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visualization.R
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#' @importFrom utils globalVariables
#' @importFrom ggplot2 ggproto GeomViolin
#'
NULL
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Heatmaps
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' Dimensional reduction heatmap
#'
#' Draws a heatmap focusing on a principal component. Both cells and genes are sorted by their
#' principal component scores. Allows for nice visualization of sources of heterogeneity in the dataset.
#'
#' @inheritParams DoHeatmap
#' @param dims Dimensions to plot
#' @param nfeatures Number of genes to plot
#' @param cells A list of cells to plot. If numeric, just plots the top cells.
#' @param reduction Which dimmensional reduction to use
#' @param balanced Plot an equal number of genes with both + and - scores.
#' @param projected Use the full projected dimensional reduction
#' @param ncol Number of columns to plot
#' @param fast If true, use \code{image} to generate plots; faster than using ggplot2, but not customizable
#' @param assays A vector of assays to pull data from
#'
#' @return No return value by default. If using fast = FALSE, will return a ggplot object.
#'
#' @export
#'
#' @seealso \code{\link[graphics]{image}} \code{\link[ggplot2]{geom_raster}}
#'
#' @examples
#' DimHeatmap(object = pbmc_small)
#'
DimHeatmap <- function(
object,
dims = 1,
nfeatures = 30,
cells = NULL,
reduction = 'pca',
disp.min = -2.5,
disp.max = NULL,
balanced = TRUE,
projected = FALSE,
ncol = NULL,
combine = TRUE,
fast = TRUE,
raster = TRUE,
slot = 'scale.data',
assays = NULL
) {
ncol <- ncol %||% ifelse(test = length(x = dims) > 2, yes = 3, no = length(x = dims))
plots <- vector(mode = 'list', length = length(x = dims))
assays <- assays %||% DefaultAssay(object = object)
disp.max <- disp.max %||% ifelse(
test = slot == 'scale.data',
yes = 2.5,
no = 6
)
if (!DefaultAssay(object = object[[reduction]]) %in% assays) {
warning("The original assay that the reduction was computed on is different than the assay specified")
}
cells <- cells %||% ncol(x = object)
if (is.numeric(x = cells)) {
cells <- lapply(
X = dims,
FUN = function(x) {
cells <- TopCells(
object = object[[reduction]],
dim = x,
ncells = cells,
balanced = balanced
)
if (balanced) {
cells$negative <- rev(x = cells$negative)
}
cells <- unlist(x = unname(obj = cells))
return(cells)
}
)
}
if (!is.list(x = cells)) {
cells <- lapply(X = 1:length(x = dims), FUN = function(x) {return(cells)})
}
features <- lapply(
X = dims,
FUN = TopFeatures,
object = object[[reduction]],
nfeatures = nfeatures,
balanced = balanced,
projected = projected
)
features.all <- unique(x = unlist(x = features))
if (length(x = assays) > 1) {
features.keyed <- lapply(
X = assays,
FUN = function(assay) {
features <- features.all[features.all %in% rownames(x = object[[assay]])]
if (length(x = features) > 0) {
return(paste0(Key(object = object[[assay]]), features))
}
}
)
features.keyed <- Filter(f = Negate(f = is.null), x = features.keyed)
features.keyed <- unlist(x = features.keyed)
} else {
features.keyed <- features.all
DefaultAssay(object = object) <- assays
}
data.all <- FetchData(
object = object,
vars = features.keyed,
cells = unique(x = unlist(x = cells)),
slot = slot
)
data.all <- MinMax(data = data.all, min = disp.min, max = disp.max)
data.limits <- c(min(data.all), max(data.all))
