#' @importFrom utils globalVariables
#' @importFrom ggplot2 fortify GeomViolin ggproto
#' @importFrom SeuratObject DefaultDimReduc
#'
NULL

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Generics
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

#' @importFrom methods setGeneric
#'
setGeneric(
  name = '.PrepImageData',
  def = function(data, cells, ...) {
    standardGeneric(f = '.PrepImageData')
  }
)

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# 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 dimensional 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 and excludes figure legend in output
#' @param assays A vector of assays to pull data from
#' @param combine Combine plots into a single \code{\link[patchwork]{patchwork}ed} ggplot object with 
#' single shared figure legend when \code{fast=FALSE}. If \code{FALSE}, return a list of ggplot objects
#' @param legend.position When \code{combine=TRUE}, allows legend position to be adjusted
#' for \code{\link[patchwork]{patchwork}ed} output (default "right"). See \link[ggplot2]{theme}
#'
#' @return No return value by default. If using fast = FALSE, will return a
#' \code{\link[patchwork]{patchwork}ed} ggplot object if combine = TRUE, otherwise
#' returns a list of ggplot objects
#'
#' @importFrom patchwork wrap_plots plot_layout
#' @export
#' @concept visualization
#'
#' @seealso \code{\link[graphics]{image}} \code{\link[ggplot2]{geom_raster}}
#'
#' @examples
#' data("pbmc_small")
#' 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,
    fast = TRUE,
    raster = TRUE,
    slot = 'scale.data',
    assays = NULL,
    combine = TRUE,
    legend.position = "right"
) {
  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)),
    layer = 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
      )
      plots[[i]] <- plots[[i]] +
        ggtitle(paste0(Key(object = object[[reduction]]), dims[i])) +
        theme(plot.title = element_text(hjust = 0.5, face = "bold"))
    }
  }
  if (fast) {
    par(mfrow = orig.par)
    return(invisible(x = NULL))
  }
  if (combine) {
    plots <- wrap_plots(plots, ncol = ncol, guides = "collect") + 
      plot_layout(guides = "collect") &
      theme(legend.position = legend.position)
  }
  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 vjust Vertical 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 \code{\link[patchwork]{patchwork}ed}
#' ggplot object. If \code{FALSE}, return a list of ggplot objects
#'
#' @return A \code{\link[patchwork]{patchwork}ed} ggplot object if
#' \code{combine = TRUE}; otherwise, a list of ggplot objects
#'
#' @importFrom stats median
#' @importFrom scales hue_pal
#' @importFrom ggplot2 annotation_raster coord_cartesian scale_color_manual
#' ggplot_build geom_text
#' @importFrom patchwork wrap_plots
#' @export
#' @concept visualization
#'
#' @examples
#' data("pbmc_small")
#' 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,
  vjust = 0,
  angle = 45,
  raster = TRUE,
  draw.lines = TRUE,
  lines.width = NULL,
  group.bar.height = 0.02,
  combine = TRUE
) {
  assay <- assay %||% DefaultAssay(object = object)
  DefaultAssay(object = object) <- assay
  cells <- cells %||% colnames(x = object[[assay]])
  if (is.numeric(x = cells)) {
    cells <- colnames(x = object)[cells]
  }
  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 <- Features(object,layer = 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, " layer for the ", assay, " assay.")
    }
    warning("The following features were omitted as they were not found in the ", slot,
            " layer for the ", assay, " assay: ", paste(bad.features, collapse = ", "))
  }
  data <- as.data.frame(x = as.matrix(x = t(x = GetAssayData(
    object = object,
    layer = 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))))
      if (!is.null(x = names(x = group.colors))) {
        cols <- unname(obj = group.colors[levels(x = group.use)])
      } else {
        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
      x.min <- min(pbuild$layout$panel_params[[1]]$x.range) + 0.1
      x.max <- max(pbuild$layout$panel_params[[1]]$x.range) - 0.1
      plot <- plot +
        annotation_raster(
          raster = t(x = cols[group.use2]),
          xmin = x.min,
          xmax = x.max,
          ymin = y.pos,
          ymax = y.max
        ) +
        coord_cartesian(ylim = c(0, y.max), clip = 'off') +
        scale_color_manual(
          values = cols[-length(x = cols)],
          name = "Identity",
          na.translate = FALSE
        )
      if (label) {
        x.max <- max(pbuild$layout$panel_params[[1]]$x.range)
        # Attempt to pull xdivs from x.major in ggplot2 < 3.3.0; if NULL, pull from the >= 3.3.0 slot
        x.divs <- pbuild$layout$panel_params[[1]]$x.major %||% attr(x = pbuild$layout$panel_params[[1]]$x$get_breaks(), which = "pos")
        x <- data.frame(group = sort(x = group.use), x = x.divs)
        label.x.pos <- tapply(X = x$x, INDEX = x$group, FUN = function(y) {
          if (isTRUE(x = draw.lines)) {
            mean(x = y[-length(x = y)])
          } else {
            mean(x = y)
          }
        })
        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(label = .data[['group']], x = .data[['label.x.pos']]),
          y = y.max + y.max * 0.03 * 0.5 + vjust,
          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 <- wrap_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
#' @concept visualization
#'
#' @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 = "none")
  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 slot Slot to pull expression data from (e.g. "counts" or "data")
#' @param layer Layer to pull expression data from (e.g. "counts" or "data")
#' @param stack Horizontally stack plots for each feature
#' @param combine Combine plots into a single \code{\link[patchwork]{patchwork}ed}
#' ggplot object. If \code{FALSE}, return a list of ggplot
#' @param fill.by Color violins/ridges based on either 'feature' or 'ident'
#'
#' @return A \code{\link[patchwork]{patchwork}ed} ggplot object if
#' \code{combine = TRUE}; otherwise, a list of ggplot objects
#'
#' @export
#' @concept visualization
#'
#' @examples
#' data("pbmc_small")
#' 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,
  slot = deprecated(),
  layer = 'data',
  stack = FALSE,
  combine = TRUE,
  fill.by = 'feature'
) {
  if (is_present(arg = slot)) {
    deprecate_soft(
      when = '5.0.0',
      what = 'RidgePlot(slot = )',
      with = 'RidgePlot(layer = )'
    )
    layer <- slot %||% layer
  }
  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,
    layer = layer,
    stack = stack,
    combine = combine,
    fill.by = fill.by
  ))
}

#' 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 points
#' @param alpha Alpha value for points
#' @param split.by A factor in object metadata to split the plot by, pass 'ident'
#' to split by cell identity
#' @param split.plot  plot each group of the split violin plots by multiple or
#' single violin shapes.
#' @param adjust Adjust parameter for geom_violin
#' @param flip flip plot orientation (identities on x-axis)
#' @param add.noise determine if adding a small noise for plotting
#' @param raster Convert points to raster format. Requires 'ggrastr' to be installed.
# default is \code{NULL} which automatically rasterizes if ggrastr is installed and
# number of points exceed 100,000.
#' @param raster.dpi the dpi for raster layer, default is 300.
#' See \code{\link[ggrastr]{rasterize}} for more info.
#'
#' @return A \code{\link[patchwork]{patchwork}ed} ggplot object if
#' \code{combine = TRUE}; otherwise, a list of ggplot objects
#'
#' @export
#' @concept visualization
#'
#' @seealso \code{\link{FetchData}}
#'
#' @examples
#' data("pbmc_small")
#' VlnPlot(object = pbmc_small, features = 'PC_1')
#' VlnPlot(object = pbmc_small, features = 'LYZ', split.by = 'groups')
#'
VlnPlot <- function(
  object,
  features,
  cols = NULL,
  pt.size = NULL,
  alpha = 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,
  slot = deprecated(),
  layer = NULL,
  split.plot = FALSE,
  stack = FALSE,
  combine = TRUE,
  fill.by = 'feature',
  flip = FALSE,
  add.noise = TRUE,
  raster = NULL,
  raster.dpi = 300
) {
  if (is_present(arg = slot)) {
    deprecate_soft(
      when = '5.0.0',
      what = 'VlnPlot(slot = )',
      with = 'VlnPlot(layer = )'
    )
    layer <- slot %||% layer
  }
  layer.set <- suppressWarnings(
    Layers(
      object = object,
      search = layer %||% 'data'
    )
  )
  if (is.null(layer) && length(layer.set) == 1 && layer.set == 'scale.data'){
    warning('Default search for "data" layer yielded no results; utilizing "scale.data" layer instead.')
  }
  assay.name <- assay %||% DefaultAssay(object = object)
  if (is.null(layer.set) & is.null(layer) ) {
    warning('Default search for "data" layer in "', assay.name, '" assay yielded no results; utilizing "counts" layer instead.',
            call. = FALSE, immediate. = TRUE)
    layer.set <- Layers(
      object = object,
      search = 'counts'
    )
  }
  if (is.null(layer.set)) {
    stop('layer "', layer,'" is not found in assay: "', assay.name, '"')
  } else {
    layer <- layer.set
  }
  if (
    !is.null(x = split.by) &
    getOption(x = 'Seurat.warn.vlnplot.split', default = TRUE)
  ) {
    message(
      "The default behaviour of split.by has changed.\n",
      "Separate violin plots are now plotted side-by-side.\n",
      "To restore the old behaviour of a single split violin,\n",
      "set split.plot = TRUE.
      \nThis message will be shown once per session."
    )
    options(Seurat.warn.vlnplot.split = FALSE)
  }
  return(ExIPlot(
    object = object,
    type = ifelse(test = split.plot, yes = 'splitViolin', 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,
    alpha = alpha,
    cols = cols,
    group.by = group.by,
    split.by = split.by,
    log = log,
    layer = layer,
    stack = stack,
    combine = combine,
    fill.by = fill.by,
    flip = flip,
    add.noise = add.noise,
    raster = raster,
    raster.dpi = raster.dpi
  ))
}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# 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
#' @concept visualization
#'
#' @seealso \code{\link{DimPlot}}
#'
#' @examples
#' \dontrun{
#' if (requireNamespace("ape", quietly = TRUE)) {
#'   data("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 A factor in object metadata to split the plot by, pass 'ident'
#' to split by cell identity
#' @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.  Only applicable if \code{raster = FALSE}.
#' @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 shuffle Whether to randomly shuffle the order of points. This can be
#' useful for crowded plots if points of interest are being buried. (default is FALSE)
#' @param seed Sets the seed if randomly shuffling the order of points.
#' @param label Whether to label the clusters
#' @param label.size Sets size of labels
#' @param label.color Sets the color of the label text
#' @param label.box Whether to put a box around the label text (geom_text vs
#' geom_label)
#' @param alpha Alpha value for plotting (default is 1)
#' @param repel Repel labels
#' @param stroke.size Adjust stroke (outline) size of points
#' @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.  If \code{sizes.highlight = TRUE} size of all
#' points will be this value.
#' @param na.value Color value for NA points when using custom scale
#' @param ncol Number of columns for display when combining plots
#' @param combine Combine plots into a single \code{\link[patchwork]{patchwork}ed}
#' ggplot object. If \code{FALSE}, return a list of ggplot objects
#' @param raster Convert points to raster format, default is \code{NULL} which
#' automatically rasterizes if plotting more than 100,000 cells
#' @param raster.dpi Pixel resolution for rasterized plots, passed to geom_scattermore(). Default is c(512, 512).
#' @param label.size.cutoff Clusters with fewer cells than the cutoff are not labeled (replaced with ' ' label)
#'
#' @return A \code{\link[patchwork]{patchwork}ed} ggplot object if
#' \code{combine = TRUE}; otherwise, a list of ggplot objects
#'
#' @importFrom rlang !!
#' @importFrom ggplot2 facet_wrap vars sym labs
#' @importFrom patchwork wrap_plots
#'
#' @export
#' @concept visualization
#'
#' @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
#' data("pbmc_small")
#' DimPlot(object = pbmc_small)
#' DimPlot(object = pbmc_small, split.by = 'letter.idents')
#'
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,
  shuffle = FALSE,
  seed = 1,
  label = FALSE,
  label.size = 4,
  label.color = 'black',
  label.box = FALSE,
  repel = FALSE,
  alpha = 1,
  stroke.size = NULL,
  cells.highlight = NULL,
  cols.highlight = '#DE2D26',
  sizes.highlight = 1,
  na.value = 'grey50',
  ncol = NULL,
  combine = TRUE,
  raster = NULL,
  raster.dpi = c(512, 512),
  label.size.cutoff = 0
) {
  if (!is_integerish(x = dims, n = 2L, finite = TRUE) || !all(dims > 0L)) {
    abort(message = "'dims' must be a two-length integer vector")
  }
  reduction <- reduction %||% DefaultDimReduc(object = object)
  # cells <- cells %||% colnames(x = object)
  ##### Cells for all cells in the assay.
  #### Cells function should not only get default layer
  cells <- cells %||% Cells(
    x = object,
    assay = DefaultAssay(object = object[[reduction]])
  )
  # data <- Embeddings(object = object[[reduction]])[cells, dims]
  # data <- as.data.frame(x = data)
  dims <- paste0(Key(object = object[[reduction]]), dims)

  orig.groups <- group.by
  group.by <- group.by %||% 'ident'

  if (label & (label.size.cutoff > 0)) {
    labels <- FetchData(object, group.by)
    for(i in seq_along(group.by)) {
      grouping_var <- group.by[i]
      label_table <- table(labels[,grouping_var])
      invalid_labels <- names(which(label_table < label.size.cutoff))
      labels[, i] <- as.character(labels[,i])
      labels[which(labels[,grouping_var] %in% invalid_labels),grouping_var] <- ' '
      colnames(labels)[i] <- paste0(colnames(labels)[i], "_filtered")
    }
    object <- AddMetaData(object,labels)
    group.by <- colnames(labels)
  }

  # check for overlap between colnames of dim reduc embeddings and metadata
  metadata_cols <- names(object[[]])
  colname_overlap <- intersect(dims, metadata_cols)
  if (length(colname_overlap) > 0) {
    warning("Found metadata columns with the same names as requested reduction columns: ",
      paste(colname_overlap, collapse = ", "),
      ". Consider renaming these metadata column(s) to avoid conflicts with dimensionality reduction embeddings.",
      call. = FALSE)
  }

  data <- FetchData(
    object = object,
    vars = c(dims, group.by),
    cells = cells,
    clean = 'project'
  )

