand_axes)
max_y <- max(data$data[[1]]$y) * (1 + contour_expand_axes)
# Expand axes limits to allocate the new contours.
suppressMessages({
p.loop <- p.loop +
ggplot2::xlim(c(min_x, max_x)) +
ggplot2::ylim(c(min_y, max_y))
})
}
} else if (!(is.null(split.by))){
# Recover all metadata.
data <- sample[[]]
# Retrieve the metadata column belonging to the split.by parameter.
data.use <- data[, split.by, drop = FALSE]
# Retrieve the plotting order, keep factor levels if the column is a factor.
if (is.null(idents.keep)){
plot_order <- if (is.factor(data.use[, 1])){levels(data.use[, 1])} else {sort(unique(data.use[, 1]))}
} else {
plot_order <- sort(idents.keep)
}
list.plots.split.by <- list()
count_plot <- 0 # Will update for each unique value in split.by.
count_iteration <- count_iteration + 1 # Will update for each feature.
# Compute the limits of the variable.
## This should be replaced by a function in utils.R
limits <- c(min(sample$dummy[!is.na(sample$dummy)]), max(sample$dummy[!is.na(sample$dummy)]))
if (!is.na(min.cutoff.use)){
limits[1] <- min.cutoff.use
}
if (!is.na(max.cutoff.use)){
limits[2] <- max.cutoff.use
}
# For each value in split.by.
for (iteration in plot_order){
feature.use <- "dummy2"
count_plot <- count_plot + 1
# Create a second dummy variable storing the values of the feature but only for the selected subset.
sample$dummy2 <- sample$dummy
# Get the names of the cells used in this subset.
cells.iteration <- sample@meta.data[, split.by] == iteration
# Assign the cells that are not part of the iteration to NA.
sample$dummy2[!(cells.iteration)] <- NA
sample.use <- sample[, cells.iteration]
if (isTRUE(plot_cell_borders)){
out <- compute_umap_layer(sample = sample.use,
labels = colnames(sample.use@reductions[[reduction]][[]])[dims],
pt.size = pt.size,
border.density = border.density,
border.size = border.size,
border.color = border.color,
raster = raster,
raster.dpi = raster.dpi,
reduction = reduction,
group.by = group.by,
split.by = split.by,
n = 100)
base_layer_subset <- out$base_layer
}
if (utils::packageVersion("Seurat") >= "4.1.0"){
p.loop <- Seurat::FeaturePlot(sample.use,
feature.use,
slot = slot,
reduction = reduction,
order = order,
dims = dims,
pt.size = pt.size,
raster = raster,
raster.dpi = c(raster.dpi, raster.dpi),
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
label = label,
label.size = label.size,
label.color = label.color)
} else { # nocov start
p.loop <- Seurat::FeaturePlot(sample.use,
feature.use,
slot = slot,
reduction = reduction,
order = order,
dims = dims,
pt.size = pt.size,
raster = raster,
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
label = label,
label.size = label.size,
label.color = label.color)
} # nocov end
p.loop$layers[[length(p.loop$layers)]]$aes_params$fontface <- "bold"
scale.setup <- compute_scales(sample = sample,
feature = feature,
assay = assay,
reduction = NULL,
slot = slot,
number.breaks = number.breaks,
min.cutoff = min.cutoff.use,
max.cutoff = max.cutoff.use,
flavor = "Seurat",
enforce_symmetry = enforce_symmetry,
center_on_value = if(symmetry.type == "absolute"){FALSE} else {TRUE},
value_center = symmetry.center)
p.loop <- add_scale(p = p.loop,
function_use = ggplot2::scale_color_gradientn(colors = colors.gradient,
na.value = na.value,
name = legend.title,
breaks = scale.setup$breaks,
labels = scale.setup$labels,
limits = scale.setup$limits),
scale = "color")
p.loop <- p.loop +
ggplot2::ggtitle(iteration)
if (legend.position != "none"){
p.loop <- modify_continuous_legend(p = p.loop,
legend.title = if (is.null(legend.title)){feature} else {legend.title},
legend.aes = "color",
legend.type = legend.type,
legend.position = legend.position,
legend.length = legend.length,
legend.width = legend.width,
legend.framecolor = legend.framecolor,
legend.tickcolor = legend.tickcolor,
legend.framewidth = legend.framewidth,
legend.tickwidth = legend.tickwidth)
}
if (iteration != plot_order[length(plot_order)]){
p.loop <- p.loop & Seurat::NoLegend()