# if (check.plot && any(c(length(x = features.keyed), length(x = cells[[1]])) > 700)) {
# choice <- menu(c("Continue with plotting", "Quit"), title = "Plot(s) requested will likely take a while to plot.")
# if (choice != 1) {
# return(invisible(x = NULL))
# }
# }
if (fast) {
nrow <- floor(x = length(x = dims) / 3.01) + 1
orig.par <- par()$mfrow
par(mfrow = c(nrow, ncol))
}
for (i in 1:length(x = dims)) {
dim.features <- c(features[[i]][[2]], rev(x = features[[i]][[1]]))
dim.features <- rev(x = unlist(x = lapply(
X = dim.features,
FUN = function(feat) {
return(grep(pattern = paste0(feat, '$'), x = features.keyed, value = TRUE))
}
)))
dim.cells <- cells[[i]]
data.plot <- data.all[dim.cells, dim.features]
if (fast) {
SingleImageMap(
data = data.plot,
title = paste0(Key(object = object[[reduction]]), dims[i]),
order = dim.cells
)
} else {
plots[[i]] <- SingleRasterMap(
data = data.plot,
raster = raster,
limits = data.limits,
cell.order = dim.cells,
feature.order = dim.features
)
}
}
if (fast) {
par(mfrow = orig.par)
return(invisible(x = NULL))
}
if (combine) {
plots <- CombinePlots(
plots = plots,
ncol = ncol,
legend = 'right'
)
}
return(plots)
}
#' Feature expression heatmap
#'
#' Draws a heatmap of single cell feature expression.
#'
#' @param object Seurat object
#' @param features A vector of features to plot, defaults to \code{VariableFeatures(object = object)}
#' @param cells A vector of cells to plot
#' @param disp.min Minimum display value (all values below are clipped)
#' @param disp.max Maximum display value (all values above are clipped); defaults to 2.5
#' if \code{slot} is 'scale.data', 6 otherwise
#' @param group.by A vector of variables to group cells by; pass 'ident' to group by cell identity classes
#' @param group.bar Add a color bar showing group status for cells
#' @param group.colors Colors to use for the color bar
#' @param slot Data slot to use, choose from 'raw.data', 'data', or 'scale.data'
#' @param assay Assay to pull from
# @param check.plot Check that plotting will finish in a reasonable amount of time
#' @param label Label the cell identies above the color bar
#' @param size Size of text above color bar
#' @param hjust Horizontal justification of text above color bar
#' @param angle Angle of text above color bar
#' @param raster If true, plot with geom_raster, else use geom_tile. geom_raster may look blurry on
#' some viewing applications such as Preview due to how the raster is interpolated. Set this to FALSE
#' if you are encountering that issue (note that plots may take longer to produce/render).
#' @param draw.lines Include white lines to separate the groups
#' @param lines.width Integer number to adjust the width of the separating white lines.
#' Corresponds to the number of "cells" between each group.
#' @param group.bar.height Scale the height of the color bar
#' @param combine Combine plots into a single gg object; note that if TRUE; themeing will not work
#' when plotting multiple dimensions
#'
#' @return A ggplot object
#'
#' @importFrom stats median
#' @importFrom scales hue_pal
#' @importFrom ggplot2 annotation_raster coord_cartesian scale_color_manual
#' ggplot_build aes_string
#' @export
#'
#' @examples
#' DoHeatmap(object = pbmc_small)
#'
DoHeatmap <- function(
object,
features = NULL,
cells = NULL,
group.by = 'ident',
group.bar = TRUE,
group.colors = NULL,
disp.min = -2.5,
disp.max = NULL,
slot = 'scale.data',
assay = NULL,
label = TRUE,
size = 5.5,
hjust = 0,
angle = 45,
raster = TRUE,
draw.lines = TRUE,
lines.width = NULL,
group.bar.height = 0.02,
combine = TRUE
) {
cells <- cells %||% colnames(x = object)
if (is.numeric(x = cells)) {
cells <- colnames(x = object)[cells]
}
assay <- assay %||% DefaultAssay(object = object)
DefaultAssay(object = object) <- assay
features <- features %||% VariableFeatures(object = object)
features <- rev(x = unique(x = features))
disp.max <- disp.max %||% ifelse(
test = slot == 'scale.data',
yes = 2.5,
no = 6
)
# make sure features are present
possible.features <- rownames(x = GetAssayData(object = object, slot = slot))
if (any(!features %in% possible.features)) {
bad.features <- features[!features %in% possible.features]
features <- features[features %in% possible.features]
if(length(x = features) == 0) {
stop("No requested features found in the ", slot, " slot for the ", assay, " assay.")