  # cells <- rownames(x = object)
  # object[['ident']] <- Idents(object = object)
  # orig.groups <- group.by
  # group.by <- group.by %||% 'ident'
  # data <- cbind(data, object[[group.by]][cells, , drop = FALSE])
  group.by <- colnames(x = data)[3:ncol(x = data)]
  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)) {
    split <- FetchData(object = object, vars = split.by, clean=TRUE)[split.by]
    data <- data[rownames(split),]
    data[, split.by] <- split
  }
  if (isTRUE(x = shuffle)) {
    set.seed(seed = seed)
    data <- data[sample(x = 1:nrow(x = data)), ]
  }
  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,
        alpha = alpha,
        stroke.size = stroke.size,
        label = FALSE,
        cells.highlight = cells.highlight,
        cols.highlight = cols.highlight,
        sizes.highlight = sizes.highlight,
        na.value = na.value,
        raster = raster,
        raster.dpi = raster.dpi
      )
      if (label) {
        plot <- LabelClusters(
          plot = plot,
          id = x,
          repel = repel,
          size = label.size,
          split.by = split.by,
          box = label.box,
          color = label.color
        )
      }
      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
            }
          )
      }
      plot <- if (is.null(x = orig.groups)) {
        plot + labs(title = NULL)
      } else {
        plot + CenterTitle()
      }
    }
  )
  if (!is.null(x = split.by)) {
    ncol <- 1
  }
  if (combine) {
    plots <- wrap_plots(plots, ncol = orig.groups %iff% 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 stroke.size Adjust stroke (outline) size of points
#' @param split.by A factor in object metadata to split the plot by, pass 'ident'
#' to split by cell identity
#' @param keep.scale How to handle the color scale across multiple plots. Options are:
#' \itemize{
#'   \item \dQuote{feature} (default; by row/feature scaling): The plots for
#'     each individual feature are scaled to the maximum expression of the
#'     feature across the conditions provided to \code{split.by}
#'   \item \dQuote{all} (universal scaling): The plots for all features and
#'     conditions are scaled to the maximum expression value for the feature
#'     with the highest overall expression
#'   \item \code{NULL} (no scaling): Each individual plot is scaled to the
#'     maximum expression value of the feature in the condition provided to
#'     \code{split.by}. Be aware setting \code{NULL} will result in color
#'     scales that are not comparable between plots
#' }
#' @param slot Which slot to pull expression data from?
#' @param assay Primary assay to pull feature 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 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 Redundant with \code{order}. This argument is being
#' deprecated. Please use \code{order} instead.
#' @param interactive Launch an interactive \code{\link[Seurat:IFeaturePlot]{FeaturePlot}}
#' @param combine Combine plots into a single \code{\link[patchwork]{patchwork}ed}
#' ggplot object. If \code{FALSE}, return a list of ggplot objects
#'
#' @return A \code{\link[patchwork]{patchwork}ed} ggplot object if
#' \code{combine = TRUE}; otherwise, a list of ggplot objects
#'
#' @importFrom grDevices rgb
#' @importFrom patchwork wrap_plots
#' @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
#' @concept visualization
#'
#' @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
#' data("pbmc_small")
#' 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,
  alpha = 1,
  stroke.size = NULL,
  order = FALSE,
  min.cutoff = NA,
  max.cutoff = NA,
  reduction = NULL,
  split.by = NULL,
  keep.scale = "feature",
  shape.by = NULL,
  slot = 'data',
  assay = NULL,
  blend = FALSE,
  blend.threshold = 0.5,
  label = FALSE,
  label.size = 4,
  label.color = "black",
  repel = FALSE,
  ncol = NULL,
  coord.fixed = FALSE,
  by.col = TRUE,
  sort.cell = deprecated(),
  interactive = FALSE,
  combine = TRUE,
  raster = NULL,
  raster.dpi = c(512, 512)
) {
  # TODO: deprecate fully on 3.2.0
  if (is_present(arg = sort.cell)) {
    deprecate_stop(
      when = '4.9.0',
      what = 'FeaturePlot(sort.cell = )',
      with = 'FeaturePlot(order = )'
    )
  }
  if (isTRUE(x = interactive)) {
    return(IFeaturePlot(
      object = object,
      feature = features[1],
      dims = dims,
      reduction = reduction,
      slot = slot
    ))
  }
  # Check keep.scale param for valid entries
  if (!is.null(x = keep.scale)) {
    keep.scale <- arg_match0(arg = keep.scale, values = c('feature', 'all'))
  }
  # 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 (!is_integerish(x = dims, n = 2L, finite = TRUE) && !all(dims > 0L)) {
    abort(message = "'dims' must be a two-length integer vector")
  }
  # Figure out blending stuff
  if (isTRUE(x = blend) && length(x = features) != 2) {
    abort(message = "Blending feature plots only works with two features")
  }
  # Set color scheme for blended FeaturePlots
  if (isTRUE(x = blend)) {
    default.colors <- eval(expr = formals(fun = FeaturePlot)$cols)
    cols <- switch(
      EXPR = as.character(x = length(x = cols)),
      '0' = {
        warn(message = "No colors provided, using default colors")
        default.colors
      },
      '1' = {
        warn(message = paste(
          "Only one color provided, assuming",
          sQuote(x = cols),
          "is double-negative and augmenting with default colors"
        ))
        c(cols, default.colors[2:3])
      },
      '2' = {
        warn(message = paste(
          "Only two colors provided, assuming specified are for features and agumenting with",
          sQuote(default.colors[1]),
          "for double-negatives",
        ))
        c(default.colors[1], cols)
      },
      '3' = cols,
      {
        warn(message = "More than three colors provided, using only first three")
        cols[1:3]
      }
    )
  }
  if (isTRUE(x = blend) && length(x = cols) != 3) {
    abort("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 %||% Cells(x = object[[reduction]])
  # Get plotting data
  data <- FetchData(
    object = object,
    vars = c(dims, 'ident', features),
    cells = cells,
    layer = slot,
    assay = assay
  )
  # Check presence of features/dimensions
  if (ncol(x = data) < 4) {
    abort(message = paste(
      "None of the requested features were found:",
      paste(features, collapse = ', '),
      "in slot ",
      slot
    ))
  } else if (!all(dims %in% colnames(x = data))) {
    abort(message = "The dimensions requested were not found")
  }
  features <- setdiff(x = names(x = data), y = c(dims, 'ident'))
  # 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,
    feature = features
  )
  max.cutoff <- mapply(
    FUN = function(cutoff, feature) {
      return(ifelse(
        test = is.na(x = cutoff),
        yes = max(data[, feature]),
        no = cutoff
      ))
    },
    cutoff = max.cutoff,
    feature = features
  )
  check.lengths <- unique(x = vapply(
    X = list(features, min.cutoff, max.cutoff),
    FUN = length,
    FUN.VALUE = numeric(length = 1)
  ))
  if (length(x = check.lengths) != 1) {
    abort(
      message = "There must be the same number of minimum and maximum cuttoffs as there are features"
    )
  }
  names(x = min.cutoff) <- names(x = max.cutoff) <- features
  brewer.gran <- ifelse(
    test = length(x = cols) == 1,
    yes = brewer.pal.info[cols, ]$maxcolors,
    no = length(x = cols)
  )
  # Apply cutoffs
  for (i in seq_along(along.with = features)) {
    f <- features[i]
    data.feature <- data[[f]]
    min.use <- SetQuantile(cutoff = min.cutoff[f], data = data.feature)
    max.use <- SetQuantile(cutoff = max.cutoff[f], data = data.feature)
    data.feature[data.feature < min.use] <- min.use
    data.feature[data.feature > max.use] <- max.use
    if (brewer.gran != 2) {
      data.feature <- if (all(data.feature == 0)) {
        rep_len(x = 0, length.out = length(x = data.feature))
      } else {
        as.numeric(x = as.factor(x = cut(
          x = as.numeric(x = data.feature),
          breaks = 2
        )))
      }
    }
    data[[f]] <- data.feature
  }
  # Figure out splits (FeatureHeatmap)
  data$split <- if (is.null(x = split.by)) {
    RandomName()
  } else {
    switch(
      EXPR = split.by,
      ident = Idents(object = object)[cells, drop = TRUE],
      object[[split.by, drop = TRUE]][cells, drop = TRUE]
    )
  }
  if (!is.factor(x = data$split)) {
    data$split <- factor(x = data$split)
  }
  # Set shaping variable
  if (!is.null(x = shape.by)) {
    data[, shape.by] <- object[[shape.by, drop = TRUE]]
  }
  # Make list of plots
  plots <- vector(
    mode = "list",
    length = ifelse(
      test = blend,
      yes = 4,
      no = length(x = features) * length(x = levels(x = data$split))
    )
  )
  # Apply common limits
  xlims <- c(floor(x = min(data[, dims[1]])), ceiling(x = max(data[, dims[1]])))
  ylims <- c(floor(min(data[, dims[2]])), ceiling(x = max(data[, dims[2]])))
  # Set blended colors
  if (blend) {
    ncol <- 4
    color.matrix <- BlendMatrix(
      two.colors = cols[2:3],
      col.threshold = blend.threshold,
      negative.color = cols[1]
    )
    cols <- cols[2:3]
    colors <- list(
      color.matrix[, 1],
      color.matrix[1, ],
      as.vector(x = color.matrix)
    )
  }
  # Make the plots
  for (i in 1:length(x = levels(x = data$split))) {
    # Figure out which split we're working with
    ident <- levels(x = data$split)[i]
    data.plot <- data[as.character(x = data$split) == ident, , drop = FALSE]
    # Blend expression values
    if (isTRUE(x = blend)) {
      features <- features[1:2]
      no.expression <- features[colMeans(x = data.plot[, features]) == 0]
      if (length(x = no.expression) != 0) {
        abort(message = paste(
          "The following features have no value:",
          paste(no.expression, collapse = ', ')
        ))
      }
      data.plot <- cbind(data.plot[, c(dims, 'ident')], BlendExpression(data = data.plot[, features[1:2]]))
      features <- colnames(x = data.plot)[4:ncol(x = data.plot)]
    }
    # Make per-feature plots
    for (j in 1:length(x = features)) {
      feature <- features[j]
      # Get blended colors
      if (isTRUE(x = blend)) {
        cols.use <- as.numeric(x = as.character(x = data.plot[, feature])) + 1
        cols.use <- colors[[j]][sort(x = unique(x = cols.use))]
      } else {
        cols.use <- NULL
      }
      data.single <- data.plot[, c(dims, 'ident', feature, shape.by)]
      # Make the plot
      plot <- SingleDimPlot(
        data = data.single,
        dims = dims,
        col.by = feature,
        order = order,
        pt.size = pt.size,
        alpha = alpha,
        stroke.size = stroke.size,
        cols = cols.use,
        shape.by = shape.by,
        label = FALSE,
        raster = raster,
        raster.dpi = raster.dpi
      ) +
        scale_x_continuous(limits = xlims) +
        scale_y_continuous(limits = ylims) +
        theme_cowplot() +
        CenterTitle()
        # theme(plot.title = element_text(hjust = 0.5))
      # Add labels
      if (isTRUE(x = label)) {
        plot <- LabelClusters(
          plot = plot,
          id = 'ident',
          repel = repel,
          size = label.size,
          color = label.color
        )
      }
      # Make FeatureHeatmaps look nice(ish)
      if (length(x = levels(x = data$split)) > 1) {
        plot <- plot + theme(panel.border = element_rect(fill = NA, colour = 'black'))
        # Add title
        plot <- plot + if (i == 1) {
          labs(title = feature)
        } else {
          labs(title = NULL)
        }
        # Add second axis
        if (j == length(x = features) && !blend) {
          suppressMessages(
            expr = plot <- plot +
              scale_y_continuous(
                sec.axis = dup_axis(name = ident),
                limits = ylims
              ) +
              no.right
          )
        }
        # Remove left Y axis
        if (j != 1) {
          plot <- plot + theme(
            axis.line.y = element_blank(),
            axis.ticks.y = element_blank(),
            axis.text.y = element_blank(),
            axis.title.y.left = element_blank()
          )
        }
        # Remove bottom X axis
        if (i != length(x = levels(x = data$split))) {
          plot <- plot + theme(
            axis.line.x = element_blank(),
            axis.ticks.x = element_blank(),
            axis.text.x = element_blank(),
            axis.title.x = element_blank()
          )
        }
      } else {
        plot <- plot + labs(title = feature)
      }
      # Add colors scale for normal FeaturePlots
      if (!blend) {
        plot <- plot + guides(color = NULL)
        cols.grad <- cols
        if (length(x = cols) == 1) {
          plot <- plot + scale_color_brewer(palette = cols)
        } else if (length(x = cols) > 1) {
          unique.feature.exp <- unique(data.plot[, feature])
          if (length(unique.feature.exp) == 1) {
            warn(message = paste0(
              "All cells have the same value (",
              unique.feature.exp,
              ") of ",
              dQuote(x = feature)
            ))
            if (unique.feature.exp == 0) {
              cols.grad <- cols[1]
            } else{
              cols.grad <- cols
            }
          }
          plot <- suppressMessages(
            expr = plot + scale_color_gradientn(
              colors = cols.grad,
              guide = "colorbar"
            )
          )
        }
      }
      if (!(is.null(x = keep.scale)) && keep.scale == "feature" && !blend) {
        max.feature.value <- max(data[, feature])
        min.feature.value <- min(data[, feature])
        plot <- suppressMessages(plot & scale_color_gradientn(colors = cols, limits = c(min.feature.value, max.feature.value)))
      }
      # Add coord_fixed
      if (coord.fixed) {
        plot <- plot + coord_fixed()
      }
      # I'm not sure why, but sometimes the damn thing fails without this
      # Thanks ggplot2
      plot <- plot
      # Place the plot
      plots[[(length(x = features) * (i - 1)) + j]] <- plot
    }
  }
  # Add blended color key
  if (isTRUE(x = blend)) {
    blend.legend <- BlendMap(color.matrix = color.matrix)
    for (ii in 1:length(x = levels(x = data$split))) {
      suppressMessages(expr = plots <- append(
        x = plots,
        values = list(
          blend.legend +
            scale_y_continuous(
              sec.axis = dup_axis(name = ifelse(
                test = length(x = levels(x = data$split)) > 1,
                yes = levels(x = data$split)[ii],
                no = ''
              )),
              expand = c(0, 0)
            ) +
            labs(
              x = features[1],
              y = features[2],
              title = if (ii == 1) {
                paste('Color threshold:', blend.threshold)
              } else {
                NULL
              }
            ) +
            no.right
        ),
        after = 4 * ii - 1
      ))
    }
  }
  # Remove NULL plots
  plots <- Filter(f = Negate(f = is.null), x = plots)
  # Combine the plots
  if (is.null(x = ncol)) {
    ncol <- 2
    if (length(x = features) == 1) {
      ncol <- 1
    }
    if (length(x = features) > 6) {
      ncol <- 3
    }
    if (length(x = features) > 9) {
      ncol <- 4
    }
  }
  ncol <- ifelse(
    test = is.null(x = split.by) || isTRUE(x = blend),
    yes = ncol,
    no = length(x = features)
  )
  legend <- if (isTRUE(x = blend)) {
    'none'
  } else {
    split.by %iff% 'none'
  }
  # Transpose the FeatureHeatmap matrix (not applicable for blended FeaturePlots)
  if (isTRUE(x = combine)) {
    if (by.col && !is.null(x = split.by) && !blend) {
      plots <- lapply(
        X = plots,
        FUN = function(x) {
          return(suppressMessages(
            expr = x +
              theme_cowplot() +
              ggtitle("") +
              scale_y_continuous(sec.axis = dup_axis(name = ""), limits = ylims) +
              no.right
          ))
        }
      )
      nsplits <- length(x = levels(x = data$split))
      idx <- 1
      for (i in (length(x = features) * (nsplits - 1) + 1):(length(x = features) * nsplits)) {
        plots[[i]] <- suppressMessages(
          expr = plots[[i]] +
            scale_y_continuous(
              sec.axis = dup_axis(name = features[[idx]]),
              limits = ylims
            ) +
            no.right
        )
        idx <- idx + 1
      }
      idx <- 1
      for (i in which(x = 1:length(x = plots) %% length(x = features) == 1)) {
        plots[[i]] <- plots[[i]] +
          ggtitle(levels(x = data$split)[[idx]]) +
          theme(plot.title = element_text(hjust = 0.5))
        idx <- idx + 1
      }
      idx <- 1
      if (length(x = features) == 1) {
        for (i in 1:length(x = plots)) {
          plots[[i]] <- plots[[i]] +
            ggtitle(levels(x = data$split)[[idx]]) +
            theme(plot.title = element_text(hjust = 0.5))
          idx <- idx + 1
        }
        ncol <- 1
        nrow <- nsplits
      } else {
        nrow <- split.by %iff% length(x = levels(x = data$split))
      }
      plots <- plots[c(do.call(
        what = rbind,
        args = split(
          x = 1:length(x = plots),
          f = ceiling(x = seq_along(along.with = 1:length(x = plots)) / length(x = features))
        )
      ))]
      # Set ncol to number of splits (nrow) and nrow to number of features (ncol)
      plots <- wrap_plots(plots, ncol = nrow, nrow = ncol)
      if (!is.null(x = legend) && legend == 'none') {
        plots <- plots & NoLegend()
      }
    } else {
      plots <- wrap_plots(plots, ncol = ncol, nrow = split.by %iff% length(x = levels(x = data$split)))
    }
    if (!is.null(x = legend) && legend == 'none') {
      plots <- plots & NoLegend()
    }
    if (!(is.null(x = keep.scale)) && keep.scale == "all" && !blend) {
      max.feature.value <- max(data[, features])
      min.feature.value <- min(data[, features])
      plots <- suppressMessages(plots & scale_color_gradientn(colors = cols, limits = c(min.feature.value, max.feature.value)))
    }
  }
  return(plots)
}

#' Visualize features in dimensional reduction space interactively
#'
#' @inheritParams FeaturePlot
#' @param feature Feature to plot
#'
#' @return Returns the final plot as a ggplot object
#'
#' @importFrom cowplot theme_cowplot
#' @importFrom ggplot2 theme element_text guides scale_color_gradientn
#' @importFrom miniUI miniPage miniButtonBlock miniTitleBarButton miniContentPanel
#' @importFrom shiny fillRow sidebarPanel selectInput plotOutput reactiveValues
#' observeEvent stopApp observe updateSelectInput renderPlot runGadget
#'
#' @export
#' @concept visualization
#'
IFeaturePlot <- function(object, feature, dims = c(1, 2), reduction = NULL, slot = 'data') {
  # Set initial data values
  feature.label <- 'Feature to visualize'
  assay.keys <- Key(object = object)[Assays(object = object)]
  keyed <- sapply(X = assay.keys, FUN = grepl, x = feature)
  assay <- if (any(keyed)) {
    names(x = which(x = keyed))[1]
  } else {
    DefaultAssay(object = object)
  }
  features <- sort(x = rownames(x = GetAssayData(
    object = object,
    layer = slot,
    assay = assay
  )))
  assays.use <- vapply(
    X = Assays(object = object),
    FUN = function(x) {
      return(!IsMatrixEmpty(x = GetAssayData(
        object = object,
        layer = slot,
        assay = x
      )))
    },
    FUN.VALUE = logical(length = 1L)
  )
  assays.use <- sort(x = Assays(object = object)[assays.use])
  reduction <- reduction %||% DefaultDimReduc(object = object)
  dims.reduc <- gsub(
    pattern = Key(object = object[[reduction]]),
    replacement = '',
    x = colnames(x = object[[reduction]])
  )
  # Set up the gadget UI
  ui <- miniPage(
    miniButtonBlock(miniTitleBarButton(
      inputId = 'done',
      label = 'Done',
      primary = TRUE
    )),
    miniContentPanel(
      fillRow(
        sidebarPanel(
          selectInput(
            inputId = 'assay',
            label = 'Assay',
            choices = assays.use,
            selected = assay,
            selectize = FALSE,
            width = '100%'
          ),
          selectInput(
            inputId = 'feature',
            label = feature.label,
            choices = features,
            selected = feature,
            selectize = FALSE,
            width = '100%'
          ),
          selectInput(
            inputId = 'reduction',
            label = 'Dimensional reduction',
            choices = Reductions(object = object),
            selected = reduction,
            selectize = FALSE,
            width = '100%'
          ),
          selectInput(
            inputId = 'xdim',
            label = 'X dimension',
            choices = dims.reduc,
            selected = as.character(x = dims[1]),
            selectize = FALSE,
            width = '100%'
          ),
          selectInput(
            inputId = 'ydim',
            label = 'Y dimension',
            choices = dims.reduc,
            selected = as.character(x = dims[2]),
            selectize = FALSE,
            width = '100%'
          ),
          selectInput(
            inputId = 'palette',
            label = 'Color scheme',
            choices = names(x = FeaturePalettes),
            selected = 'Seurat',
            selectize = FALSE,
            width = '100%'
          ),
          width = '100%'
        ),
        plotOutput(outputId = 'plot', height = '100%'),
        flex = c(1, 4)
      )
    )
  )
  # Prepare plotting data
  dims <- paste0(Key(object = object[[reduction]]), dims)
  plot.data <- FetchData(object = object, vars = c(dims, feature), layer = slot)
  # Shiny server
  server <- function(input, output, session) {
    plot.env <- reactiveValues(
      data = plot.data,
      dims = paste0(Key(object = object[[reduction]]), dims),
      feature = feature,
      palette = 'Seurat'
    )
    # Observe events
    observeEvent(
      eventExpr = input$done,
      handlerExpr = stopApp(returnValue = plot.env$plot)
    )
    observe(x = {
      assay <- input$assay
      feature.use <- input$feature
      features.assay <- sort(x = rownames(x = GetAssayData(
        object = object,
        layer = slot,
        assay = assay
      )))
      feature.use <- ifelse(
        test = feature.use %in% features.assay,
        yes = feature.use,
        no = features.assay[1]
      )
      reduc <- input$reduction
      dims.reduc <- gsub(
        pattern = Key(object = object[[reduc]]),
        replacement = '',
        x = colnames(x = object[[reduc]])
      )
      dims <- c(input$xdim, input$ydim)
      for (i in seq_along(along.with = dims)) {
        if (!dims[i] %in% dims.reduc) {
          dims[i] <- dims.reduc[i]
        }
      }
      updateSelectInput(
        session = session,
        inputId = 'xdim',
        label = 'X dimension',
        choices = dims.reduc,
        selected = as.character(x = dims[1])
      )
      updateSelectInput(
        session = session,
        inputId = 'ydim',
        label = 'Y dimension',
        choices = dims.reduc,
        selected = as.character(x = dims[2])
      )
      updateSelectInput(
        session = session,
        inputId = 'feature',
        label = feature.label,
        choices = features.assay,
        selected = feature.use
      )
    })
    observe(x = {
      feature.use <- input$feature
      feature.keyed <- paste0(Key(object = object[[input$assay]]), feature.use)
      reduc <- input$reduction
      dims <- c(input$xdim, input$ydim)
      dims <- paste0(Key(object = object[[reduc]]), dims)
      plot.data <- tryCatch(
        expr = FetchData(
          object = object,
          vars = c(dims, feature.keyed),
          layer = slot
        ),
        warning = function(...) {
          return(plot.env$data)
        },
        error = function(...) {
          return(plot.env$data)
        }
      )
      dims <- colnames(x = plot.data)[1:2]
      colnames(x = plot.data) <- c(dims, feature.use)
      plot.env$data <- plot.data
      plot.env$feature <- feature.use
      plot.env$dims <- dims
    })
    observe(x = {
      plot.env$palette <- input$palette
    })
    # Create the plot
    output$plot <- renderPlot(expr = {
      plot.env$plot <- SingleDimPlot(
        data = plot.env$data,
        dims = plot.env$dims,
        col.by = plot.env$feature,
        label = FALSE
      ) +
        theme_cowplot() +
        theme(plot.title = element_text(hjust = 0.5)) +
        guides(color = NULL) +
        scale_color_gradientn(
          colors = FeaturePalettes[[plot.env$palette]],
          guide = 'colorbar'
        )
      plot.env$plot
    })
  }
  runGadget(app = ui, server = server)
}