}
# Add cell borders.
if (isTRUE(plot_cell_borders)){
p.loop$layers <- append(base_layer_subset, p.loop$layers)
p.loop$layers <- append(na_layer, p.loop$layers)
p.loop$layers <- append(base_layer, p.loop$layers)
suppressMessages({
p.loop <- p.loop +
ggplot2::scale_x_continuous(limits = c(min(p.loop$layers[[1]]$data$x),
max(p.loop$layers[[1]]$data$x))) +
ggplot2::scale_y_continuous(limits = c(min(p.loop$layers[[1]]$data$y),
max(p.loop$layers[[1]]$data$y)))
})
if (!(is.null(group.by))){
if (isTRUE(group.by.show.dots)){
p.loop$layers <- append(p.loop$layers, c(center_layer_2, center_layer_1))
}
if (isTRUE(group.by.cell_borders)){
p.loop$layers <- append(color_layer, p.loop$layers)
}
if (is.null(group.by.legend)){
legend.name <- group.by
} else {
if (is.na(group.by.legend)){
legend.name <- ""
} else {
legend.name <- group.by.legend
}
}
p.loop <- p.loop +
ggplot2::scale_fill_manual(values = group.by.colors.use,
name = legend.name) +
ggplot2::guides(fill = ggplot2::guide_legend(title = legend.name,
title.position = "top",
title.hjust = 0.5,
ncol = legend.ncol,
nrow = legend.nrow,
byrow = legend.byrow,
override.aes = list(alpha = 1)))
}
}
if (isTRUE(plot_density_contour)){
data <- ggplot2::ggplot_build(p.loop)
density_layer <- ggplot2::stat_density_2d(data = data$data[[1]],
mapping = ggplot2::aes(x = .data$x,
y = .data$y),
color = contour.color,
lineend = contour.lineend,
linejoin = contour.linejoin)
if (contour.position == "bottom"){
p.loop$layers <- append(density_layer, p.loop$layers)
} else if (contour.position == "top"){
p.loop$layers <- append(p.loop$layers, density_layer)
}
min_x <- min(data$data[[1]]$x) * (1 + contour_expand_axes)
max_x <- max(data$data[[1]]$x) * (1 + contour_expand_axes)
min_y <- min(data$data[[1]]$y) * (1 + contour_expand_axes)
max_y <- max(data$data[[1]]$y) * (1 + contour_expand_axes)
# Expand axes limits to allocate the new contours.
suppressMessages({
p.loop <- p.loop +
ggplot2::xlim(c(min_x, max_x)) +
ggplot2::ylim(c(min_y, max_y))
})
}
list.plots.split.by[[iteration]] <- p.loop
}
p.loop <- patchwork::wrap_plots(list.plots.split.by,
ncol = ncol,
guides = "collect") +
patchwork::plot_annotation(title = ifelse(typeof(individual.titles) == "character", individual.titles[[count_iteration]], ""),
subtitle = ifelse(typeof(individual.subtitles) == "character", individual.subtitles[[count_iteration]], ""),
caption = ifelse(typeof(individual.captions) == "character", individual.captions[[count_iteration]], ""))
}
# Patch for diffusion maps.
if (stringr::str_starts(reduction, "diff|DIFF")){
# Add "DC" labels.
p.loop <- p.loop &
ggplot2::xlab(paste0("DC_", dims[1])) &
ggplot2::ylab(paste0("DC_", dims[2]))
} else if (reduction == "pca"){
p.loop <- p.loop &
ggplot2::xlab(paste0("PC_", dims[1])) &
ggplot2::ylab(paste0("PC_", dims[2]))
}
# Add the plot to the list.
list.plots[[feature]] <- p.loop
}
# Generate the final plot with patchwork and use the "ncol" parameter value for the number of columns.
p <- patchwork::wrap_plots(list.plots, nrow = 1)