}
warning("The following features were omitted as they were not found in the ", slot,
" slot for the ", assay, " assay: ", paste(bad.features, collapse = ", "))
}
data <- as.data.frame(x = as.matrix(x = t(x = GetAssayData(
object = object,
slot = slot)[features, cells, drop = FALSE])))
object <- suppressMessages(expr = StashIdent(object = object, save.name = 'ident'))
group.by <- group.by %||% 'ident'
groups.use <- object[[group.by]][cells, , drop = FALSE]
# group.use <- switch(
# EXPR = group.by,
# 'ident' = Idents(object = object),
# object[[group.by, drop = TRUE]]
# )
# group.use <- factor(x = group.use[cells])
plots <- vector(mode = 'list', length = ncol(x = groups.use))
for (i in 1:ncol(x = groups.use)) {
data.group <- data
group.use <- groups.use[, i, drop = TRUE]
if (!is.factor(x = group.use)) {
group.use <- factor(x = group.use)
}
names(x = group.use) <- cells
if (draw.lines) {
# create fake cells to serve as the white lines, fill with NAs
lines.width <- lines.width %||% ceiling(x = nrow(x = data.group) * 0.0025)
placeholder.cells <- sapply(
X = 1:(length(x = levels(x = group.use)) * lines.width),
FUN = function(x) {
return(RandomName(length = 20))
}
)
placeholder.groups <- rep(x = levels(x = group.use), times = lines.width)
group.levels <- levels(x = group.use)
names(x = placeholder.groups) <- placeholder.cells
group.use <- as.vector(x = group.use)
names(x = group.use) <- cells
group.use <- factor(x = c(group.use, placeholder.groups), levels = group.levels)
na.data.group <- matrix(
data = NA,
nrow = length(x = placeholder.cells),
ncol = ncol(x = data.group),
dimnames = list(placeholder.cells, colnames(x = data.group))
)
data.group <- rbind(data.group, na.data.group)
}
lgroup <- length(levels(group.use))
plot <- SingleRasterMap(
data = data.group,
raster = raster,
disp.min = disp.min,
disp.max = disp.max,
feature.order = features,
cell.order = names(x = sort(x = group.use)),
group.by = group.use
)
if (group.bar) {
# TODO: Change group.bar to annotation.bar
default.colors <- c(hue_pal()(length(x = levels(x = group.use))))
cols <- group.colors[1:length(x = levels(x = group.use))] %||% default.colors
if (any(is.na(x = cols))) {
cols[is.na(x = cols)] <- default.colors[is.na(x = cols)]
cols <- Col2Hex(cols)
col.dups <- sort(x = unique(x = which(x = duplicated(x = substr(
x = cols,
start = 1,
stop = 7
)))))
through <- length(x = default.colors)
while (length(x = col.dups) > 0) {
pal.max <- length(x = col.dups) + through
cols.extra <- hue_pal()(pal.max)[(through + 1):pal.max]
cols[col.dups] <- cols.extra
col.dups <- sort(x = unique(x = which(x = duplicated(x = substr(
x = cols,
start = 1,
stop = 7
)))))
}
}
group.use2 <- sort(x = group.use)
if (draw.lines) {
na.group <- RandomName(length = 20)
levels(x = group.use2) <- c(levels(x = group.use2), na.group)
group.use2[placeholder.cells] <- na.group
cols <- c(cols, "#FFFFFF")
}
pbuild <- ggplot_build(plot = plot)
names(x = cols) <- levels(x = group.use2)
# scale the height of the bar
y.range <- diff(x = pbuild$layout$panel_params[[1]]$y.range)
y.pos <- max(pbuild$layout$panel_params[[1]]$y.range) + y.range * 0.015
y.max <- y.pos + group.bar.height * y.range
plot <- plot +
annotation_raster(
raster = t(x = cols[group.use2]),
xmin = -Inf,
xmax = Inf,
ymin = y.pos,
ymax = y.max
) +
coord_cartesian(ylim = c(0, y.max), clip = 'off') +
scale_color_manual(values = cols)
if (label) {
x.max <- max(pbuild$layout$panel_params[[1]]$x.range)
x.divs <- pbuild$layout$panel_params[[1]]$x.major
x <- data.frame(group = sort(x = group.use), x = x.divs)
label.x.pos <- tapply(X = x$x, INDEX = x$group, FUN = median) * x.max
label.x.pos <- data.frame(group = names(x = label.x.pos), label.x.pos)
plot <- plot + geom_text(
stat = "identity",
data = label.x.pos,
aes_string(label = 'group', x = 'label.x.pos'),
y = y.max + y.max * 0.03 * 0.5,
angle = angle,
hjust = hjust,
size = size
)
plot <- suppressMessages(plot + coord_cartesian(
ylim = c(0, y.max + y.max * 0.002 * max(nchar(x = levels(x = group.use))) * size),
clip = 'off')
)
}
}
plot <- plot + theme(line = element_blank())
plots[[i]] <- plot
}
if (combine) {
plots <- CombinePlots(plots = plots)
}
return(plots)
}
#' Hashtag oligo heatmap
#'
#' Draws a heatmap of hashtag oligo signals across singlets/doublets/negative cells. Allows for the visualization of HTO demultiplexing results.