#' Highlight Neighbors in DimPlot
#'
#' It will color the query cells and the neighbors of the query cells in the
#' DimPlot
#'
#' @inheritParams DimPlot
#' @param nn.idx the neighbor index of all cells
#' @param query.cells cells used to find their neighbors
#' @param show.all.cells Show all cells or only query and neighbor cells
#'
#' @inherit DimPlot return
#'
#' @export
#' @concept visualization
#'
NNPlot <- function(
  object,
  reduction,
  nn.idx,
  query.cells,
  dims = 1:2,
  label = FALSE,
  label.size = 4,
  repel = FALSE,
  sizes.highlight = 2,
  pt.size = 1,
  cols.highlight = c("#377eb8", "#e41a1c"),
  na.value =  "#bdbdbd",
  order = c("self", "neighbors", "other"),
  show.all.cells = TRUE,
  ...
) {
  if (inherits(x = nn.idx, what = 'Neighbor')) {
    rownames(x = slot(object = nn.idx, name = 'nn.idx')) <- Cells(x = nn.idx)
    nn.idx <- Indices(object = nn.idx)
  }
  if (length(x = query.cells) > 1) {
    neighbor.cells <- apply(
      X = nn.idx[query.cells, -1],
      MARGIN = 2,
      FUN = function(x) {
        return(Cells(x = object)[x])
      }
    )
  } else {
    neighbor.cells <- Cells(x = object)[nn.idx[query.cells , -1]]
  }
  neighbor.cells <- as.vector(x = neighbor.cells)
  neighbor.cells <- neighbor.cells[!is.na(x = neighbor.cells)]
  object[["nn.col"]] <- "other"
  object[["nn.col"]][neighbor.cells, ] <- "neighbors"
  object[["nn.col"]][query.cells, ] <- "self"
  object$nn.col <- factor(
    x = object$nn.col,
    levels = c("self", "neighbors", "other")
  )
  if (!show.all.cells) {
    object <- subset(
      x = object,
      cells = Cells(x = object)[which(x = object[["nn.col"]] != "other")]
    )
    nn.cols  <- c(rev(x = cols.highlight))
    nn.pt.size <- sizes.highlight
  } else {
    highlight.info <- SetHighlight(
      cells.highlight = c(query.cells, neighbor.cells),
      cells.all = Cells(x = object),
      sizes.highlight = sizes.highlight,
      pt.size = pt.size,
      cols.highlight = "red"
    )
    nn.cols  <- c(na.value, rev(x = cols.highlight))
    nn.pt.size <- highlight.info$size
  }
  NN.plot <- DimPlot(
    object = object,
    reduction = reduction,
    dims = dims,
    group.by = "nn.col",
    cols = nn.cols,
    label = label,
    order =  order,
    pt.size = nn.pt.size ,
    label.size = label.size,
    repel = repel
  )
  return(NN.plot)
}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Scatter plots
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

#' Cell-cell scatter plot
#'
#' Creates a plot of scatter plot of features across two single cells. Pearson
#' correlation between the two cells is displayed above the plot.
#'
#' @inheritParams FeatureScatter
#' @inheritParams DimPlot
#' @param cell1 Cell 1 name
#' @param cell2 Cell 2 name
#' @param features Features to plot (default, all features)
#' @param highlight Features to highlight
#' @return A ggplot object
#'
#' @export
#' @concept visualization
#'
#' @aliases CellPlot
#'
#' @examples
#' data("pbmc_small")
#' CellScatter(object = pbmc_small, cell1 = 'ATAGGAGAAACAGA', cell2 = 'CATCAGGATGCACA')
#'
CellScatter <- function(
  object,
  cell1,
  cell2,
  features = NULL,
  highlight = NULL,
  cols = NULL,
  pt.size = 1,
  smooth = FALSE,
  raster = NULL,
  raster.dpi = c(512, 512)
) {
  features <- features %||% rownames(x = object)
  data <- FetchData(
    object = object,
    vars = features,
    cells = c(cell1, cell2)
  )
  data <- as.data.frame(x = t(x = data))
  plot <- SingleCorPlot(
    data = data,
    cols = cols,
    pt.size = pt.size,
    rows.highlight = highlight,
    smooth = smooth,
    raster = raster,
    raster.dpi = raster.dpi
  )
  return(plot)
}

#' Scatter plot of single cell data
#'
#' Creates a scatter plot of two features (typically feature expression), across a
#' set of single cells. Cells are colored by their identity class. Pearson
#' correlation between the two features is displayed above the plot.
#'
#' @param object Seurat object
#' @param feature1 First feature to plot. Typically feature expression but can also
#' be metrics, PC scores, etc. - anything that can be retreived with FetchData
#' @param feature2 Second feature to plot.
#' @param cells Cells to include on the scatter plot.
#' @param shuffle Whether to randomly shuffle the order of points. This can be
#' useful for crowded plots if points of interest are being buried. (default is FALSE)
#' @param seed Sets the seed if randomly shuffling the order of points.
#' @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 cols Colors to use for identity class plotting.
#' @param pt.size Size of the points on the plot
#' @param shape.by Ignored for now
#' @param split.by A factor in object metadata to split the feature plot by, pass 'ident'
#' to split by cell identity
#' @param span Spline span in loess function call, if \code{NULL}, no spline added
#' @param smooth Smooth the graph (similar to smoothScatter)
#' @param slot Slot to pull data from, should be one of 'counts', 'data', or 'scale.data'
#' @param combine Combine plots into a single \code{\link[patchwork]{patchwork}ed}
#' @param plot.cor Display correlation in plot title
#' @param ncol Number of columns if plotting multiple plots
#' @param raster Convert points to raster format, default is \code{NULL}
#' which will automatically use raster if the number of points plotted is greater than
#' 100,000
#' @param raster.dpi Pixel resolution for rasterized plots, passed to geom_scattermore().
#' Default is c(512, 512).
#' @param jitter Jitter for easier visualization of crowded points (default is FALSE)
#' @param log Plot features on the log scale (default is FALSE)
#'
#' @return A ggplot object
#'
#' @importFrom ggplot2 geom_smooth facet_wrap vars sym labs
#' @importFrom patchwork wrap_plots
#'
#' @export
#' @concept visualization
#'
#' @aliases GenePlot
#'
#' @examples
#' data("pbmc_small")
#' FeatureScatter(object = pbmc_small, feature1 = 'CD9', feature2 = 'CD3E')
#'
FeatureScatter <- function(
  object,
  feature1,
  feature2,
  cells = NULL,
  shuffle = FALSE,
  seed = 1,
  group.by = NULL,
  split.by = NULL,
  cols = NULL,
  pt.size = 1,
  shape.by = NULL,
  span = NULL,
  smooth = FALSE,
  combine = TRUE,
  slot = 'data',
  plot.cor = TRUE,
  ncol = NULL,
  raster = NULL,
  raster.dpi = c(512, 512),
  jitter = FALSE,
  log = FALSE
) {
  cells <- cells %||% colnames(x = object)
  if (isTRUE(x = shuffle)) {
    set.seed(seed = seed)
    cells <- sample(x = cells)
  }
  group.by <- group.by %||% 'ident'
  data <-  FetchData(
    object = object,
    vars = c(feature1, feature2, group.by),
    cells = cells,
    layer = slot
  )
  if (!grepl(pattern = feature1, x = names(x = data)[1], fixed = TRUE)) {
    abort(message = paste("Feature 1", sQuote(x = feature1), "not found"))
  }
  if (!grepl(pattern = feature2, x = names(x = data)[2], fixed = TRUE)) {
    abort(message = paste("Feature 2", sQuote(x = feature2), "not found"))
  }
  feature1 <-  names(x = data)[1]
  feature2 <-  names(x = data)[2]
  group.by <- intersect(x = group.by, y = names(x = data)[3:ncol(x = data)])
  for (group in group.by) {
    if (!is.factor(x = data[, group])) {
      data[, group] <- factor(x = data[, group])
    }
  }
  if (!is.null(x = split.by)) {
    split <- FetchData(object = object, vars = split.by, clean=TRUE)[split.by]
    data <- data[rownames(split),]
    data[, split.by] <- split
  }
  plots <- lapply(
    X = group.by,
    FUN = function(x) {
      plot <- SingleCorPlot(
        data = data[,c(feature1, feature2, split.by)],
        col.by = data[, x],
        cols = cols,
        pt.size = pt.size,
        smooth = smooth,
        legend.title = 'Identity',
        span = span,
        plot.cor = plot.cor,
        raster = raster,
        raster.dpi = raster.dpi,
        jitter = jitter
      )
      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
            }
          )
      }
      if (log) {
        plot <- plot + scale_x_log10() + scale_y_log10()
      }
      plot
    }
  )
  if (isTRUE(x = length(x = plots) == 1)) {
    return(plots[[1]])
  }
  if (isTRUE(x = combine)) {
    plots <- wrap_plots(plots, ncol = length(x = group.by))
  }
  return(plots)
}

#' View variable features
#'
#' @inheritParams FeatureScatter
#' @inheritParams SeuratObject::HVFInfo
#' @param cols Colors to specify non-variable/variable status
#' @param assay Assay to pull variable features from
#' @param log Plot the x-axis in log scale
#' @param raster Convert points to raster format, default is \code{NULL}
#' which will automatically use raster if the number of points plotted is greater than
#' 100,000
#'
#' @return A ggplot object
#'
#' @importFrom ggplot2 labs scale_color_manual scale_x_log10
#' @export
#' @concept visualization
#'
#' @aliases VariableGenePlot MeanVarPlot
#'
#' @seealso \code{\link{FindVariableFeatures}}
#'
#' @examples
#' data("pbmc_small")
#' VariableFeaturePlot(object = pbmc_small)
#'
VariableFeaturePlot <- function(
  object,
  cols = c('black', 'red'),
  pt.size = 1,
  log = NULL,
  selection.method = NULL,
  assay = NULL,
  raster = NULL,
  raster.dpi = c(512, 512)
) {
  if (length(x = cols) != 2) {
    stop("'cols' must be of length 2")
  }
  hvf.info <- HVFInfo(
    object = object,
    assay = assay,
    method = selection.method,
    status = TRUE
  )
  status.col <- colnames(hvf.info)[grepl("variable", colnames(hvf.info))][[1]]
  var.status <- c('no', 'yes')[unlist(hvf.info[[status.col]]) + 1]
  if (colnames(x = hvf.info)[3] == 'dispersion.scaled') {
    hvf.info <- hvf.info[, c(1, 2)]
  } else if (colnames(x = hvf.info)[3] == 'variance.expected') {
    hvf.info <- hvf.info[, c(1, 4)]
  } else {
    hvf.info <- hvf.info[, c(1, 3)]
  }
  axis.labels <- switch(
    EXPR = colnames(x = hvf.info)[2],
    'variance.standardized' = c('Average Expression', 'Standardized Variance'),
    'dispersion' = c('Average Expression', 'Dispersion'),
    'residual_variance' = c('Geometric Mean of Expression', 'Residual Variance')
  )
  log <- log %||% (any(c('variance.standardized', 'residual_variance') %in% colnames(x = hvf.info)))
  # var.features <- VariableFeatures(object = object, assay = assay)
  # var.status <- ifelse(
  #   test = rownames(x = hvf.info) %in% var.features,
  #   yes = 'yes',
  #   no = 'no'
  # )
  plot <- SingleCorPlot(
    data = hvf.info,
    col.by = var.status,
    pt.size = pt.size,
    raster = raster,
    raster.dpi = raster.dpi
  )
  if (length(x = unique(x = var.status)) == 1) {
    switch(
      EXPR = var.status[1],
      'yes' = {
        cols <- cols[2]
        labels.legend <- 'Variable'
      },
      'no' = {
        cols <- cols[1]
        labels.legend <- 'Non-variable'
      }
    )
  } else {
    labels.legend <- c('Non-variable', 'Variable')
  }
  plot <- plot +
    labs(title = NULL, x = axis.labels[1], y = axis.labels[2]) +
    scale_color_manual(
      labels = paste(labels.legend, 'count:', table(var.status)),
      values = cols
    )
  if (log) {
    plot <- plot + scale_x_log10()
  }
  return(plot)
}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Polygon Plots
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

#' Polygon DimPlot
#'
#' Plot cells as polygons, rather than single points. Color cells by identity, or a categorical variable
#' in metadata
#'
#' @inheritParams PolyFeaturePlot
#' @param group.by A grouping variable present in the metadata. Default is to use the groupings present
#' in the current cell identities (\code{Idents(object = object)})
#'
#' @return Returns a ggplot object
#'
#' @export
#' @concept visualization
#'
PolyDimPlot <- function(
  object,
  group.by = NULL,
  cells = NULL,
  poly.data = 'spatial',
  flip.coords = FALSE
) {
  polygons <- Misc(object = object, slot = poly.data)
  if (is.null(x = polygons)) {
    stop("Could not find polygon data in misc slot")
  }
  group.by <- group.by %||% 'ident'
  group.data <- FetchData(
    object = object,
    vars = group.by,
    cells = cells
  )
  group.data$cell <- rownames(x = group.data)
  data <- merge(x = polygons, y = group.data, by = 'cell')
  if (flip.coords) {
    coord.x <- data$x
    data$x <- data$y
    data$y <- coord.x
  }
  plot <- SinglePolyPlot(data = data, group.by = group.by)
  return(plot)
}

#' Polygon FeaturePlot
#'
#' Plot cells as polygons, rather than single points. Color cells by any value
#' accessible by \code{\link{FetchData}}.
#'
#' @inheritParams FeaturePlot
#' @param poly.data Name of the polygon dataframe in the misc slot
#' @param ncol Number of columns to split the plot into
#' @param common.scale ...
#' @param flip.coords Flip x and y coordinates
#'
#' @return Returns a ggplot object
#'
#' @importFrom ggplot2 scale_fill_viridis_c facet_wrap
#'
#' @export
#' @concept visualization
#' @concept spatial
#'
PolyFeaturePlot <- function(
  object,
  features,
  cells = NULL,
  poly.data = 'spatial',
  ncol = ceiling(x = length(x = features) / 2),
  min.cutoff = 0,
  max.cutoff = NA,
  common.scale = TRUE,
  flip.coords = FALSE
) {
  polygons <- Misc(object = object, slot = poly.data)
  if (is.null(x = polygons)) {
    stop("Could not find polygon data in misc slot")
  }
  assay.data <- FetchData(
    object = object,
    vars = features,
    cells = cells
  )
  features <- colnames(x = assay.data)
  cells <- rownames(x = assay.data)
  min.cutoff <- mapply(
    FUN = function(cutoff, feature) {
      return(ifelse(
        test = is.na(x = cutoff),
        yes = min(assay.data[, feature]),
        no = cutoff
      ))
    },
    cutoff = min.cutoff,
    feature = features
  )
  max.cutoff <- mapply(
    FUN = function(cutoff, feature) {
      return(ifelse(
        test = is.na(x = cutoff),
        yes = max(assay.data[, feature]),
        no = cutoff
      ))
    },
    cutoff = max.cutoff,
    feature = features
  )
  check.lengths <- unique(x = vapply(
    X = list(features, min.cutoff, max.cutoff),
    FUN = length,
    FUN.VALUE = numeric(length = 1)
  ))
  if (length(x = check.lengths) != 1) {
    stop("There must be the same number of minimum and maximum cuttoffs as there are features")
  }
  assay.data <- mapply(
    FUN = function(feature, min, max) {
      return(ScaleColumn(vec = assay.data[, feature], cutoffs = c(min, max)))
    },
    feature = features,
    min = min.cutoff,
    max = max.cutoff
  )
  if (common.scale) {
    assay.data <- apply(
      X = assay.data,
      MARGIN = 2,
      FUN = function(x) {
        return(x - min(x))
      }
    )
    assay.data <- t(
      x = t(x = assay.data) / apply(X = assay.data, MARGIN = 2, FUN = max)
    )
  }
  assay.data <- as.data.frame(x = assay.data)
  assay.data <- data.frame(
    cell = as.vector(x = replicate(n = length(x = features), expr = cells)),
    feature = as.vector(x = t(x = replicate(n = length(x = cells), expr = features))),
    expression = unlist(x = assay.data, use.names = FALSE)
  )
  data <- merge(x = polygons, y = assay.data, by = 'cell')
  data$feature <- factor(x = data$feature, levels = features)
  if (flip.coords) {
    coord.x <- data$x
    data$x <- data$y
    data$y <- coord.x
  }
  plot <- SinglePolyPlot(data = data, group.by = 'expression', font_size = 8) +
    scale_fill_viridis_c() +
    facet_wrap(facets = 'feature', ncol = ncol)
  return(plot)
}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Spatial Plots
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