}
# Fix the extra space and add theme parameters.
p <- p &
ggplot2::theme_minimal(base_size = font.size) &
ggplot2::theme(plot.margin = ggplot2::margin(t = 0, r = 0, b = 0, l = 0),
plot.title = ggplot2::element_text(face = plot.title.face,
hjust = ifelse(!(is.null(split.by)), 0.5, 0)),
plot.subtitle = ggplot2::element_text(face = plot.subtitle.face, hjust = 0),
plot.caption = ggplot2::element_text(face = plot.caption.face, hjust = 1),
panel.grid = ggplot2::element_blank(),
plot.title.position = "plot",
plot.caption.position = "plot",
text = ggplot2::element_text(family = font.type),
legend.position = legend.position,
legend.text = ggplot2::element_text(face = legend.text.face),
legend.title = ggplot2::element_text(face = legend.title.face),
legend.justification = "center",
plot.background = ggplot2::element_rect(fill = "white", color = "white"),
panel.background = ggplot2::element_rect(fill = "white", color = "white"),
legend.background = ggplot2::element_rect(fill = "white", color = "white"))
if (is.null(split.by) & legend.position != "none"){
counter <- 0
for (feature in features){
counter <- counter + 1
p[[counter]] <- modify_continuous_legend(p = p[[counter]],
legend.title = legend.title,
legend.aes = "color",
legend.type = legend.type,
legend.position = legend.position,
legend.length = legend.length,
legend.width = legend.width,
legend.framecolor = legend.framecolor,
legend.tickcolor = legend.tickcolor,
legend.framewidth = legend.framewidth,
legend.tickwidth = legend.tickwidth)
}
}
# Add custom title.
if (!is.null(plot.title)){
if (length(features) > 1 | !(is.null(split.by)) | !(is.null(cells.highlight)) | !(is.null(idents.highlight))){
p <- p +
patchwork::plot_annotation(title = plot.title)
} else {
p <- p +
ggplot2::labs(title = plot.title)
}
}
# Add custom subtitle.
if (!is.null(plot.subtitle)){
if (length(features) > 1 | !(is.null(split.by)) | !(is.null(cells.highlight)) | !(is.null(idents.highlight))){
p <- p +
patchwork::plot_annotation(subtitle = plot.subtitle)
} else {
p <- p +
ggplot2::labs(subtitle = plot.subtitle)
}
}
# Add custom caption
if (!is.null(plot.caption)){
if (length(features) > 1 | !(is.null(split.by)) | !(is.null(cells.highlight)) | !(is.null(idents.highlight))){
p <- p +
patchwork::plot_annotation(caption = plot.caption)
} else {
p <- p +
ggplot2::labs(caption = plot.caption)
}
}
# Add individual titles.
if (!is.null(individual.titles)){
for (counter in seq(1, length(features))){
if (!(is.na(individual.titles[counter]))){
if (is.null(split.by)){
p[[counter]]$labels$title <- individual.titles[counter]
}
}
}
}
# Add individual subtitles.
if (!is.null(individual.subtitles)){
for (counter in seq(1, length(features))){
if (!(is.na(individual.subtitles[counter]))){
if (is.null(split.by)){
p[[counter]]$labels$subtitle <- individual.subtitles[counter]
}
}
}
}
# Add individual captions
if (!is.null(individual.captions)){
for (counter in seq(1, length(features))){
if (!(is.na(individual.captions[counter]))){
if (is.null(split.by)){
p[[counter]]$labels$caption <- individual.captions[counter]
}
}
}
}
if (base::isFALSE(plot.axes)){
p <- p &
ggplot2::theme(axis.title = ggplot2::element_blank(),
axis.text = ggplot2::element_blank(),
axis.ticks = ggplot2::element_blank(),
axis.line = ggplot2::element_blank())
} else {
p <- p &
ggplot2::theme(axis.title = ggplot2::element_text(face = axis.title.face),
axis.text = ggplot2::element_text(face = axis.text.face),
axis.ticks = ggplot2::element_line(color = "black"),
axis.line = ggplot2::element_line(color = "black"))
}
# Further patch for diffusion maps.
if (stringr::str_starts(reduction, "diff|DIFF")){
labels <- colnames(sample@reductions[[reduction]][[]])[dims]
# Fix the axis scale so that the highest and lowest values are in the range of the DCs (previously was around +-1.5, while DCs might range to +-0.004 or so).
p <- suppressMessages({
p &
ggplot2::xlim(c(min(sample@reductions[[reduction]][[]][, labels[1]], na.rm = TRUE),
max(sample@reductions[[reduction]][[]][, labels[1]], na.rm = TRUE))) &
ggplot2::ylim(c(min(sample@reductions[[reduction]][[]][, labels[2]], na.rm = TRUE),
max(sample@reductions[[reduction]][[]][, labels[2]], na.rm = TRUE)))
})
}
# Add legend titles.
if (is.null(split.by) & is.null(legend.title)){
counter <- 0
for (feature in features){
counter <- counter + 1
p[[counter]]$guides$colour$title <- feature
}
}
# Return the plot.
return(p)
}
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