#'
#' @param object Seurat object. Assumes that the hash tag oligo (HTO) data has been added and normalized, and demultiplexing has been run with HTODemux().
#' @param classification The naming for metadata column with classification result from HTODemux().
#' @param global.classification The slot for metadata column specifying a cell as singlet/doublet/negative.
#' @param assay Hashtag assay name.
#' @param ncells Number of cells to plot. Default is to choose 5000 cells by random subsampling, to avoid having to draw exceptionally large heatmaps.
#' @param singlet.names Namings for the singlets. Default is to use the same names as HTOs.
#' @param raster If true, plot with geom_raster, else use geom_tile. geom_raster may look blurry on
#' some viewing applications such as Preview due to how the raster is interpolated. Set this to FALSE
#' if you are encountering that issue (note that plots may take longer to produce/render).
#' @return Returns a ggplot2 plot object.
#'
#' @importFrom ggplot2 guides
#' @export
#'
#' @seealso \code{\link{HTODemux}}
#'
#' @examples
#' \dontrun{
#' object <- HTODemux(object)
#' HTOHeatmap(object)
#' }
#'
HTOHeatmap <- function(
object,
assay = 'HTO',
classification = paste0(assay, '_classification'),
global.classification = paste0(assay, '_classification.global'),
ncells = 5000,
singlet.names = NULL,
raster = TRUE
) {
DefaultAssay(object = object) <- assay
Idents(object = object) <- object[[classification, drop = TRUE]]
if (ncells > ncol(x = object)) {
warning("ncells (", ncells, ") is larger than the number of cells present in the provided object (", ncol(x = object), "). Plotting heatmap for all cells.")
} else {
object <- subset(
x = object,
cells = sample(x = colnames(x = object), size = ncells)
)
}
classification <- object[[classification]]
singlets <- which(x = object[[global.classification]] == 'Singlet')
singlet.ids <- sort(x = unique(x = as.character(x = classification[singlets, ])))
doublets <- which(object[[global.classification]] == 'Doublet')
doublet.ids <- sort(x = unique(x = as.character(x = classification[doublets, ])))
heatmap.levels <- c(singlet.ids, doublet.ids, 'Negative')
object <- ScaleData(object = object, assay = assay, verbose = FALSE)
data <- FetchData(object = object, vars = singlet.ids)
Idents(object = object) <- factor(x = classification[, 1], levels = heatmap.levels)
plot <- SingleRasterMap(
data = data,
raster = raster,
feature.order = rev(x = singlet.ids),
cell.order = names(x = sort(x = Idents(object = object))),
group.by = Idents(object = object)
) + guides(color = FALSE)
return(plot)
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Expression by identity plots
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' Single cell ridge plot
#'
#' Draws a ridge plot of single cell data (gene expression, metrics, PC
#' scores, etc.)
#'
#' @param object Seurat object
#' @param features Features to plot (gene expression, metrics, PC scores,
#' anything that can be retreived by FetchData)
#' @param cols Colors to use for plotting
#' @param idents Which classes to include in the plot (default is all)
#' @param sort Sort identity classes (on the x-axis) by the average
#' expression of the attribute being potted, can also pass 'increasing' or 'decreasing' to change sort direction
#' @param assay Name of assay to use, defaults to the active assay
#' @param group.by Group (color) cells in different ways (for example, orig.ident)
#' @param y.max Maximum y axis value
#' @param same.y.lims Set all the y-axis limits to the same values
#' @param log plot the feature axis on log scale
#' @param ncol Number of columns if multiple plots are displayed
#' @param combine Combine plots into a single gg object; note that if TRUE; themeing will not work when plotting multiple features
#' @param slot Use non-normalized counts data for plotting
#' @param ... Extra parameters passed on to \code{\link{CombinePlots}}
#'
#' @return A ggplot object
#'
#' @export
#'
#' @examples
#' RidgePlot(object = pbmc_small, features = 'PC_1')
#'
RidgePlot <- function(
object,
features,
cols = NULL,
idents = NULL,
sort = FALSE,
assay = NULL,
group.by = NULL,
y.max = NULL,
same.y.lims = FALSE,
log = FALSE,
ncol = NULL,
combine = TRUE,
slot = 'data',
...