#' Spatial Cluster Plots
#'
#' Visualize clusters or other categorical groupings in a spatial context
#'
#' @inheritParams DimPlot
#' @inheritParams SingleImagePlot
#' @param object A \code{\link[SeuratObject]{Seurat}} object
#' @param fov Name of FOV to plot
#' @param boundaries A vector of segmentation boundaries per image to plot;
#' can be a character vector, a named character vector, or a named list.
#' Names should be the names of FOVs and values should be the names of
#' segmentation boundaries
#' @param molecules A vector of molecules to plot
#' @param nmols Max number of each molecule specified in `molecules` to plot
#' @param dark.background Set plot background to black
#' @param crop Crop the plots to area with cells only
#' @param overlap Overlay boundaries from a single image to create a single
#' plot; if \code{TRUE}, then boundaries are stacked in the order they're
#' given (first is lowest)
#' @param axes Keep axes and panel background
#' @param combine Combine plots into a single
#' \code{patchwork} ggplot object.If \code{FALSE},
#' return a list of ggplot objects
#' @param coord.fixed Plot cartesian coordinates with fixed aspect ratio
#' @param flip_xy Flag to flip X and Y axes. Default is FALSE.
#'
#' @return If \code{combine = TRUE}, a \code{patchwork}
#' ggplot object; otherwise, a list of ggplot objects
#'
#' @importFrom rlang !! is_na sym
#' @importFrom patchwork wrap_plots
#' @importFrom ggplot2 element_blank facet_wrap vars
#' @importFrom SeuratObject DefaultFOV Cells
#' DefaultBoundary FetchData Images Overlay
#'
#' @export
#' @concept visualization
#' @concept spatial
#'
ImageDimPlot <- function(
  object,
  fov = NULL,
  boundaries = NULL,
  group.by = NULL,
  split.by = NULL,
  cols = NULL,
  shuffle.cols = FALSE,
  size = 0.5,
  molecules = NULL,
  mols.size = 0.1,
  mols.cols = NULL,
  mols.alpha = 1.0,
  nmols = 1000,
  alpha = 1.0,
  border.color = 'white',
  border.size = NULL,
  na.value = 'grey50',
  dark.background = TRUE,
  crop = FALSE,
  cells = NULL,
  overlap = FALSE,
  axes = FALSE,
  combine = TRUE,
  coord.fixed = TRUE,
  flip_xy = TRUE
) {
  cells <- cells %||% Cells(x = object)
  # Determine FOV to use
  fov <- fov %||% DefaultFOV(object = object)
  fov <- Filter(
    f = function(x) {
      return(
        x %in% Images(object = object) &&
          inherits(x = object[[x]], what = 'FOV')
      )
    },
    x = fov
  )
  if (!length(x = fov)) {
    stop("No compatible spatial coordinates present")
  }
  # Identify boundaries to use
  boundaries <- boundaries %||% sapply(
    X = fov,
    FUN = function(x) {
      return(DefaultBoundary(object = object[[x]]))
    },
    simplify = FALSE,
    USE.NAMES = TRUE
  )
  boundaries <- .BoundariesByImage(
    object = object,
    fov = fov,
    boundaries = boundaries
  )
  fov <- names(x = boundaries)
  overlap <- rep_len(x = overlap, length.out = length(x = fov))
  crop <- rep_len(x = crop, length.out = length(x = fov))
  names(x = crop) <- fov
  # Prepare plotting data
  group.by <- boundaries %!NA% group.by %||% 'ident'
  vars <- c(group.by, split.by)
  md <- if (!is_na(x = vars)) {
    FetchData(
      object = object,
      vars = vars[!is.na(x = vars)],
      cells = cells
    )
  } else {
    NULL
  }
  pnames <- unlist(x = lapply(
    X = seq_along(along.with = fov),
    FUN = function(i) {
      return(if (isTRUE(x = overlap[i])) {
        fov[i]
      } else {
        paste(fov[i], boundaries[[i]], sep = '_')
      })
    }
  ))
  pdata <- vector(mode = 'list', length = length(x = pnames))
  names(x = pdata) <- pnames
  for (i in names(x = pdata)) {
    ul <- unlist(x = strsplit(x = i, split = '_'))
    img <- paste(ul[1:length(ul)-1], collapse = '_')
    # Apply overlap
    lyr <- ul[length(ul)]
    if (is.na(x = lyr)) {
      lyr <- boundaries[[img]]
    }
    # TODO: Apply crop
    pdata[[i]] <- lapply(
      X = lyr,
      FUN = function(l) {
        if (l == 'NA') {
          return(NA)
        }
        df <- fortify(model = object[[img]][[l]])
        df <- df[df$cell %in% cells, , drop = FALSE]
        if (!is.null(x = md)) {
          df <- merge(x = df, y = md, by.x = 'cell', by.y = 0, all.x = TRUE)
        }
        df$cell <- paste(l, df$cell, sep = '_')
        df$boundary <- l
        return(df)
      }
    )
    pdata[[i]] <- if (!is_na(x = pdata[[i]])) {
      do.call(what = 'rbind', args = pdata[[i]])
    } else {
      unlist(x = pdata[[i]])
    }
  }
  # Fetch molecule information
  if (!is.null(x = molecules)) {
    molecules <- .MolsByFOV(
      object = object,
      fov = fov,
      molecules = molecules
    )
    mdata <- vector(mode = 'list', length = length(x = fov))
    names(x = mdata) <- fov
    for (img in names(x = mdata)) {
      idata <- object[[img]]
      if (!img %in% names(x = molecules)) {
        mdata[[img]] <- NULL
        next
      }
      if (isTRUE(x = crop[img])) {
        idata <- Overlay(x = idata, y = idata)
      }
      imols <- gsub(
        pattern = paste0('^', Key(object = idata)),
        replacement = '',
        x = molecules[[img]]
      )
      mdata[[img]] <- FetchData(
        object = idata,
        vars = imols,
        nmols = nmols
      )
    }
  } else {
    mdata <- NULL
  }
  # Build the plots
  plots <- vector(
    mode = 'list',
    length = length(x = pdata) * ifelse(
      test = length(x = group.by),
      yes = length(x = group.by),
      no = 1L
    )
  )
  idx <- 1L
  for (group in group.by) {
    for (i in seq_along(along.with = pdata)) {
      img <- unlist(x = strsplit(x = names(x = pdata)[i], split = '_'))[1L]
      p <- SingleImagePlot(
        data = pdata[[i]],
        col.by = pdata[[i]] %!NA% group,
        molecules = mdata[[img]],
        cols = cols,
        shuffle.cols = shuffle.cols,
        size = size,
        alpha = alpha,
        mols.size = mols.size,
        mols.cols = mols.cols,
        mols.alpha = mols.alpha,
        border.color = border.color,
        border.size = border.size,
        na.value = na.value,
        dark.background = dark.background
      )
      if (!is.null(x = split.by)) {
        p <- p + facet_wrap(
          facets = vars(!!sym(x = split.by))
        )
      }
      if (!isTRUE(x = axes)) {
        p <- p + NoAxes(panel.background = element_blank())
      }
      if (!anyDuplicated(x = pdata[[i]]$cell)) {
        p <- p + guides(fill = guide_legend(override.aes = list(size=4L, alpha=1)))
      }
      if (isTRUE(coord.fixed)) {
        p <- p + coord_fixed()
      }
      if(!isTRUE(flip_xy) && isTRUE(coord.fixed)){
        xy_ratio = (max(pdata[[i]]$x) - min(pdata[[i]]$x)) / (max(pdata[[i]]$y) - min(pdata[[i]]$y))
        p = p + coord_flip() + theme(aspect.ratio = 1/xy_ratio)
      }
      plots[[idx]] <- p
      idx <- idx + 1L
    }
  }
  if (isTRUE(x = combine)) {
    plots <- wrap_plots(plots)
  }
  return(plots)
}

#' Spatial Feature Plots
#'
#' Visualize expression in a spatial context
#'
#' @inheritParams FeaturePlot
#' @inheritParams ImageDimPlot
#' @param scale Set color scaling across multiple plots; choose from:
#' \itemize{
#'  \item \dQuote{\code{feature}}: Plots per-feature are scaled across splits
#'  \item \dQuote{\code{all}}: Plots per-feature are scaled across all features
#'  \item \dQuote{\code{none}}: Plots are not scaled; \strong{note}: setting
#'   \code{scale} to \dQuote{\code{none}} will result in color scales that are
#'   \emph{not} comparable between plots
#' }
#' Ignored if \code{blend = TRUE}
#'
#' @inherit ImageDimPlot return
#'
#' @importFrom patchwork wrap_plots
#' @importFrom cowplot theme_cowplot
#' @importFrom ggplot2 dup_axis element_blank element_text facet_wrap guides
#' labs margin vars scale_y_continuous theme
#' @importFrom SeuratObject DefaultFOV Cells DefaultBoundary
#' FetchData Images Overlay
#'
#' @export
#' @concept visualization
#' @concept spatial
#'
ImageFeaturePlot <- function(
  object,
  features,
  fov = NULL,
  boundaries = NULL,
  cols = if (isTRUE(x = blend)) {
    c("lightgrey", "#ff0000", "#00ff00")
  } else {
    c("lightgrey", "firebrick1")
  },
  size = 0.5,
  min.cutoff = NA,
  max.cutoff = NA,
  split.by = NULL,
  molecules = NULL,
  mols.size = 0.1,
  mols.cols = NULL,
  nmols = 1000,
  alpha = 1.0,
  border.color = 'white',
  border.size = NULL,
  dark.background = TRUE,
  blend = FALSE,
  blend.threshold = 0.5,
  crop = FALSE,
  cells = NULL,
  scale = c('feature', 'all', 'none'),
  overlap = FALSE,
  axes = FALSE,
  combine = TRUE,
  coord.fixed = TRUE
) {
  cells <- cells %||% Cells(x = object)
  scale <- scale[[1L]]
  scale <- match.arg(arg = scale)
  # 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)
    )
  )
  # Determine fov to use
  fov <- fov %||% DefaultFOV(object = object)
  fov <- Filter(
    f = function(x) {
      return(
        x %in% Images(object = object) &&
          inherits(x = object[[x]], what = 'FOV')
      )
    },
    x = fov
  )
  if (!length(x = fov)) {
    stop("No compatible spatial coordinates present")
  }
  # Identify boundaries to use
  boundaries <- boundaries %||% sapply(
    X = fov,
    FUN = function(x) {
      return(DefaultBoundary(object = object[[x]]))
    },
    simplify = FALSE,
    USE.NAMES = TRUE
  )
  boundaries <- .BoundariesByImage(
    object = object,
    fov = fov,
    boundaries = boundaries
  )
  fov <- names(x = boundaries)
  # Check overlaps/crops
  if (isTRUE(x = blend) || !is.null(x = split.by)) {
    type <- ifelse(test = isTRUE(x = 'blend'), yes = 'Blended', no = 'Split')
    if (length(x = fov) != 1L) {
      fov <- fov[1L]
      warning(
        type,
        ' image feature plots can only be done on a single image, using "',
        fov,
        '"',
        call. = FALSE,
        immediate. = TRUE
      )
    }
    if (any(!overlap) && length(x = boundaries[[fov]]) > 1L) {
      warning(
        type,
        " image feature plots require overlapped segmentations",
        call. = FALSE,
        immediate. = TRUE
      )
    }
    overlap <- TRUE
  }
  overlap <- rep_len(x = overlap, length.out = length(x = fov))
  crop <- rep_len(x = crop, length.out = length(x = fov))
  names(x = crop) <- names(x = overlap) <- fov
  # Checks for blending
  if (isTRUE(x = blend)) {
    if (length(x = features) != 2L) {
      stop("Blended feature plots only works with two features")
    }
    default.colors <- eval(expr = formals(fun = ImageFeaturePlot)$cols)
    cols <- switch(
      EXPR = as.character(x = length(x = cols)),
      '0' = {
        warning("No colors provided, using default colors", immediate. = TRUE)
        default.colors
      },
      '1' = {
        warning(
          "Only one color provided, assuming specified is double-negative and augmenting with default colors",
          immediate. = TRUE
        )
        c(cols, default.colors[2:3])
      },
      '2' = {
        warning(
          "Only two colors provided, assuming specified are for features and augmenting with '",
          default.colors[1],
          "' for double-negatives",
          immediate. = TRUE
        )
        c(default.colors[1], cols)
      },
      '3' = cols,
      {
        warning(
          "More than three colors provided, using only first three",
          immediate. = TRUE
        )
        cols[1:3]
      }
    )
  }
  # Get feature, splitting data
  md <- FetchData(
    object = object,
    vars = c(features, split.by[1L]),
    cells = cells
  )
  split.by <- intersect(x = split.by, y = colnames(x = md))
  if (!length(x = split.by)) {
    split.by <- NULL
  }
  imax <- ifelse(
    test = is.null(x = split.by),
    yes = ncol(x = md),
    no = ncol(x = md) - length(x = split.by)
  )
  features <- colnames(x = md)[1:imax]
  # Determine cutoffs
  min.cutoff <- mapply(
    FUN = function(cutoff, feature) {
      return(ifelse(
        test = is.na(x = cutoff),
        yes = min(md[[feature]]),
        no = cutoff
      ))
    },
    cutoff = min.cutoff,
    feature = features
  )
  max.cutoff <- mapply(
    FUN = function(cutoff, feature) {
      return(ifelse(
        test = is.na(x = cutoff),
        yes = max(md[[feature]]),
        no = cutoff
      ))
    },
    cutoff = max.cutoff,
    feature = features
  )
  check.lengths <- unique(x = vapply(
    X = list(features, min.cutoff, max.cutoff),
    FUN = length,
    FUN.VALUE = numeric(length = 1)
  ))
  if (length(x = check.lengths) != 1) {
    stop("There must be the same number of minimum and maximum cuttoffs as there are features")
  }
  brewer.gran <- ifelse(
    test = length(x = cols) == 1,
    yes = brewer.pal.info[cols, ]$maxcolors,
    no = length(x = cols)
  )
  # Apply cutoffs
  for (i in seq_along(along.with = features)) {
    f <- features[[i]]
    data.feature <- md[[f]]
    min.use <- SetQuantile(cutoff = min.cutoff[i], data = data.feature)
    max.use <- SetQuantile(cutoff = max.cutoff[i], data = data.feature)
    data.feature[data.feature < min.use] <- min.use
    data.feature[data.feature > max.use] <- max.use
    if (brewer.gran != 2) {
      data.feature <- if (all(data.feature == 0)) {
        rep_len(x = 0, length.out = length(x = data.feature))
      } else {
        as.numeric(x = as.factor(x = cut(
          x = as.numeric(x = data.feature),
          breaks = brewer.gran
        )))
      }
    }
    md[[f]] <- data.feature
  }
  # Figure out splits
  if (is.null(x = split.by)) {
    split.by <- RandomName()
    md[[split.by]] <- factor(x = split.by)
  }
  if (!is.factor(x = md[[split.by]])) {
    md[[split.by]] <- factor(x = md[[split.by]])
  }
  # Apply blends
  if (isTRUE(x = blend)) {
    md <- lapply(
      X = levels(x = md[[split.by]]),
      FUN = function(x) {
        df <- md[as.character(x = md[[split.by]]) == x, , drop = FALSE]
        no.expression <- features[colMeans(x = df[, features]) == 0]
        if (length(x = no.expression)) {
          stop(
            "The following features have no value: ",
            paste(no.expression, collapse = ', ')
          )
        }
        return(cbind(
          df[, split.by, drop = FALSE],
          BlendExpression(data = df[, features])
        ))
      }
    )
    md <- do.call(what = 'rbind', args = md)
    features <- setdiff(x = colnames(x = md), y = split.by)
  }
  # Prepare plotting data
  pnames <- unlist(x = lapply(
    X = seq_along(along.with = fov),
    FUN = function(i) {
      return(if (isTRUE(x = overlap[i])) {
        fov[i]
      } else {
        paste(fov[i], boundaries[[i]], sep = '_')
      })
    }
  ))
  pdata <- vector(mode = 'list', length = length(x = pnames))
  names(x = pdata) <- pnames
  for (i in names(x = pdata)) {
    ul <- unlist(x = strsplit(x = i, split = '_'))
    # img <- paste(ul[1:length(ul)-1], collapse = '_')
    # Apply overlap
    # lyr <- ul[length(ul)]
    if(length(ul) > 1) {
         img <- paste(ul[1:length(ul)-1], collapse = '_')
         lyr <- ul[length(ul)]
    } else if (length(ul) == 1) {
         img <- ul[1]
         lyr <- "centroids"
    } else {
         stop("the length of ul is 0. please check.")
    }
    if (is.na(x = lyr)) {
      lyr <- boundaries[[img]]
    }
    pdata[[i]] <- lapply(
      X = lyr,
      FUN = function(l) {
        df <- fortify(model = object[[img]][[l]])
        df <- df[df$cell %in% cells, , drop = FALSE]
        if (!is.null(x = md)) {
          df <- merge(x = df, y = md, by.x = 'cell', by.y = 0, all.x = TRUE)
        }
        df$cell <- paste(l, df$cell, sep = '_')
        df$boundary <- l
        return(df)
      }
    )
    pdata[[i]] <- if (!is_na(x = pdata[[i]])) {
      do.call(what = 'rbind', args = pdata[[i]])
    } else {
      unlist(x = pdata[[i]])
    }
  }
  # Fetch molecule information
  if (!is.null(x = molecules)) {
    molecules <- .MolsByFOV(
      object = object,
      fov = fov,
      molecules = molecules
    )
    mdata <- vector(mode = 'list', length = length(x = fov))
    names(x = mdata) <- fov
    for (img in names(x = mdata)) {
      idata <- object[[img]]
      if (!img %in% names(x = molecules)) {
        mdata[[img]] <- NULL
        next
      }
      if (isTRUE(x = crop[img])) {
        idata <- Overlay(x = idata, y = idata)
      }
      imols <- gsub(
        pattern = paste0('^', Key(object = idata)),
        replacement = '',
        x = molecules[[img]]
      )
      mdata[[img]] <- FetchData(
        object = idata,
        vars = imols,
        nmols = nmols
      )
    }
  } else {
    mdata <- NULL
  }
  # Set blended colors
  if (isTRUE(x = blend)) {
    ncol <- 4
    color.matrix <- BlendMatrix(
      two.colors = cols[2:3],
      col.threshold = blend.threshold,
      negative.color = cols[1]
    )
    cols <- cols[2:3]
    colors <- list(
      color.matrix[, 1],
      color.matrix[1, ],
      as.vector(x = color.matrix)
    )
    blend.legend <- BlendMap(color.matrix = color.matrix)
  }
  limits <- switch(
    EXPR = scale,
    'all' = range(unlist(x = md[, features])),
    NULL
  )
  # Build the plots
  plots <- vector(
    mode = 'list',
    length = length(x = levels(x = md[[split.by]]))
  )
  names(x = plots) <- levels(x = md[[split.by]])
  for (i in seq_along(along.with = levels(x = md[[split.by]]))) {
    ident <- levels(x = md[[split.by]])[i]
    plots[[ident]] <- vector(mode = 'list', length = length(x = pdata))
    names(x = plots[[ident]]) <- names(x = pdata)
    if (isTRUE(x = blend)) {
      blend.key <- suppressMessages(
        expr = blend.legend +
          scale_y_continuous(
            sec.axis = dup_axis(name = ifelse(
              test = length(x = levels(x = md[[split.by]])) > 1,
              yes = ident,
              no = ''
            )),
            expand = c(0, 0)
          ) +
          labs(
            x = features[1L],
            y = features[2L],
            title = if (i == 1L) {
              paste('Color threshold:', blend.threshold)
            } else {
              NULL
            }
          ) +
          no.right
      )
    }
    for (j in seq_along(along.with = pdata)) {
      key <- names(x = pdata)[j]
      img <- unlist(x = strsplit(x = key, split = '_'))[1L]
      plots[[ident]][[key]] <- vector(
        mode = 'list',
        length = length(x = features) + ifelse(
          test = isTRUE(x = blend),
          yes = 1L,
          no = 0L
        )
      )
      data.plot <- pdata[[j]][as.character(x = pdata[[j]][[split.by]]) == ident, , drop = FALSE]
      for (y in seq_along(along.with = features)) {
        feature <- features[y]
        # Get blended colors
        cols.use <- if (isTRUE(x = blend)) {
          cc <- as.numeric(x = as.character(x = data.plot[, feature])) + 1
          colors[[y]][sort(unique(x = cc))]
        } else {
          NULL
        }
        colnames(data.plot) <- gsub("-", "_", colnames(data.plot))
        p <- SingleImagePlot(
          data = data.plot,
          col.by = gsub("-", "_", feature),
          size = size,
          col.factor = blend,
          cols = cols.use,
          molecules = mdata[[img]],
          mols.size = mols.size,
          mols.cols = mols.cols,
          alpha = alpha,
          border.color = border.color,
          border.size = border.size,
          dark.background = dark.background
        ) +
          CenterTitle() + labs(fill=feature)
        # Remove fill guides for blended plots
        if (isTRUE(x = blend)) {
          p <- p + guides(fill = 'none')
        }
        if (isTRUE(coord.fixed)) {
          p <- p + coord_fixed()
        }
        # Remove axes
        if (!isTRUE(x = axes)) {
          p <- p + NoAxes(panel.background = element_blank())
        } else if (isTRUE(x = blend) || length(x = levels(x = md[[split.by]])) > 1L) {
          if (y != 1L) {
            p <- p + theme(
              axis.line.y = element_blank(),
              axis.ticks.y = element_blank(),
              axis.text.y = element_blank(),
              axis.title.y.left = element_blank()
            )
          }
          if (i != length(x = levels(x = md[[split.by]]))) {
            p <- p + theme(
              axis.line.x = element_blank(),
              axis.ticks.x = element_blank(),
              axis.text.x = element_blank(),
              axis.title.x = element_blank()
            )
          }
        }
        # Add colors for unblended plots
        if (!isTRUE(x = blend)) {
          if (length(x = cols) == 1L) {
            p <- p + scale_fill_brewer(palette = cols)
          } else {
            cols.grad <- cols
            fexp <- data.plot[data.plot[[split.by]] == ident, feature, drop = TRUE]
            fexp <- unique(x = fexp)
            if (length(x = fexp) == 1L) {
              warning(
                "All cells have the same value (",
                fexp,
                ") of ",
                feature,
                call. = FALSE,
                immediate. = TRUE
              )
              if (fexp == 0) {
                cols.grad <- cols.grad[1L]
              }
            }
            # Check if we're scaling the colorbar across splits
            if (scale == 'feature') {
              limits <- range(pdata[[j]][[feature]])
            }
            p <- p + ggplot2::scale_fill_gradientn(
              colors = cols.grad,
              guide = 'colorbar',
              limits = limits
            )
          }
        }
        # Add some labels
        p <- p + if (i == 1L) {
          ggplot2::labs(title = feature)
        } else {
          ggplot2::labs(title = NULL)
        }
        plots[[ident]][[key]][[y]] <- p
      }
      if (isTRUE(x = blend)) {
        plots[[ident]][[key]][[length(x = plots[[ident]][[key]])]] <- blend.key
      } else if (length(x = levels(x = md[[split.by]])) > 1L) {
        plots[[ident]][[key]][[y]] <- suppressMessages(
          expr = plots[[ident]][[key]][[y]] +
            scale_y_continuous(sec.axis = dup_axis(name = ident)) +
            no.right
        )
      }
    }
    plots[[ident]] <- unlist(
      x = plots[[ident]],
      recursive = FALSE,
      use.names = FALSE
    )
  }
  plots <- unlist(x = plots, recursive = FALSE, use.names = FALSE)
  if (isTRUE(x = combine)) {
    if (isTRUE(x = blend) || length(x = levels(x = md[[split.by]])) > 1L) {
      plots <- wrap_plots(
        plots,
        ncol = ifelse(
          test = isTRUE(x = blend),
          yes = 4L,
          no = length(x = features)
        ),
        nrow = length(x = levels(x = md[[split.by]])),
        guides = 'collect'
      )
    } else {
      plots <- wrap_plots(plots)
    }
  }
  return(plots)
}

#' Visualize spatial and clustering (dimensional reduction) data in a linked,
#' interactive framework
#'
#' @inheritParams SpatialPlot
#' @inheritParams FeaturePlot
#' @inheritParams DimPlot
#' @param feature Feature to visualize
#' @param image Name of the image to use in the plot
#'
#' @return Returns final plots. If \code{combine}, plots are stiched together
#' using \code{\link{CombinePlots}}; otherwise, returns a list of ggplot objects
#'
#' @rdname LinkedPlots
#' @name LinkedPlots
#'
#' @importFrom scales hue_pal
#' @importFrom patchwork wrap_plots
#' @importFrom ggplot2 scale_alpha_ordinal guides
#' @importFrom miniUI miniPage gadgetTitleBar miniTitleBarButton miniContentPanel
#' @importFrom shiny fillRow plotOutput brushOpts clickOpts hoverOpts
#' verbatimTextOutput reactiveValues observeEvent stopApp nearPoints
#' brushedPoints renderPlot renderPrint runGadget
#'
#' @aliases LinkedPlot LinkedDimPlot
#'
#' @export
#' @concept visualization
#' @concept spatial
#'
#' @examples
#' \dontrun{
#' LinkedDimPlot(seurat.object)
#' LinkedFeaturePlot(seurat.object, feature = 'Hpca')
#' }
#'
LinkedDimPlot <- function(
  object,
  dims = 1:2,
  reduction = NULL,
  image = NULL,
  image.scale = "lowres",
  group.by = NULL,
  alpha = c(0.1, 1),
  combine = TRUE
) {
  # Setup gadget UI
  ui <- miniPage(
    gadgetTitleBar(
      title = 'LinkedDimPlot',
      left = miniTitleBarButton(inputId = 'reset', label = 'Reset')
    ),
    miniContentPanel(
      fillRow(
        plotOutput(
          outputId = 'spatialplot',
          height = '100%',
          # brush = brushOpts(id = 'brush', delay = 10, clip = TRUE, resetOnNew = FALSE),
          click = clickOpts(id = 'spclick', clip = TRUE),
          hover = hoverOpts(id = 'sphover', delay = 10, nullOutside = TRUE)
        ),
        plotOutput(
          outputId = 'dimplot',
          height = '100%',
          brush = brushOpts(id = 'brush', delay = 10, clip = TRUE, resetOnNew = FALSE),
          click = clickOpts(id = 'dimclick', clip = TRUE),
          hover = hoverOpts(id = 'dimhover', delay = 10, nullOutside = TRUE)
        ),
        height = '97%'
      ),
      verbatimTextOutput(outputId = 'info')
    )
  )
  # Prepare plotting data
  image <- image %||% DefaultImage(object = object)
  cells.use <- Cells(x = object[[image]])
  reduction <- reduction %||% DefaultDimReduc(object = object)
  dims <- dims[1:2]
  dims <- paste0(Key(object = object[[reduction]]), dims)
  group.by <- group.by %||% 'ident'
  group.data <- FetchData(
    object = object,
    vars = group.by,
    cells = cells.use
  )
  coords <- GetTissueCoordinates(object = object[[image]], scale = image.scale)
  embeddings <- Embeddings(object = object[[reduction]])[cells.use, dims]
  plot.data <- cbind(coords, group.data, embeddings)
  plot.data$selected_ <- FALSE
  Idents(object = object) <- group.by

  # Retrieve coordinates for tissue plot and dim plot separately
  sp_x <- colnames(coords)[1]
  sp_y <- colnames(coords)[2]
  dp_x <- dims[1]
  dp_y <- dims[2]
  sp_y_min <- min(plot.data[[sp_y]])
  sp_y_max <- max(plot.data[[sp_y]])

  # Add tiny helper function to flip interactive coordinate points
  flip_y <- function(pt) { if (!is.null(pt$y)) { pt$y <- sp_y_max - (pt$y - sp_y_min) }; pt }

  # Setup the server
  server <- function(input, output, session) {
    click <- reactiveValues(pt = NULL, invert = FALSE)
    plot.env <- reactiveValues(data = plot.data, alpha.by = NULL)
    # Handle events
    observeEvent(
      eventExpr = input$done,
      handlerExpr = {
        plots <- list(plot.env$spatialplot, plot.env$dimplot)
        if (combine) {
          plots <- wrap_plots(plots, ncol = 2)
        }
        stopApp(returnValue = plots)
      }
    )
    observeEvent(
      eventExpr = input$reset,
      handlerExpr = {
        click$pt <- NULL
        click$invert <- FALSE
        plot.env$data <- plot.data
        plot.env$alpha.by <- NULL
        session$resetBrush(brushId = 'brush')
      }
    )
    observeEvent(eventExpr = input$brush, handlerExpr = click$pt <- NULL)
    observeEvent(
      eventExpr = input$spclick,
      handlerExpr = {
        # Flip coordinates vertically for spatial plot to match tissue image
        click$pt <- flip_y(input$spclick)
        click$invert <- TRUE
      }
    )
    observeEvent(
      eventExpr = input$dimclick,
      handlerExpr = {
        click$pt <- input$dimclick
        click$invert <- FALSE
      }
    )
    observeEvent(
      eventExpr = c(input$brush, input$spclick, input$dimclick),
      handlerExpr = {
        plot.env$data <- if (is.null(x = input$brush)) {
          clicked <- nearPoints(
            df = plot.data,
            coordinfo = click$pt,
            threshold = 10,
            maxpoints = 1,
            xvar = if (click$invert) sp_x else dp_x,
            yvar = if (click$invert) sp_y else dp_y
          )
          if (nrow(x = clicked) == 1) {
            cell.clicked <- rownames(x = clicked)
            group.clicked <- plot.data[cell.clicked, group.by, drop = TRUE]
            idx.group <- which(x = plot.data[[group.by]] == group.clicked)
            plot.data[idx.group, 'selected_'] <- TRUE
            plot.data
          } else {
            plot.data
          }
        } else if (input$brush$outputId == 'dimplot') {
          brushedPoints(df = plot.data, brush = input$brush, allRows = TRUE, xvar = dp_x, yvar = dp_y)
        } else if (input$brush$outputId == 'spatialplot') {
          b <- input$brush
          b$ymin <- sp_y_max - (b$ymin - sp_y_min)
          b$ymax <- sp_y_max - (b$ymax - sp_y_min)
          brushedPoints(df = plot.data, brush = b, allRows = TRUE, xvar = sp_x, yvar = sp_y)
        }
        plot.env$alpha.by <- if (any(plot.env$data$selected_)) {
          'selected_'
        } else {
          NULL
        }
      }
    )
    # Set plots
    output$spatialplot <- renderPlot(
      expr = {
        plot.env$spatialplot <- SingleSpatialPlot(
          data = plot.env$data,
          image = object[[image]],
          col.by = group.by,
          pt.size.factor = 1.6,
          crop = TRUE,
          alpha.by = plot.env$alpha.by
        ) + scale_alpha_ordinal(range = alpha) + NoLegend()
        plot.env$spatialplot
      }
    )
    output$dimplot <- renderPlot(
      expr = {
        plot.env$dimplot <- SingleDimPlot(
          data = plot.env$data,
          dims = dims,
          col.by = group.by,
          alpha.by = plot.env$alpha.by
        ) + scale_alpha_ordinal(range = alpha) + guides(alpha = "none")
        plot.env$dimplot
      }
    )
    # Add hover text
    output$info <- renderPrint(
      expr = {
        cell.hover <- rownames(x = nearPoints(
          df = plot.data,
          coordinfo = if (is.null(input[['sphover']])) {
            input$dimhover
          } else {
            flip_y(input$sphover)
          },
          threshold = 10,
          maxpoints = 1,
          xvar = if (is.null(input$sphover)) dp_x else sp_x,
          yvar = if (is.null(input$sphover)) dp_y else sp_y
        ))
        # if (length(x = cell.hover) == 1) {
        #   palette <- hue_pal()(n = length(x = levels(x = object)))
        #   group <- plot.data[cell.hover, group.by, drop = TRUE]
        #   background <- palette[which(x = levels(x = object) == group)]
        #   text <- unname(obj = BGTextColor(background = background))
        #   style <- paste0(
        #     paste(
        #       paste('background-color:', background),
        #       paste('color:', text),
        #       sep = '; '
        #     ),
        #     ';'
        #   )
        #   info <- paste(cell.hover, paste('Group:', group), sep = '<br />')
        # } else {
        #   style <- 'background-color: white; color: black'
        #   info <- NULL
        # }
        # HTML(text = paste0("<div style='", style, "'>", info, "</div>"))
        # p(HTML(info), style = style)
        # paste0('<div style="', style, '">', info, '</div>')
        # TODO: Get newlines, extra information, and background color working
        if (length(x = cell.hover) == 1) {
          paste(cell.hover, paste('Group:', plot.data[cell.hover, group.by, drop = TRUE]), collapse = '<br />')
        } else {
          NULL
        }
      }
    )
  }
  # Run the thang
  runGadget(app = ui, server = server)
}

#' @rdname LinkedPlots
#'
#' @aliases LinkedFeaturePlot
#'
#' @importFrom ggplot2 scale_fill_gradientn theme scale_alpha guides
#' scale_color_gradientn guide_colorbar
#'
#' @export
#' @concept visualization
#' @concept spatial
LinkedFeaturePlot <- function(
  object,
  feature,
  dims = 1:2,
  reduction = NULL,
  image = NULL,
  image.scale = "lowres",
  slot = 'data',
  alpha = c(0.1, 1),
  combine = TRUE
) {
  # Setup gadget UI
  ui <- miniPage(
    gadgetTitleBar(
      title = 'LinkedFeaturePlot',
      left = NULL
    ),
    miniContentPanel(
      fillRow(
        plotOutput(
          outputId = 'spatialplot',
          height = '100%',
          hover = hoverOpts(id = 'sphover', delay = 10, nullOutside = TRUE)
        ),
        plotOutput(
          outputId = 'dimplot',
          height = '100%',
          hover = hoverOpts(id = 'dimhover', delay = 10, nullOutside = TRUE)
        ),
        height = '97%'
      ),
      verbatimTextOutput(outputId = 'info')
    )
  )
  # Prepare plotting data
  cols <- SpatialColors(n = 100)
  image <- image %||% DefaultImage(object = object)
  cells.use <- Cells(x = object[[image]])
  reduction <- reduction %||% DefaultDimReduc(object = object)
  dims <- dims[1:2]
  dims <- paste0(Key(object = object[[reduction]]), dims)
  group.data <- FetchData(
    object = object,
    vars = feature,
    cells = cells.use
  )
  coords <- GetTissueCoordinates(object = object[[image]], scale = image.scale)
  embeddings <- Embeddings(object = object[[reduction]])[cells.use, dims]
  sp_x <- colnames(coords)[1]
  sp_y <- colnames(coords)[2]
  dp_x <- dims[1]
  dp_y <- dims[2]
  # coordinates should be in image space, so need to flip y when setting or displaying info for points
  flip_y <- function(pt) { if (!is.null(pt)) { pt$y <- max(coords[[sp_y]]) - (pt$y - min(coords[[sp_y]])) }; pt }
  plot.data <- cbind(coords, group.data, embeddings)
  # Setup the server
  server <- function(input, output, session) {
    plot.env <- reactiveValues()
    # Handle events
    observeEvent(
      eventExpr = input$done,
      handlerExpr = {
        plots <- list(plot.env$spatialplot, plot.env$dimplot)
        if (combine) {
          plots <- wrap_plots(plots, ncol = 2)
        }
        stopApp(returnValue = plots)
      }
    )
    # Set plots
    output$spatialplot <- renderPlot(
      expr = {
        plot.env$spatialplot <- SingleSpatialPlot(
          data = plot.data,
          image = object[[image]],
          col.by = feature,
          pt.size.factor = 1.6,
          crop = TRUE,
          alpha.by = feature
        ) +
          scale_fill_gradientn(name = feature, colours = cols) +
          theme(legend.position = 'top') +
          scale_alpha(range = alpha) +
          guides(alpha = "none")
        plot.env$spatialplot
      }
    )
    output$dimplot <- renderPlot(
      expr = {
        plot.env$dimplot <- SingleDimPlot(
          data = plot.data,
          dims = dims,
          col.by = feature
        ) +
          scale_color_gradientn(name = feature, colours = cols, guide = 'colorbar') +
          guides(color = guide_colorbar())
        plot.env$dimplot
      }
    )
    # Add hover text
    output$info <- renderPrint(
      expr = {
        cell.hover <- rownames(x = nearPoints(
          df = plot.data,
          coordinfo = if (is.null(x = input[['sphover']])) {
            input$dimhover
          } else {
            flip_y(input$sphover)
          },
          threshold = 10,
          maxpoints = 1,
          # specify plot-specific axis columns for nearPoints
          xvar = if (is.null(x = input$sphover)) dp_x else sp_x,
          yvar = if (is.null(x = input$sphover)) dp_y else sp_y
        ))
        # TODO: Get newlines, extra information, and background color working
        if (length(x = cell.hover) == 1) {
          paste(cell.hover, paste('Expression:', plot.data[cell.hover, feature, drop = TRUE]), collapse = '<br />')
        } else {
          NULL
        }
      }
    )
  }
  runGadget(app = ui, server = server)
}