) {
return(ExIPlot(
object = object,
type = 'ridge',
features = features,
idents = idents,
ncol = ncol,
sort = sort,
assay = assay,
y.max = y.max,
same.y.lims = same.y.lims,
cols = cols,
group.by = group.by,
log = log,
combine = combine,
slot = slot,
...
))
}
#' Single cell violin plot
#'
#' Draws a violin plot of single cell data (gene expression, metrics, PC
#' scores, etc.)
#'
#' @inheritParams RidgePlot
#' @param pt.size Point size for geom_violin
#' @param split.by A variable to split the violin plots by,
#' @param multi.group plot each group of the split violin plots by multiple or single violin shapes
#' see \code{\link{FetchData}} for more details
#' @param adjust Adjust parameter for geom_violin
#'
#' @return A ggplot object
#'
#' @export
#'
#' @seealso \code{\link{FetchData}}
#'
#' @examples
#' VlnPlot(object = pbmc_small, features = 'PC_1')
#' VlnPlot(object = pbmc_small, features = 'LYZ', split.by = 'groups')
#'
VlnPlot <- function(
object,
features,
cols = NULL,
pt.size = 1,
idents = NULL,
sort = FALSE,
assay = NULL,
group.by = NULL,
split.by = NULL,
adjust = 1,
y.max = NULL,
same.y.lims = FALSE,
log = FALSE,
ncol = NULL,
combine = TRUE,
slot = 'data',
multi.group = FALSE,
...
) {
return(ExIPlot(
object = object,
type = ifelse(test = multi.group, yes = 'multiViolin', no = 'violin'),
features = features,
idents = idents,
ncol = ncol,
sort = sort,
assay = assay,
y.max = y.max,
same.y.lims = same.y.lims,
adjust = adjust,
pt.size = pt.size,
cols = cols,
group.by = group.by,
split.by = split.by,
log = log,
slot = slot,
combine = combine,
...
))
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Dimensional reduction plots
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' Color dimensional reduction plot by tree split
#'
#' Returns a DimPlot colored based on whether the cells fall in clusters
#' to the left or to the right of a node split in the cluster tree.
#'
#' @param object Seurat object
#' @param node Node in cluster tree on which to base the split
#' @param left.color Color for the left side of the split
#' @param right.color Color for the right side of the split
#' @param other.color Color for all other cells
#' @inheritDotParams DimPlot -object
#'
#' @return Returns a DimPlot
#'
#' @export
#'
#' @examples
#' pbmc_small
#' pbmc_small <- BuildClusterTree(object = pbmc_small, verbose = FALSE)
#' PlotClusterTree(pbmc_small)
#' ColorDimSplit(pbmc_small, node = 5)
#'
ColorDimSplit <- function(
object,
node,
left.color = 'red',
right.color = 'blue',
other.color = 'grey50',
...
) {
CheckDots(..., fxns = 'DimPlot')
tree <- Tool(object = object, slot = "BuildClusterTree")
split <- tree$edge[which(x = tree$edge[, 1] == node), ][, 2]
all.children <- sort(x = tree$edge[, 2][! tree$edge[, 2] %in% tree$edge[, 1]])
left.group <- DFT(tree = tree, node = split[1], only.children = TRUE)
right.group <- DFT(tree = tree, node = split[2], only.children = TRUE)
if (any(is.na(x = left.group))) {
left.group <- split[1]
}
if (any(is.na(x = right.group))) {
right.group <- split[2]
}
left.group <- MapVals(v = left.group, from = all.children, to = tree$tip.label)
right.group <- MapVals(v = right.group, from = all.children, to = tree$tip.label)
remaining.group <- setdiff(x = tree$tip.label, y = c(left.group, right.group))
left.cells <- WhichCells(object = object, ident = left.group)
right.cells <- WhichCells(object = object, ident = right.group)
remaining.cells <- WhichCells(object = object, ident = remaining.group)
object <- SetIdent(
object = object,
cells = left.cells,
value = "Left Split"
)
object <- SetIdent(
object = object,
cells = right.cells,
value = "Right Split"
)
object <- SetIdent(
object = object,
cells = remaining.cells,
value = "Not in Split"
)
levels(x = object) <- c("Left Split", "Right Split", "Not in Split")
colors.use = c(left.color, right.color, other.color)
return(DimPlot(object = object, cols = colors.use, ...))