#' Visualize clusters spatially and interactively
#'
#' @inheritParams SpatialPlot
#' @inheritParams DimPlot
#' @inheritParams LinkedPlots
#'
#' @return Returns final plot as a ggplot object
#'
#' @importFrom ggplot2 scale_alpha_ordinal
#' @importFrom miniUI miniPage miniButtonBlock miniTitleBarButton miniContentPanel
#' @importFrom shiny fillRow plotOutput verbatimTextOutput reactiveValues
#' observeEvent stopApp nearPoints renderPlot runGadget
#'
#' @export
#' @concept visualization
#' @concept spatial
#'
ISpatialDimPlot <- function(
  object,
  image = NULL,
  image.scale = "lowres",
  group.by = NULL,
  alpha = c(0.3, 1)
) {
  # Setup gadget UI
  ui <- miniPage(
    miniButtonBlock(miniTitleBarButton(
      inputId = 'done',
      label = 'Done',
      primary = TRUE
    )),
    miniContentPanel(
      fillRow(
        plotOutput(
          outputId = 'plot',
          height = '100%',
          click = clickOpts(id = 'click', clip = TRUE),
          hover = hoverOpts(id = 'hover', delay = 10, nullOutside = TRUE)
        ),
        height = '97%'
      ),
      verbatimTextOutput(outputId = 'info')
    )
  )
  # Get plotting data
  # Prepare plotting data
  image <- image %||% DefaultImage(object = object)
  cells.use <- Cells(x = object[[image]])
  group.by <- group.by %||% 'ident'
  group.data <- FetchData(
    object = object,
    vars = group.by,
    cells = cells.use
  )
  coords <- GetTissueCoordinates(object = object[[image]], scale = image.scale)
  sp_x <- colnames(coords)[1]
  sp_y <- colnames(coords)[2]
  # coordinates should be in image space, so need to flip y when setting or displaying info for points
  flip_y <- function(pt) { if (!is.null(pt)) { pt$y <- max(coords[[sp_y]]) - (pt$y - min(coords[[sp_y]])) }; pt }
  scale.factor <- ScaleFactors(object[[image]])[[image.scale]]
  plot.data <- cbind(coords, group.data)
  plot.data$selected_ <- FALSE
  Idents(object = object) <- group.by
  # Set up the server
  server <- function(input, output, session) {
    click <- reactiveValues(pt = NULL)
    plot.env <- reactiveValues(data = plot.data, alpha.by = NULL)
    # Handle events
    observeEvent(
      eventExpr = input$done,
      handlerExpr = stopApp(returnValue = plot.env$plot)
    )
    observeEvent(
      eventExpr = input$click,
      handlerExpr = {
        click$pt <- flip_y(input$click)
        clicked <- nearPoints(
          df = plot.data,
          coordinfo = click$pt,
          threshold = 10,
          maxpoints = 1,
          xvar = sp_x,
          yvar = sp_y
        )
        plot.env$data <- if (nrow(x = clicked) == 1) {
          cell.clicked <- rownames(x = clicked)
          group.clicked <- plot.data[cell.clicked, group.by, drop = TRUE]
          idx.group <- which(x = plot.data[[group.by]] == group.clicked)
          plot.data[idx.group, 'selected_'] <- TRUE
          plot.data
        } else {
          plot.data
        }
        plot.env$alpha.by <- if (any(plot.env$data$selected_)) {
          'selected_'
        } else {
          NULL
        }
      }
    )
    # Set plot
    output$plot <- renderPlot(
      expr = {
        plot.env$plot <- SingleSpatialPlot(
          data = plot.env$data,
          image = object[[image]],
          col.by = group.by,
          crop = TRUE,
          alpha.by = plot.env$alpha.by,
          pt.size.factor = 1.6
        ) + scale_alpha_ordinal(range = alpha) + NoLegend()
        plot.env$plot
      }
    )
    # Add hover text
    output$info <- renderPrint(
      expr = {
        hovered <- nearPoints(
          df = plot.data,
          coordinfo = flip_y(input$hover),
          threshold = 10,
          maxpoints = 1,
          xvar = sp_x,
          yvar = sp_y
        )
        if (nrow(hovered) == 1) {
          cell.hover <- rownames(hovered)
          # SingleSpatialPlot relies on the spatial coordinates appearing
          # in the first two columns of the returned data.frame - it's kinda
          # fragile but we're obligated to use the same behaviour here
          coords.hover <- hovered[1, colnames(coords)[1:2]] / scale.factor
          group.hover <- hovered[1, group.by]
          sprintf(
            "Cell: %s, Group: %s, Coordinates: (%.2f, %.2f)",
            cell.hover,
            group.hover,
            coords.hover[[1]],
            coords.hover[[2]]
          )
        } else {
          NULL
        }
      }
    )
  }
  runGadget(app = ui, server = server)
}

#' Visualize features spatially and interactively
#'
#' @inheritParams SpatialPlot
#' @inheritParams FeaturePlot
#' @inheritParams LinkedPlots
#'
#' @return Returns final plot as a ggplot object
#'
#' @importFrom ggplot2 scale_fill_gradientn theme scale_alpha guides
#' @importFrom miniUI miniPage miniButtonBlock miniTitleBarButton miniContentPanel
#' @importFrom shiny fillRow sidebarPanel sliderInput selectInput reactiveValues
#' observeEvent stopApp observe updateSelectInput plotOutput renderPlot runGadget
#'
#' @export
#' @concept visualization
#' @concept spatial
ISpatialFeaturePlot <- function(
  object,
  feature,
  image = NULL,
  image.scale = "lowres",
  slot = 'data',
  alpha = c(0.1, 1)
) {
  # Set inital data values
  assay.keys <- Key(object = object)[Assays(object = object)]
  keyed <- sapply(X = assay.keys, FUN = grepl, x = feature)
  assay <- if (any(keyed)) {
    names(x = which(x = keyed))[1]
  } else {
    DefaultAssay(object = object)
  }
  features <- sort(x = rownames(x = GetAssayData(
    object = object,
    layer = slot,
    assay = assay
  )))
  feature.label <- 'Feature to visualize'
  assays.use <- vapply(
    X = Assays(object = object),
    FUN = function(x) {
      return(!IsMatrixEmpty(x = GetAssayData(
        object = object,
        layer = slot,
        assay = x
      )))
    },
    FUN.VALUE = logical(length = 1L)
  )
  assays.use <- sort(x = Assays(object = object)[assays.use])
  # Setup gadget UI
  ui <- miniPage(
    miniButtonBlock(miniTitleBarButton(
      inputId = 'done',
      label = 'Done',
      primary = TRUE
    )),
    miniContentPanel(
      fillRow(
        sidebarPanel(
          sliderInput(
            inputId = 'alpha',
            label = 'Alpha intensity',
            min = 0,
            max = max(alpha),
            value = min(alpha),
            step = 0.01,
            width = '100%'
          ),
          sliderInput(
            inputId = 'pt.size',
            label = 'Point size',
            min = 0,
            max = 5,
            value = 1.6,
            step = 0.1,
            width = '100%'
          ),
          selectInput(
            inputId = 'assay',
            label = 'Assay',
            choices = assays.use,
            selected = assay,
            selectize = FALSE,
            width = '100%'
          ),
          selectInput(
            inputId = 'feature',
            label = feature.label,
            choices = features,
            selected = feature,
            selectize = FALSE,
            width = '100%'
          ),
          selectInput(
            inputId = 'palette',
            label = 'Color scheme',
            choices = names(x = FeaturePalettes),
            selected = 'Spatial',
            selectize = FALSE,
            width = '100%'
          ),
          width = '100%'
        ),
        plotOutput(outputId = 'plot', height = '100%'),
        flex = c(1, 4)
      )
    )
  )
  # Prepare plotting data
  image <- image %||% DefaultImage(object = object)
  cells.use <- Cells(x = object[[image]])
  coords <- GetTissueCoordinates(object = object[[image]], scale = image.scale)
  feature.data <- FetchData(
    object = object,
    vars = feature,
    cells = cells.use,
    layer = slot
  )
  plot.data <- cbind(coords, feature.data)
  server <- function(input, output, session) {
    plot.env <- reactiveValues(
      data = plot.data,
      feature = feature,
      palette = 'Spatial'
    )
    # Observe events
    observeEvent(
      eventExpr = input$done,
      handlerExpr = stopApp(returnValue = plot.env$plot)
    )
    observe(x = {
      assay <- input$assay
      feature.use <- input$feature
      features.assay <- sort(x = rownames(x = GetAssayData(
        object = object,
        layer = slot,
        assay = assay
      )))
      feature.use <- ifelse(
        test = feature.use %in% features.assay,
        yes = feature.use,
        no = features.assay[1]
      )
      updateSelectInput(
        session = session,
        inputId = 'assay',
        label = 'Assay',
        choices = assays.use,
        selected = assay
      )
      updateSelectInput(
        session = session,
        inputId = 'feature',
        label = feature.label,
        choices = features.assay,
        selected = feature.use
      )
    })
    observe(x = {
      feature.use <- input$feature
      try(
        expr = {
          feature.data <- FetchData(
            object = object,
            vars = paste0(Key(object = object[[input$assay]]), feature.use),
            cells = cells.use,
            layer = slot
          )
          colnames(x = feature.data) <- feature.use
          plot.env$data <- cbind(coords, feature.data)
          plot.env$feature <- feature.use
        },
        silent = TRUE
      )
    })
    observe(x = {
      plot.env$palette <- input$palette
    })
    # Create plot
    output$plot <- renderPlot(expr = {
      plot.env$plot <- SingleSpatialPlot(
        data = plot.env$data,
        image = object[[image]],
        col.by = plot.env$feature,
        pt.size.factor = input$pt.size,
        crop = TRUE,
        alpha.by = plot.env$feature
      ) +
        # scale_fill_gradientn(name = plot.env$feature, colours = cols) +
        scale_fill_gradientn(name = plot.env$feature, colours = FeaturePalettes[[plot.env$palette]]) +
        theme(legend.position = 'top') +
        scale_alpha(range = c(input$alpha, 1)) +
        guides(alpha = "none")
      plot.env$plot
    })
  }
  runGadget(app = ui, server = server)
}

#' Interactive Spatial Cell Selection Tool
#'
#' Launch an interactive gadget for lasso-based cell selection from a spatial Seurat object.
#' Supports Visium, SlideSeq, and Vizgen data. Returns the cell names of the selected subset,
#' suitable for downstream subsetting or analysis.
#'
#' @note This function requires the
#'   \href{https://cran.r-project.org/package=plotly}{\pkg{plotly}},
#'   \href{https://cran.r-project.org/package=magrittr}{\pkg{magrittr}},
#'   and \href{https://cran.r-project.org/package=base64enc}{\pkg{base64enc}} packages
#'   to be installed. It also requires \pkg{shiny} and \pkg{miniUI} for the interactive UI.
#'
#' @param object A \code{\link[SeuratObject]{Seurat}} object with spatial data.
#' @param image Name of the spatial image stored in the object. If \code{NULL}, uses the default image for the object.
#' @param image.scale Character. Which image scaling factor to use for spatial coordinate transformation (\code{"lowres"} by default).
#' @param group.by Metadata variable (column name) to use for coloring cell points (e.g., cluster assignment). If \code{NULL}, uses \code{"seurat_clusters"} if available, otherwise all cells are grouped together.
#' @param alpha Numeric transparency value for cell points (default \code{1.0}).
#' @param pt.size.factor Numeric scaling factor for point size (default \code{1.0}).
#' @param overlay_image Logical; if \code{TRUE}, overlays the tissue image in the background of the plot (default \code{TRUE}).
#'
#' @importFrom grDevices png dev.off
#' @importFrom miniUI miniPage gadgetTitleBar miniContentPanel
#' @importFrom shiny uiOutput reactiveVal renderUI tags observeEvent stopApp runGadget
#'
#' @return A character vector of cell names selected via lasso, which can be used to subset the object.
#' @export
#'
#' @examples
#' \dontrun{
#' selected_cells <- InteractiveSpatialPlot(object = brain)
#' selected_cells <- InteractiveSpatialPlot(object = brain, overlay_image = FALSE)
#' }
InteractiveSpatialPlot <- function(
  object,
  image = NULL,
  image.scale = "lowres",
  group.by = NULL,
  alpha = 1.0,
  pt.size.factor = 1.0,
  overlay_image = TRUE
) {
  # Check for required packages, stop with clear message if missing
  required_pkgs <- c("plotly", "magrittr", "base64enc", "shiny")
  
  missing_pkgs <- required_pkgs[
    !vapply(required_pkgs, requireNamespace, quietly = TRUE, FUN.VALUE = logical(1))
  ]
  
  if (length(missing_pkgs) > 0) {
    stop(
      "InteractiveSpatialPlot() functionality requires these packages to be installed: ",
      paste0("'", missing_pkgs, "'", collapse = ", "),
      call. = FALSE
    )
  }

  # Import magrittr pipe locally
  `%>%` <- magrittr::`%>%`

  # Use provided image name or fallback to default
  image <- image %||% DefaultImage(object)

  # Sanity check: requested image must exist in the object
  if (!image %in% names(object@images)) {
    stop("Image '", image, "' not found. Available image(s): ", paste(names(object@images), collapse = ", "))
  }

  # Retrieve the spatial image object
  image_obj <- object[[image]]

  # Determine image technology type (Visium, SlideSeq, or Vizgen)
  img_class <- class(image_obj)[1]
  if (img_class %in% c("VisiumV1", "VisiumV2")) {
    type <- "visium"
  } else if (img_class == "SlideSeq") {
    type <- "slideseq"
  } else if (img_class == "FOV") {
    type <- "vizgen"
  } else {
    stop("Unrecognized image class: ", img_class)
  }

  # Extract and scale cell coordinates according to image type
  if (type == "visium") {
    # For Visium: coordinates stored in centroids, need scaling
    if (!"boundaries" %in% slotNames(image_obj)) {
      stop("Image object does not have a 'boundaries' slot; check if data is truly Visium data")
    }
    centroids <- image_obj@boundaries$centroids
    coords <- setNames(as.data.frame(centroids@coords), c("x", "y"))
    coords$cell <- centroids@cells

    # Scale coordinates to match image pixel units
    scale.factor <- Seurat::ScaleFactors(image_obj)[[image.scale]]
    if (is.null(scale.factor)) stop("Scale factor for '", image.scale, "' not found")

    coords$x_raw <- coords$x  # Store original, unscaled x
    coords$y_raw <- coords$y  # Store original, unscaled y
    coords$x <- coords$x * scale.factor
    coords$y <- coords$y * scale.factor

  } else if (type == "slideseq") {
    # For Slide-seq: coordinates are stored directly
    if (!"coordinates" %in% slotNames(image_obj)) {
      stop("Image object does not have a 'coordinates' slot; check if data is truly Slide-seq data")
    }
    coords <- as.data.frame(image_obj@coordinates)
    coords$cell <- rownames(coords)
    colnames(coords)[1:2] <- c("x", "y")
    coords$x_raw <- coords$x
    coords$y_raw <- coords$y

  } else if (type == "vizgen") {
    # For Vizgen: coordinates in centroids
    if (!"boundaries" %in% slotNames(image_obj)) {
      stop("Vizgen FOV missing 'boundaries' slot")
    }
    centroids <- image_obj@boundaries$centroids
    coords <- as.data.frame(centroids@coords)
    colnames(coords) <- c("x", "y")
    coords$cell <- centroids@cells
    coords$x_raw <- coords$x
    coords$y_raw <- coords$y
  }

  # Get cell-level metadata for grouping/labeling
  meta <- object@meta.data

  # If group.by not given, use 'seurat_clusters' if available; otherwise group all together
  if (is.null(group.by)) {
    group.by <- if ("seurat_clusters" %in% colnames(meta)) "seurat_clusters" else "all"
  }
  # Assign group/cluster for coloring the plot
  if (group.by != "all") {
    coords$group <- meta[coords$cell, group.by]
  } else {
    coords$group <- "all"
  }

  # Compose hover text: show cell name, original (x, y) coordinates, rounded for clarity
  coords$hover <- paste0(
    "Cell: ", coords$cell,
    "<br>x: ", round(coords$x_raw, 1),
    ", y: ", round(coords$y_raw, 1)
  )

  # Prepare background tissue image as a base64-encoded PNG (if available and enabled)
  base64_image <- NULL
  img_width <- NULL
  img_height <- NULL
  if (overlay_image) {
    # Only attempt to overlay image if compatible type and slot present
    if (type == "visium" && "image" %in% slotNames(image_obj)) {
      img_raster <- image_obj@image
    } else if (
      type == "vizgen" &&
      "boundaries" %in% slotNames(image_obj) &&
      "centroids" %in% slotNames(image_obj@boundaries) &&
      "image" %in% slotNames(image_obj@boundaries$centroids)
    ) {
      img_raster <- image_obj@boundaries$centroids@image
    }
    # Convert the raster image array to base64 PNG (for embedding in plotly)
    if (exists("img_raster")) {
      img_width <- dim(img_raster)[2]
      img_height <- dim(img_raster)[1]
      temp_png <- tempfile(fileext = ".png")
      png(temp_png, width = img_width, height = img_height)
      grid::grid.raster(img_raster)
      dev.off()
      img_bytes <- readBin(temp_png, "raw", file.info(temp_png)$size)
      base64_image <- paste0("data:image/png;base64,", base64enc::base64encode(img_bytes))
    }
  }

  # Calculate custom axis tick positions and labels to show original coordinates
  # This is necessary as points are downscaled to fit on the tissue image
  # However, to best retain their original spatial orientation, we plot
  # the original coordinate scale on the axis
  create_axis_ticks <- function(scaled_coords, raw_coords, n_ticks = 6) {
    # Get range of scaled and raw coordinates
    scaled_range <- range(scaled_coords, na.rm = TRUE)
    raw_range <- range(raw_coords, na.rm = TRUE)

    # Create tick positions in the raw coordinate space
    raw_ticks <- pretty(raw_range, n = n_ticks)

    # Calculate corresponding scaled positions
    # Linear interpolation from raw to scaled coordinates
    scale_factor <- diff(scaled_range) / diff(raw_range)
    scaled_ticks <- (raw_ticks - raw_range[1]) * scale_factor + scaled_range[1]

    return(list(tickvals = scaled_ticks, ticktext = as.character(raw_ticks)))
  }

  # Create custom axis ticks for both x and y axes
  x_ticks <- create_axis_ticks(coords$x, coords$x_raw)
  y_ticks <- create_axis_ticks(coords$y, coords$y_raw)

  # Set up the gadget UI with a plotly output area
  ui <- miniPage(
    gadgetTitleBar("Select a subset of cells"),
    miniContentPanel(
      plotly::plotlyOutput("plot", height = "100%"),
      shiny::tags$div(
        shiny::uiOutput("selection_count"),
        style = "position:absolute; bottom:8px; right:10px; padding:4px 6px; background:rgba(255,255,255,0.8); font-size:12px; border-radius:3px; pointer-events:none;"
      )
    )
  )

  # Shiny gadget server logic for interactive plot and lasso selection
  server <- function(input, output, session) {

    current_selection <- shiny::reactiveVal(coords$cell)

    # Render the interactive plotly scattergl plot
    output$plot <- plotly::renderPlotly({
      plt <- plotly::plot_ly(
        data = coords,
        x = ~x,
        y = ~y,
        color = ~group,  # Color by group/cluster if available
        key = ~cell,     # Store cell names for selection retrieval
        type = "scattergl", # Use WebGL for performance with large datasets
        mode = "markers",
        marker = list(size = 2 * pt.size.factor), # Default pt size is 2
        text = ~hover,           # Show hover info (cellid + coordinates)
        hoverinfo = "text",
        alpha = alpha            # Global transparency
      )

      # Overlay the tissue image as background if available
      if (!is.null(base64_image)) {
        plt <- plt %>% plotly::layout(
          images = list(
            list(
              source = base64_image,
              xref = "x", yref = "y", # Anchor to data coordinates
              x = 0,
              y = 0,
              sizex = img_width,
              sizey = img_height,
              sizing = "stretch",
              opacity = 0.6,
              layer = "below"
            )
          )
        )
      }

      # Lock axes to same scale and reverse y for image alignment
      # Set lasso mode and custom axis labels
      plt <- plt %>% plotly::layout(
        dragmode = "lasso",
        yaxis = list(
          autorange = "reversed",
          scaleanchor = "x",
          title = "y",
          tickvals = y_ticks$tickvals,
          ticktext = y_ticks$ticktext
        ),
        xaxis = list(
          scaleanchor = "y",
          title = "x",
          tickvals = x_ticks$tickvals,
          ticktext = x_ticks$ticktext
        )
      )
      plt
    })

    observeEvent(plotly::event_data("plotly_selected"), {
      selected <- plotly::event_data("plotly_selected")

      if (is.null(selected) || NROW(selected) == 0) {
        current_selection(NULL)
      } else {
        keys <- selected$key
        keys <- keys[!is.na(keys)]
        current_selection(keys)
      }
    }, ignoreInit = TRUE)

    output$selection_count <- shiny::renderUI({
      shiny::tags$span(paste0("Selected cells: ", NROW(current_selection())))
    })

    # When user clicks "Done", retrieve lasso selection and close gadget
    observeEvent(input$done, {
      stopApp(current_selection())
    })

    # When user clicks "Cancel", exit gadget and return NULL
    observeEvent(input$cancel, {
      stopApp(NULL)
    })
  }

  # Launch the interactive gadget
  runGadget(ui, server)
}

#' Visualize spatial clustering and expression data.
#'
#' SpatialPlot plots a feature or discrete grouping (e.g. cluster assignments) as
#' spots over the image that was collected. We also provide SpatialFeaturePlot
#' and SpatialDimPlot as wrapper functions around SpatialPlot for a consistent
#' naming framework.
#'
#' @inheritParams HoverLocator
#' @param object A Seurat object
#' @param group.by Name of meta.data column to group the data by
#' @param features Name of the feature to visualize. Provide either group.by OR
#' features, not both.
#' @param images Name of the images to use in the plot(s)
#' @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
#' @param image.alpha Adjust the opacity of the background images. Set to 0 to
#' remove.
#' @param image.scale Choose the scale factor ("lowres"/"hires") to apply in
#' order to matchthe plot with the specified `image` - defaults to "lowres"
#' @param crop Crop the plot in to focus on points plotted. Set to \code{FALSE} to show
#' entire background image.
#' @param slot If plotting a feature, which data slot to pull from (counts,
#' data, or scale.data)
#' @param keep.scale How to handle the color scale across multiple plots. Options are:
#' \itemize{
#'   \item \dQuote{feature} (default; by row/feature scaling): The plots for
#'     each individual feature are scaled to the maximum expression of the
#'     feature across the conditions provided to \code{split.by}
#'   \item \dQuote{all} (universal scaling): The plots for all features and
#'     conditions are scaled to the maximum expression value for the feature
#'     with the highest overall expression
#'   \item \code{NULL} (no scaling): Each individual plot is scaled to the
#'     maximum expression value of the feature in the condition provided to
#'     \code{split.by}; be aware setting \code{NULL} will result in color
#'     scales that are not comparable between plots
#' }
#' @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 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
#' cols.highlight
#' @param cols.highlight A vector of colors to highlight the cells as; ordered
#' the same as the groups in cells.highlight; last color corresponds to
#' unselected cells.
#' @param facet.highlight When highlighting certain groups of cells, split each
#' group into its own plot
#' @param label Whether to label the clusters
#' @param label.size Sets the size of the labels
#' @param label.color Sets the color of the label text
#' @param label.box Whether to put a box around the label text (geom_text vs
#' geom_label)
#' @param repel Repels the labels to prevent overlap
#' @param ncol Number of columns if plotting multiple plots
#' @param combine Combine plots into a single gg object; note that if TRUE;
#' themeing will not work when plotting multiple features/groupings
#' @param pt.size.factor Scale the size of the spots.
#' @param alpha Controls opacity of spots. Provide as a vector specifying the
#' min and max for SpatialFeaturePlot. For SpatialDimPlot, provide a single
#' alpha value for each plot.
#' @param shape Control the shape of the spots - same as the ggplot2 parameter.
#' The default is 21, which plots circles - use 22 to plot squares.
#' @param stroke Control the width of the border around the spots
#' @param stroke.alpha Control the opacity of spot borders (when stroke is specified). 
#' Set to \code{NA} to use the same alpha as the fill.
#' @param interactive Launch an interactive SpatialDimPlot or SpatialFeaturePlot
#' session, see \code{\link{ISpatialDimPlot}} or
#' \code{\link{ISpatialFeaturePlot}} for more details
#' @param do.identify,do.hover DEPRECATED in favor of \code{interactive}
#' @param identify.ident DEPRECATED
#' @param plot_segmentations Define whether plot should plot centroids or segmentations
#'
#' @return If \code{do.identify}, either a vector of cells selected or the object
#' with selected cells set to the value of \code{identify.ident} (if set). Else,
#' if \code{do.hover}, a plotly object with interactive graphics. Else, a ggplot
#' object
#'
#' @importFrom ggplot2 scale_fill_gradientn ggtitle theme element_text scale_alpha
#' @importFrom patchwork wrap_plots
#' @export
#' @concept visualization
#' @concept spatial
#'
#' @examples
#' \dontrun{
#' # For functionality analagous to FeaturePlot
#' SpatialPlot(seurat.object, features = "MS4A1")
#' SpatialFeaturePlot(seurat.object, features = "MS4A1")
#'
#' # For functionality analagous to DimPlot
#' SpatialPlot(seurat.object, group.by = "clusters")
#' SpatialDimPlot(seurat.object, group.by = "clusters")
#' }
#'
SpatialPlot <- function(
  object,
  group.by = NULL,
  features = NULL,
  images = NULL,
  cols = NULL,
  image.alpha = 1,
  image.scale = "lowres",
  crop = TRUE,
  slot = 'data',
  keep.scale = "feature",
  min.cutoff = NA,
  max.cutoff = NA,
  cells.highlight = NULL,
  cols.highlight = c('#DE2D26', 'grey50'),
  facet.highlight = FALSE,
  label = FALSE,
  label.size = 5,
  label.color = 'white',
  label.box = TRUE,
  repel = FALSE,
  ncol = NULL,
  combine = TRUE,
  pt.size.factor = 1.6,
  alpha = c(1, 1),
  shape = 21,
  stroke = NA,
  stroke.alpha = NA,
  interactive = FALSE,
  do.identify = FALSE,
  identify.ident = NULL,
  do.hover = FALSE,
  information = NULL,
  plot_segmentations = FALSE
) {
  if (isTRUE(x = do.hover) || isTRUE(x = do.identify)) {
    warning(
      "'do.hover' and 'do.identify' are deprecated as we are removing plotly-based interactive graphics, use 'interactive' instead for Shiny-based interactivity",
      call. = FALSE,
      immediate. = TRUE
    )
    interactive <- TRUE
  }
  if (!is.null(x = group.by) & !is.null(x = features)) {
    stop("Please specific either group.by or features, not both.")
  }
  images <- images %||% Images(object = object, assay = DefaultAssay(object = object))
  if (length(x = images) == 0) {
    images <- Images(object = object)
  }
  if (length(x = images) < 1) {
    stop("Could not find any spatial image information")
  }

  # Check keep.scale param for valid entries
  if (!(is.null(x = keep.scale)) && !(keep.scale %in% c("feature", "all"))) {
    stop("`keep.scale` must be set to either `feature`, `all`, or NULL")
  }

  cells <- unique(CellsByImage(object, images = images, unlist = TRUE))
  if (is.null(x = features)) {
    if (interactive) {
      # default alpha is 1 but interactive plotting requires 
      # a range for proper cluster selection highlighting
      if (identical(alpha, c(1, 1))) { 
        alpha <- c(0.1, 1)
      }
      tryCatch(
         expr = {
           return(ISpatialDimPlot(
             object = object,
             image = images[1],
             image.scale = image.scale,
             group.by = group.by,
             alpha = alpha
           ))
         },
         error = function(e) {
          # error can occur when image and assay don't match
          # or when the default assay set doesn't have data corresponding to the default ident etc.
          if (grepl("arguments imply differing number of rows", conditionMessage(e))) {
            stop(
              "Cells were removed due to missing data; check if the specified image and assay are correct.\n",
              call. = FALSE
            )
          } else {
            stop(e)
          }
         }
      )
    }
    group.by <- group.by %||% 'ident'
    object[['ident']] <- Idents(object = object)
    data <- object[[group.by]]
    data <- data[cells,,drop=F]
    for (group in group.by) {
      if (!is.factor(x = data[, group])) {
        data[, group] <- factor(x = data[, group])
      }
    }
  } else {
    if (interactive) {
      return(ISpatialFeaturePlot(
        object = object,
        feature = features[1],
        image = images[1],
        image.scale = image.scale,
        slot = slot,
        alpha = alpha
      ))
    }
    data <- FetchData(
      object = object,
      vars = features,
      cells = cells,
      layer = slot,
      clean = FALSE
    )
    features <- colnames(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,
      feature = features
    )
    max.cutoff <- mapply(
      FUN = function(cutoff, feature) {
        return(ifelse(
          test = is.na(x = cutoff),
          yes = max(data[, feature]),
          no = cutoff
        ))
      },
      cutoff = max.cutoff,
      feature = features
    )
    check.lengths <- unique(x = vapply(
      X = list(features, min.cutoff, max.cutoff),
      FUN = length,
      FUN.VALUE = numeric(length = 1)
    ))
    if (length(x = check.lengths) != 1) {
      stop("There must be the same number of minimum and maximum cuttoffs as there are features")
    }
    # Apply cutoffs
    data <- sapply(
      X = 1:ncol(x = data),
      FUN = function(index) {
        data.feature <- as.vector(x = data[, index])
        min.use <- SetQuantile(cutoff = min.cutoff[index], data.feature)
        max.use <- SetQuantile(cutoff = max.cutoff[index], data.feature)
        data.feature[data.feature < min.use] <- min.use
        data.feature[data.feature > max.use] <- max.use
        return(data.feature)
      }
    )
    colnames(x = data) <- features
    rownames(x = data) <- cells
  }
  features <- colnames(x = data)
  colnames(x = data) <- features
  rownames(x = data) <- cells
  facet.highlight <- facet.highlight && (!is.null(x = cells.highlight) && is.list(x = cells.highlight))
  if (do.hover) {
    if (length(x = images) > 1) {
      images <- images[1]
      warning(
        "'do.hover' requires only one image, using image ",
        images,
        call. = FALSE,
        immediate. = TRUE
      )
    }
    if (length(x = features) > 1) {
      features <- features[1]
      type <- ifelse(test = is.null(x = group.by), yes = 'feature', no = 'grouping')
      warning(
        "'do.hover' requires only one ",
        type,
        ", using ",
        features,
        call. = FALSE,
        immediate. = TRUE
      )
    }
    if (facet.highlight) {
      warning(
        "'do.hover' requires no faceting highlighted cells",
        call. = FALSE,
        immediate. = TRUE
      )
      facet.highlight <- FALSE
    }
  }
  if (facet.highlight) {
    if (length(x = images) > 1) {
      images <- images[1]
      warning(
        "Faceting the highlight only works with a single image, using image ",
        images,
        call. = FALSE,
        immediate. = TRUE
      )
    }
    ncols <- length(x = cells.highlight)
  } else {
    ncols <- length(x = images)
  }
  plots <- vector(
    mode = "list",
    length = length(x = features) * ncols
  )

  # Get max across all features
  if (!(is.null(x = keep.scale)) && keep.scale == "all") {
    max.feature.value <- max(apply(data, 2, function(x) max(x, na.rm = TRUE)))
  }

  for (i in 1:ncols) {
    plot.idx <- i
    image.idx <- ifelse(test = facet.highlight, yes = 1, no = i)
    image.use <- object[[images[[image.idx]]]]

    is_visium_v2 <- inherits(image.use, "VisiumV2")
    old_axis_orientation <- (!.hasSlot(image.use, "coords_x_orientation")) || (.hasSlot(image.use, "coords_x_orientation") && (slot(image.use, "coords_x_orientation") != 'horizontal'))
    
    if (is_visium_v2 && old_axis_orientation) {
      stop(
        "Please run `UpdateSeuratObject` on your Seurat object first to ensure that data aligns to the image ", images[[image.idx]], " when plotting.",
        call. = TRUE
      )
    }

    # When plotting segmentations, set the default boundary (temporarily) to segmentations
    if (plot_segmentations == TRUE && inherits(image.use, "VisiumV2") && 
        "segmentations" %in% names(image.use)) {
      db <- DefaultBoundary(image.use)
      on.exit(DefaultBoundary(image.use) <- db, add = TRUE) # Reset on exit
      DefaultBoundary(image.use) <- "segmentations"
    }
    coordinates <- GetTissueCoordinates(object = image.use, scale = image.scale)
    highlight.use <- if (facet.highlight) {
      cells.highlight[i]
    } else {
      cells.highlight
    }
    for (j in seq_along(features)) {
      cols.unset <- is.factor(x = data[, features[j]]) && is.null(x = cols)
      if (cols.unset) {
        cols <- hue_pal()(n = length(x = levels(x = data[, features[j]])))
        names(x = cols) <- levels(x = data[, features[j]])
      }

      # Get feature max for individual feature
      if (!(is.null(x = keep.scale)) && keep.scale == "feature" && !inherits(x = data[, features[j]], what = "factor") ) {
        max.feature.value <- max(data[, features[j]])
      }

      # Check if object is of type Visium and contains segmentations
      has_visium_segm_data <- inherits(image.use, "VisiumV2") &&
                              !is.null(image.use@boundaries$segmentations) &&
                              "sf.data" %in% slotNames(image.use@boundaries$segmentations)

      # GetTissueCoordinates will not always return a "cell" column (e.g., Visium V1)
      if (!("cell" %in% colnames(x = coordinates))) {
        coordinates$cell <- rownames(x = coordinates)
      }

      idx <- match(coordinates$cell, rownames(x = data))
      plot.data <- cbind(coordinates, data[idx, features[j], drop = FALSE])

      plot <- SingleSpatialPlot(
        data = plot.data,
        image = image.use,
        image.scale = image.scale,
        image.alpha = image.alpha,
        col.by = features[j],
        cols = cols,
        alpha.by = if (is.null(x = group.by)) {
          features[j]
        } else {
          NULL
        },
        pt.alpha = if (!is.null(x = group.by)) {
          alpha[j]
        } else {
          NULL
        },
        geom = if (inherits(x = image.use, what = "STARmap")) {
          "poly_starmap"
        } else if (has_visium_segm_data && plot_segmentations) {
          "poly"
        } else {
          "spatial"
        },
        cells.highlight = highlight.use,
        cols.highlight = cols.highlight,
        pt.size.factor = pt.size.factor,
        shape = shape,
        stroke = stroke,
        stroke.alpha = stroke.alpha,
        crop = crop
      )
      if (is.null(x = group.by)) {
        plot <- plot +
          scale_fill_gradientn(
            name = features[j],
            colours = SpatialColors(n = 100)
          ) +
          theme(legend.position = 'top') +
          scale_alpha(range = alpha) +
          guides(alpha = "none")
      } else if (label) {
        plot <- LabelClusters(
          plot = plot,
          id = ifelse(
            test = is.null(x = cells.highlight),
            yes = features[j],
            no = 'highlight'
          ),
          geom = if (inherits(x = image.use, what = "STARmap") || (has_visium_segm_data && plot_segmentations)) {
            'GeomPolygon'
          } else {
            'GeomSpatial'
          },
          repel = repel,
          size = label.size,
          color = label.color,
          box = label.box,
          position = "nearest"
        )
      }
      if (j == 1 && length(x = images) > 1 && !facet.highlight) {
        plot <- plot +
          ggtitle(label = images[[image.idx]]) +
          theme(plot.title = element_text(hjust = 0.5))
      }
      if (facet.highlight) {
        plot <- plot +
          ggtitle(label = names(x = cells.highlight)[i]) +
          theme(plot.title = element_text(hjust = 0.5)) +
          NoLegend()
      }
      if (has_visium_segm_data && plot_segmentations && !is.null(group.by)) {
        # Add legend guides to show filled squares next to labels when plotting segmentations
        plot <- plot + guides(fill = guide_legend(override.aes = list(alpha = 1, color = "black", linewidth = 0.2, size = 2)))
      }
      # Plot multiple images depending on keep.scale
      if (!(is.null(x = keep.scale)) && !inherits(x = data[, features[j]], "factor")) {
        plot <- suppressMessages(plot & scale_fill_gradientn(colors = SpatialColors(n = 100), limits = c(NA, max.feature.value)))
      }

      plots[[plot.idx]] <- plot
      plot.idx <- plot.idx + ncols
      if (cols.unset) {
        cols <- NULL
      }
    }
  }

  if (combine) {
    if (!is.null(x = ncol)) {
      return(wrap_plots(plots = plots, ncol = ncol))
    }
    if (length(x = images) > 1) {
      return(wrap_plots(plots = plots, ncol = length(x = images)))
    }
    return(wrap_plots(plots = plots))
  }
  return(plots)
}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Other plotting functions
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