}
#' Dimensional reduction plot
#'
#' Graphs the output of a dimensional reduction technique on a 2D scatter plot where each point is a
#' cell and it's positioned based on the cell embeddings determined by the reduction technique. By
#' default, cells are colored by their identity class (can be changed with the group.by parameter).
#'
#' @param object Seurat object
#' @param dims Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions
#' @param cells Vector of cells to plot (default is all cells)
#' @param cols Vector of colors, each color corresponds to an identity class. This may also be a single character
#' or numeric value corresponding to a palette as specified by \code{\link[RColorBrewer]{brewer.pal.info}}.
#' By default, ggplot2 assigns colors. We also include a number of palettes from the pals package.
#' See \code{\link{DiscretePalette}} for details.
#' @param pt.size Adjust point size for plotting
#' @param reduction Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca
#' @param group.by Name of one or more metadata columns to group (color) cells by
#' (for example, orig.ident); pass 'ident' to group by identity class
#' @param split.by Name of a metadata column to split plot by;
#' see \code{\link{FetchData}} for more details
#' @param shape.by If NULL, all points are circles (default). You can specify any
#' cell attribute (that can be pulled with FetchData) allowing for both
#' different colors and different shapes on cells
#' @param order Specify the order of plotting for the idents. This can be
#' useful for crowded plots if points of interest are being buried. Provide
#' either a full list of valid idents or a subset to be plotted last (on top)
#' @param label Whether to label the clusters
#' @param label.size Sets size of labels
#' @param repel Repel labels
#' @param cells.highlight A list of character or numeric vectors of cells to
#' highlight. If only one group of cells desired, can simply
#' pass a vector instead of a list. If set, colors selected cells to the color(s)
#' in \code{cols.highlight} and other cells black (white if dark.theme = TRUE);
#' will also resize to the size(s) passed to \code{sizes.highlight}
#' @param cols.highlight A vector of colors to highlight the cells as; will
#' repeat to the length groups in cells.highlight
#' @param sizes.highlight Size of highlighted cells; will repeat to the length
#' groups in cells.highlight
#' @param na.value Color value for NA points when using custom scale
#' @param combine Combine plots into a single gg object; note that if TRUE; themeing will not work when plotting multiple features
#' @param ncol Number of columns for display when combining plots
#' @param ... Extra parameters passed on to \code{\link{CombinePlots}}
#'
#' @return A ggplot object
#'
#' @importFrom rlang !!
#' @importFrom ggplot2 facet_wrap vars sym
#'
#' @export
#'
#' @note For the old \code{do.hover} and \code{do.identify} functionality, please see
#' \code{HoverLocator} and \code{CellSelector}, respectively.
#'
#' @aliases TSNEPlot PCAPlot ICAPlot
#' @seealso \code{\link{FeaturePlot}} \code{\link{HoverLocator}}
#' \code{\link{CellSelector}} \code{link{FetchData}}
#'
#' @examples
#' DimPlot(object = pbmc_small)
#' DimPlot(object = pbmc_small, split.by = 'ident')
#'
DimPlot <- function(
object,
dims = c(1, 2),
cells = NULL,
cols = NULL,
pt.size = NULL,
reduction = NULL,
group.by = NULL,
split.by = NULL,
shape.by = NULL,
order = NULL,
label = FALSE,
label.size = 4,
repel = FALSE,
cells.highlight = NULL,
cols.highlight = '#DE2D26',
sizes.highlight = 1,
na.value = 'grey50',
combine = TRUE,
ncol = NULL,
...
) {
CheckDots(..., fxns = 'CombinePlots')
if (length(x = dims) != 2) {
stop("'dims' must be a two-length vector")
}
reduction <- reduction %||% DefaultDimReduc(object = object)
cells <- cells %||% colnames(x = object)
data <- Embeddings(object = object[[reduction]])[cells, dims]
data <- as.data.frame(x = data)
dims <- paste0(Key(object = object[[reduction]]), dims)
object[['ident']] <- Idents(object = object)
group.by <- group.by %||% 'ident'
data[, group.by] <- object[[group.by]][cells, , drop = FALSE]
for (group in group.by) {
if (!is.factor(x = data[, group])) {
data[, group] <- factor(x = data[, group])
}
}
if (!is.null(x = shape.by)) {
data[, shape.by] <- object[[shape.by, drop = TRUE]]
}
if (!is.null(x = split.by)) {
data[, split.by] <- object[[split.by, drop = TRUE]]
}
plots <- lapply(
X = group.by,
FUN = function(x) {
plot <- SingleDimPlot(
data = data[, c(dims, x, split.by, shape.by)],
dims = dims,
col.by = x,
cols = cols,
pt.size = pt.size,
shape.by = shape.by,
order = order,
label = FALSE,
cells.highlight = cells.highlight,
cols.highlight = cols.highlight,
sizes.highlight = sizes.highlight,
na.value = na.value
)
if (label) {
plot <- LabelClusters(
plot = plot,
id = x,
repel = repel,
size = label.size,
split.by = split.by
)
}
if (!is.null(x = split.by)) {
plot <- plot + FacetTheme() +
facet_wrap(
facets = vars(!!sym(x = split.by)),
ncol = if (length(x = group.by) > 1 || is.null(x = ncol)) {
length(x = unique(x = data[, split.by]))
} else {
ncol
}
)
}
return(plot)
}
)
if (combine) {
plots <- CombinePlots(
plots = plots,
ncol = if (!is.null(x = split.by) && length(x = group.by) > 1) {
1
} else {
ncol
},
...
)
}
return(plots)
}
#' Visualize 'features' on a dimensional reduction plot
#'
#' Colors single cells on a dimensional reduction plot according to a 'feature'
#' (i.e. gene expression, PC scores, number of genes detected, etc.)
#'
#' @inheritParams DimPlot
#' @param order Boolean determining whether to plot cells in order of expression. Can be useful if
#' cells expressing given feature are getting buried.
#' @param features Vector of features to plot. Features can come from:
#' \itemize{
#' \item An \code{Assay} feature (e.g. a gene name - "MS4A1")
#' \item A column name from meta.data (e.g. mitochondrial percentage - "percent.mito")
#' \item A column name from a \code{DimReduc} object corresponding to the cell embedding values
#' (e.g. the PC 1 scores - "PC_1")
#' }
#' @param cols The two colors to form the gradient over. Provide as string vector with
#' the first color corresponding to low values, the second to high. Also accepts a Brewer
#' color scale or vector of colors. Note: this will bin the data into number of colors provided.
#' When blend is \code{TRUE}, takes anywhere from 1-3 colors:
#' \describe{
#' \item{1 color:}{Treated as color for double-negatives, will use default colors 2 and 3 for per-feature expression}
#' \item{2 colors:}{Treated as colors for per-feature expression, will use default color 1 for double-negatives}
#' \item{3+ colors:}{First color used for double-negatives, colors 2 and 3 used for per-feature expression, all others ignored}
#' }
#' @param min.cutoff,max.cutoff Vector of minimum and maximum cutoff values for each feature,
#' may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10')
#' @param split.by A factor in object metadata to split the feature plot by, pass 'ident'
#' to split by cell identity'; similar to the old \code{FeatureHeatmap}
#' @param slot Which slot to pull expression data from?
#' @param blend Scale and blend expression values to visualize coexpression of two features
#' @param blend.threshold The color cutoff from weak signal to strong signal; ranges from 0 to 1.
#' @param ncol Number of columns to combine multiple feature plots to, ignored if \code{split.by} is not \code{NULL}
#' @param combine Combine plots into a single gg object; note that if \code{TRUE}; themeing will not work when plotting multiple features
#' @param coord.fixed Plot cartesian coordinates with fixed aspect ratio
#' @param by.col If splitting by a factor, plot the splits per column with the features as rows; ignored if \code{blend = TRUE}
#' @param sort.cell If \code{TRUE}, the positive cells will overlap the negative cells
#'
#' @return Returns a ggplot object if only 1 feature is plotted.
#' If >1 features are plotted and \code{combine=TRUE}, returns a combined ggplot object using \code{cowplot::plot_grid}.
#' If >1 features are plotted and \code{combine=FALSE}, returns a list of ggplot objects.
#'
#' @importFrom grDevices rgb
#' @importFrom cowplot theme_cowplot
#' @importFrom RColorBrewer brewer.pal.info
#' @importFrom ggplot2 labs scale_x_continuous scale_y_continuous theme element_rect
#' dup_axis guides element_blank element_text margin scale_color_brewer scale_color_gradientn
#' scale_color_manual coord_fixed ggtitle
#'
#' @export
#'
#' @note For the old \code{do.hover} and \code{do.identify} functionality, please see
#' \code{HoverLocator} and \code{CellSelector}, respectively.
#'
#' @aliases FeatureHeatmap
#' @seealso \code{\link{DimPlot}} \code{\link{HoverLocator}}
#' \code{\link{CellSelector}}
#'
#' @examples
#' FeaturePlot(object = pbmc_small, features = 'PC_1')
#'
FeaturePlot <- function(
object,
features,
dims = c(1, 2),
cells = NULL,
cols = if (blend) {
c('lightgrey', '#ff0000', '#00ff00')
} else {
c('lightgrey', 'blue')
},
pt.size = NULL,
order = FALSE,
min.cutoff = NA,
max.cutoff = NA,
reduction = NULL,
split.by = NULL,
shape.by = NULL,
slot = 'data',
blend = FALSE,
blend.threshold = 0.5,
label = FALSE,
label.size = 4,
repel = FALSE,
ncol = NULL,
combine = TRUE,
coord.fixed = FALSE,
by.col = TRUE,
sort.cell = FALSE
) {
# Set a theme to remove right-hand Y axis lines
# Also sets right-hand Y axis text label formatting
no.right <- theme(
axis.line.y.right = element_blank(),
axis.ticks.y.right = element_blank(),
axis.text.y.right = element_blank(),
axis.title.y.right = element_text(
face = "bold",
size = 14,
margin = margin(r = 7)
)
)
# Get the DimReduc to use
reduction <- reduction %||% DefaultDimReduc(object = object)
if (length(x = dims) != 2 || !is.numeric(x = dims)) {
stop("'dims' must be a two-length integer vector")
}
# Figure out blending stuff
if (blend && length(x = features) != 2) {
stop("Blending feature plots only works with two features")
}
# Set color scheme for blended FeaturePlots
if (blend) {
default.colors <- eval(expr = formals(fun = FeaturePlot)$cols)
cols <- switch(
EXPR = as.character(x = length(x = cols)),
'0' = {
warning(
"No colors provided, using default colors",
call. = FALSE,
immediate. = TRUE
)
default.colors
},
'1' = {
warning(
"Only one color provided, assuming specified is double-negative and augmenting with default colors",
call. = FALSE,
immediate. = TRUE
)
c(cols, default.colors[2:3])
},
'2' = {
warning(
"Only two colors provided, assuming specified are for features and agumenting with '",
default.colors[1],
"' for double-negatives",
call. = FALSE,
immediate. = TRUE
)
c(default.colors[1], cols)
},
'3' = cols,
{
warning(
"More than three colors provided, using only first three",
call. = FALSE,
immediate. = TRUE
)
cols[1:3]
}
)
}
if (blend && length(x = cols) != 3) {
stop("Blending feature plots only works with three colors; first one for negative cells")
}
# Name the reductions
dims <- paste0(Key(object = object[[reduction]]), dims)
cells <- cells %||% colnames(x = object)
# Get plotting data
data <- FetchData(
object = object,
vars = c(dims, 'ident', features),
cells = cells,
slot = slot
)
# Check presence of features/dimensions
if (ncol(x = data) < 4) {
stop(
"None of the requested features were found: ",
paste(features, collapse = ', '),
" in slot ",
slot,
call. = FALSE
)
} else if (!all(dims %in% colnames(x = data))) {
stop("The dimensions requested were not found", call. = FALSE)
}
features <- colnames(x = data)[4:ncol(x = data)]
# Determine cutoffs
min.cutoff <- mapply(
FUN = function(cutoff, feature) {
return(ifelse(
test = is.na(x = cutoff),
yes = min(data[, feature]),
no = cutoff
))
},
cutoff = min.cutoff,