#' Plot the Barcode Distribution and Calculated Inflection Points
#'
#' This function plots the calculated inflection points derived from the barcode-rank
#' distribution.
#'
#' See [CalculateBarcodeInflections()] to calculate inflection points and
#' [SubsetByBarcodeInflections()] to subsequently subset the Seurat object.
#'
#' @param object Seurat object
#'
#' @return Returns a `ggplot2` object showing the by-group inflection points and provided
#'   (or default) rank threshold values in grey.
#'
#' @importFrom methods slot
#' @importFrom cowplot theme_cowplot
#' @importFrom ggplot2 ggplot geom_line geom_vline
#'
#' @export
#' @concept visualization
#'
#' @author Robert A. Amezquita, \email{robert.amezquita@fredhutch.org}
#' @seealso \code{\link{CalculateBarcodeInflections}} \code{\link{SubsetByBarcodeInflections}}
#'
#' @examples
#' data("pbmc_small")
#' pbmc_small <- CalculateBarcodeInflections(pbmc_small, group.column = 'groups')
#' BarcodeInflectionsPlot(pbmc_small)
#'
BarcodeInflectionsPlot <- function(object) {
  cbi.data <- Tool(object = object, slot = 'CalculateBarcodeInflections')
  if (is.null(x = cbi.data)) {
    stop("Barcode inflections not calculated, please run CalculateBarcodeInflections")
  }
  ## Extract necessary data frames
  inflection_points <- cbi.data$inflection_points
  barcode_distribution <- cbi.data$barcode_distribution
  threshold_values <- cbi.data$threshold_values
  # Set a cap to max rank to avoid plot being overextended
  if (threshold_values$rank[[2]] > max(barcode_distribution$rank, na.rm = TRUE)) {
    threshold_values$rank[[2]] <- max(barcode_distribution$rank, na.rm = TRUE)
  }
  ## Infer the grouping/barcode variables
  group_var <- colnames(x = barcode_distribution)[1]
  barcode_var <- colnames(x = barcode_distribution)[2]
  barcode_distribution[, barcode_var] <- log10(x = barcode_distribution[, barcode_var] + 1)
  ## Make the plot
  plot <- ggplot(
    data = barcode_distribution,
    mapping = aes(
      x = .data[['rank']],
      y = .data[[barcode_var]],
      group = .data[[group_var]],
      colour = .data[[group_var]]
    )
  ) +
    geom_line() +
    geom_vline(
      data = threshold_values,
      aes(xintercept = .data[['rank']]),
      linetype = "dashed",
      colour = 'grey60',
      size = 0.5
    ) +
    geom_vline(
      data = inflection_points,
      mapping = aes(
        xintercept = .data[['rank']],
        group = .data[[group_var]],
        colour = .data[[group_var]]
      ),
      linetype = "dashed"
    ) +
    theme_cowplot()
  return(plot)
}

#' Dot plot visualization
#'
#' Intuitive way of visualizing how feature expression changes across different
#' identity classes (clusters). The size of the dot encodes the percentage of
#' cells within a class, while the color encodes the AverageExpression level
#' across all cells within a class (blue is high).
#'
#' @param object Seurat object
#' @param assay Name of assay to use, defaults to the active assay
#' @param features Input vector of features, or named list of feature vectors
#' if feature-grouped panels are desired (replicates the functionality of the
#' old SplitDotPlotGG)
#' @param cols Colors to plot: the name of a palette from
#' \code{RColorBrewer::brewer.pal.info}, a pair of colors defining a gradient,
#' or 3+ colors defining multiple gradients (if split.by is set)
#' @param col.min Minimum scaled average expression threshold (everything
#' smaller will be set to this)
#' @param col.max Maximum scaled average expression threshold (everything larger
#' will be set to this)
#' @param dot.min The fraction of cells at which to draw the smallest dot
#' (default is 0). All cell groups with less than this expressing the given
#' gene will have no dot drawn.
#' @param dot.scale Scale the size of the points, similar to cex
#' @param idents Identity classes to include in plot (default is all)
#' @param group.by Factor to group the cells by
#' @param split.by A factor in object metadata to split the plot by, pass 'ident'
#' to split by cell identity
#' see \code{\link{FetchData}} for more details
#' @param cluster.idents Whether to order identities by hierarchical clusters
#' based on given features, default is FALSE
#' @param scale Determine whether the data is scaled, TRUE for default
#' @param scale.by Scale the size of the points by 'size' or by 'radius'
#' @param scale.min Set lower limit for scaling, use NA for default
#' @param scale.max Set upper limit for scaling, use NA for default
#'
#' @return A ggplot object
#'
#' @importFrom grDevices colorRampPalette
#' @importFrom cowplot theme_cowplot
#' @importFrom ggplot2 ggplot geom_point scale_size scale_radius
#' theme element_blank labs scale_color_identity scale_color_distiller
#' scale_color_gradient guides guide_legend guide_colorbar
#' facet_grid unit
#' @importFrom scattermore geom_scattermore
#' @importFrom stats dist hclust
#' @importFrom RColorBrewer brewer.pal.info
#'
#' @export
#' @concept visualization
#'
#' @aliases SplitDotPlotGG
#' @seealso \code{RColorBrewer::brewer.pal.info}
#'
#' @examples
#' data("pbmc_small")
#' cd_genes <- c("CD247", "CD3E", "CD9")
#' DotPlot(object = pbmc_small, features = cd_genes)
#' pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE)
#' DotPlot(object = pbmc_small, features = cd_genes, split.by = 'groups')
#'
DotPlot <- function(
  object,
  features,
  assay = NULL,
  cols = c("lightgrey", "blue"),
  col.min = -2.5,
  col.max = 2.5,
  dot.min = 0,
  dot.scale = 6,
  idents = NULL,
  group.by = NULL,
  split.by = NULL,
  cluster.idents = FALSE,
  scale = TRUE,
  scale.by = 'radius',
  scale.min = NA,
  scale.max = NA
) {
  assay <- assay %||% DefaultAssay(object = object)
  DefaultAssay(object = object) <- assay
  split.colors <- !is.null(x = split.by) && !any(cols %in% rownames(x = brewer.pal.info))
  scale.func <- switch(
    EXPR = scale.by,
    'size' = scale_size,
    'radius' = scale_radius,
    stop("'scale.by' must be either 'size' or 'radius'")
  )
  feature.groups <- NULL
  if (is.list(features) | any(!is.na(names(features)))) {
    feature.groups <- unlist(x = sapply(
      X = 1:length(features),
      FUN = function(x) {
        return(rep(x = names(x = features)[x], each = length(features[[x]])))
      }
    ))
    if (any(is.na(x = feature.groups))) {
      warning(
        "Some feature groups are unnamed.",
        call. = FALSE,
        immediate. = TRUE
      )
    }
    features <- unlist(x = features)
    names(x = feature.groups) <- features
  }
  cells <- unlist(x = CellsByIdentities(object = object, cells = colnames(object[[assay]]), idents = idents))
  data.features <- FetchData(object = object, vars = features, cells = cells)
  data.features$id <- if (is.null(x = group.by)) {
    Idents(object = object)[cells, drop = TRUE]
  } else {
    object[[group.by, drop = TRUE]][cells, drop = TRUE]
  }
  if (!is.factor(x = data.features$id)) {
    data.features$id <- factor(x = data.features$id)
  }
  id.levels <- levels(x = data.features$id)
  data.features$id <- as.vector(x = data.features$id)
  if (!is.null(x = split.by)) {
    splits <- FetchData(object = object, vars = split.by)[cells, split.by]
    if (split.colors) {
      if (length(x = unique(x = splits)) > length(x = cols)) {
        stop(paste0("Need to specify at least ", length(x = unique(x = splits)), " colors using the cols parameter"))
      }
      cols <- cols[1:length(x = unique(x = splits))]
      names(x = cols) <- unique(x = splits)
    }
    data.features$id <- paste(data.features$id, splits, sep = '_')
    unique.splits <- unique(x = splits)
    id.levels <- paste0(rep(x = id.levels, each = length(x = unique.splits)), "_", rep(x = unique(x = splits), times = length(x = id.levels)))
  }
  data.plot <- lapply(
    X = unique(x = data.features$id),
    FUN = function(ident) {
      data.use <- data.features[data.features$id == ident, 1:(ncol(x = data.features) - 1), drop = FALSE]
      avg.exp <- apply(
        X = data.use,
        MARGIN = 2,
        FUN = function(x) {
          return(mean(x = expm1(x = x)))
        }
      )
      pct.exp <- apply(X = data.use, MARGIN = 2, FUN = PercentAbove, threshold = 0)
      return(list(avg.exp = avg.exp, pct.exp = pct.exp))
    }
  )
  names(x = data.plot) <- unique(x = data.features$id)
  if (cluster.idents) {
    mat <- do.call(
      what = rbind,
      args = lapply(X = data.plot, FUN = unlist)
    )
    mat <- scale(x = mat)
    id.levels <- id.levels[hclust(d = dist(x = mat))$order]
  }
  data.plot <- lapply(
    X = names(x = data.plot),
    FUN = function(x) {
      data.use <- as.data.frame(x = data.plot[[x]])
      data.use$features.plot <- rownames(x = data.use)
      data.use$id <- x
      return(data.use)
    }
  )
  data.plot <- do.call(what = 'rbind', args = data.plot)
  if (!is.null(x = id.levels)) {
    data.plot$id <- factor(x = data.plot$id, levels = id.levels)
  }
  ngroup <- length(x = levels(x = data.plot$id))
  if (ngroup == 1) {
    scale <- FALSE
    warning(
      "Only one identity present, the expression values will be not scaled",
      call. = FALSE,
      immediate. = TRUE
    )
  } else if (ngroup < 5 & scale) {
    warning(
      "Scaling data with a low number of groups may produce misleading results",
      call. = FALSE,
      immediate. = TRUE
    )
  }
  avg.exp.scaled <- sapply(
    X = unique(x = data.plot$features.plot),
    FUN = function(x) {
      data.use <- data.plot[data.plot$features.plot == x, 'avg.exp']
      if (scale) {
        data.use <- scale(x = log1p(data.use))
        data.use <- MinMax(data = data.use, min = col.min, max = col.max)
      } else {
        data.use <- log1p(x = data.use)
      }
      return(data.use)
    }
  )
  avg.exp.scaled <- as.vector(x = t(x = avg.exp.scaled))
  if (split.colors) {
    avg.exp.scaled <- as.numeric(x = cut(x = avg.exp.scaled, breaks = 20))
  }
  data.plot$avg.exp.scaled <- avg.exp.scaled
  data.plot$features.plot <- factor(
    x = data.plot$features.plot,
    levels = features
  )
  data.plot$pct.exp[data.plot$pct.exp < dot.min] <- NA
  data.plot$pct.exp <- data.plot$pct.exp * 100
  if (split.colors) {
    splits.use <- unlist(x = lapply(
      X = data.plot$id,
      FUN = function(x)
        sub(
          paste0(".*_(",
                 paste(sort(unique(x = splits), decreasing = TRUE),
                       collapse = '|'
                 ),")$"),
          "\\1",
          x
        )
    )
    )
    data.plot$colors <- mapply(
      FUN = function(color, value) {
        return(colorRampPalette(colors = c('grey', color))(20)[value])
      },
      color = cols[splits.use],
      value = avg.exp.scaled
    )
  }
  color.by <- ifelse(test = split.colors, yes = 'colors', no = 'avg.exp.scaled')
  if (!is.na(x = scale.min)) {
    data.plot[data.plot$pct.exp < scale.min, 'pct.exp'] <- scale.min
  }
  if (!is.na(x = scale.max)) {
    data.plot[data.plot$pct.exp > scale.max, 'pct.exp'] <- scale.max
  }
  if (!is.null(x = feature.groups)) {
    data.plot$feature.groups <- factor(
      x = feature.groups[data.plot$features.plot],
      levels = unique(x = feature.groups)
    )
  }
  plot <- ggplot(data = data.plot, mapping = aes(x = .data[["features.plot"]], y = .data[["id"]])) +
    geom_point(mapping = aes(size = .data[["pct.exp"]], color = .data[[color.by]])) +
    scale.func(range = c(0, dot.scale), limits = c(scale.min, scale.max)) +
    theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
    guides(size = guide_legend(title = 'Percent Expressed')) +
    labs(
      x = 'Features',
      y = ifelse(test = is.null(x = split.by), yes = 'Identity', no = 'Split Identity')
    ) +
    theme_cowplot()
  if (!is.null(x = feature.groups)) {
    plot <- plot + facet_grid(
      rows = ~feature.groups,
      scales = "free_x",
      space = "free_x",
      switch = "y"
    ) + theme(
      panel.spacing = unit(x = 1, units = "lines"),
      strip.background = element_blank()
    )
  }
  if (split.colors) {
    plot <- plot + scale_color_identity()
  } else if (length(x = cols) == 1) {
    plot <- plot + scale_color_distiller(palette = cols)
  } else {
    plot <- plot + scale_color_gradient(low = cols[1], high = cols[2])
  }
  if (!split.colors) {
    plot <- plot + guides(color = guide_colorbar(title = 'Average Expression'))
  }
  return(plot)
}

#' Quickly Pick Relevant Dimensions
#'
#' Plots per-component standard deviations (or approximate singular values if running PCAFast),
#' percent variance explained per principal component, or cumulative percent variance explained,
#' to help pick an elbow in the graph. This elbow often corresponds well with significant
#' dimensions and is much faster to run than Jackstraw.
#'
#' @param object Seurat object
#' @param ndims Number of dimensions to plot (positive integer; capped by stored components)
#' @param reduction Reduction technique to plot (default is 'pca')
#' @param plot_type One of \code{"stdev"} (default), \code{"variance"} (per-PC \% variance), or
#'   \code{"cumulative_variance"} (running sum of those percentages; equals 100\% at the last
#'   stored PC when \code{ndims} spans all of them)
#'
#' @return A ggplot object
#'
#' @importFrom cowplot theme_cowplot
#' @importFrom ggplot2 ggplot geom_point labs aes
#' @export
#' @concept visualization
#'
#' @examples
#' data("pbmc_small")
#' ElbowPlot(object = pbmc_small)
#' ElbowPlot(object = pbmc_small, plot_type = "variance")
#' ElbowPlot(object = pbmc_small, plot_type = "cumulative_variance")
#'
ElbowPlot <- function(object, ndims = 20, reduction = 'pca', plot_type = c("stdev", "variance", "cumulative_variance")) {
  plot_type <- match.arg(plot_type)
  if (!is.numeric(ndims) || length(ndims) != 1L || !is.finite(ndims) || ndims < 1 || ndims != as.integer(ndims)) {
    stop("'ndims' must be a single positive integer", call. = FALSE)
  }
  ndims <- as.integer(ndims)
  data.use <- Stdev(object = object, reduction = reduction)
  if (length(x = data.use) == 0) {
    stop(paste("No standard deviation info stored for", reduction))
  }
  if (anyNA(data.use)) {
    stop("Standard deviations contain NA for reduction ", reduction, call. = FALSE)
  }
  if (ndims > length(x = data.use)) {
    warning("The object only has information for ", length(x = data.use), " dimensions")
    ndims <- length(x = data.use)
  }
  if (plot_type == "stdev") {
    y_label <- "Standard Deviation"
    y_data <- data.use[1:ndims]
  } else {
    den <- sum(data.use^2)
    if (!is.finite(den) || den == 0) {
      stop("Cannot compute variance explained: sum of squared standard deviations is not positive for reduction ", reduction, call. = FALSE)
    }
    pct <- data.use^2 / den * 100
    if (plot_type == "variance") {
      y_label <- "Percentage of Variance Explained"
      y_data <- pct[1:ndims]
    } else {
      y_label <- "Cumulative % Variance Explained"
      y_data <- cumsum(pct)[1:ndims]
    }
  }
  plot <- ggplot(data = data.frame(dims = 1:ndims, y_data = y_data)) +
    geom_point(mapping = aes(x = .data[["dims"]], y = .data[["y_data"]])) +
    labs(x = gsub(pattern = '_$', replacement = '', x = Key(object = object[[reduction]])), y = y_label) +
    theme_cowplot()
  return(plot)
}


#' Boxplot of correlation of a variable (e.g. number of UMIs) with expression
#' data
#'
#' @param object Seurat object
#' @param assay Assay where the feature grouping info and correlations are
#' stored
#' @param feature.group Name of the column in meta.features where the feature
#' grouping info is stored
#' @param cor Name of the column in meta.features where correlation info is
#' stored
#'
#' @return Returns a ggplot boxplot of correlations split by group
#'
#' @importFrom ggplot2 geom_boxplot scale_fill_manual geom_hline
#' @importFrom cowplot theme_cowplot
#' @importFrom scales brewer_pal
#' @importFrom stats complete.cases
#'
#' @export
#' @concept visualization
#'
GroupCorrelationPlot <- function(
  object,
  assay = NULL,
  feature.group = "feature.grp",
  cor = "nCount_RNA_cor"
) {
  assay <- assay %||% DefaultAssay(object = object)
  data <- object[[assay]][[c(feature.group, cor)]]
  data <- data[complete.cases(data), ]
  colnames(x = data) <- c('grp', 'cor')
  data$grp <- as.character(data$grp)
  plot <- ggplot(data = data, aes(x = .data[["grp"]], y = .data[["cor"]], fill = .data[["grp"]])) +
    geom_boxplot() +
    theme_cowplot() +
    scale_fill_manual(values = rev(x = brewer_pal(palette = 'YlOrRd')(n = 7))) +
    ylab(paste(
      "Correlation with",
      gsub(x = cor, pattern = "_cor", replacement = "")
    )) +
    geom_hline(yintercept = 0) +
    NoLegend() +
    theme(
      axis.line.x = element_blank(),
      axis.title.x = element_blank(),
      axis.ticks.x = element_blank(),
      axis.text.x = element_blank()
    )
  return(plot)
}

#' JackStraw Plot
#'
#' Plots the results of the JackStraw analysis for PCA significance. For each
#' PC, plots a QQ-plot comparing the distribution of p-values for all genes
#' across each PC, compared with a uniform distribution. Also determines a
#' p-value for the overall significance of each PC (see Details).
#'
#' Significant PCs should show a p-value distribution (black curve) that is
#' strongly skewed to the left compared to the null distribution (dashed line)
#' The p-value for each PC is based on a proportion test comparing the number
#' of genes with a p-value below a particular threshold (score.thresh), compared with the
#' proportion of genes expected under a uniform distribution of p-values.
#'
#' @param object Seurat object
#' @param dims Dims to plot
#' @param cols Vector of colors, each color corresponds to an individual PC. 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 reduction reduction to pull jackstraw info from
#' @param xmax X-axis maximum on each QQ plot.
#' @param ymax Y-axis maximum on each QQ plot.
#'
#' @return A ggplot object
#'
#' @author Omri Wurtzel
#' @seealso \code{\link{ScoreJackStraw}}
#'
#' @importFrom stats qunif
#' @importFrom scales hue_pal
#' @importFrom ggplot2 ggplot stat_qq labs xlim ylim
#' coord_flip geom_abline guides guide_legend
#' @importFrom cowplot theme_cowplot
#'
#' @export
#' @concept visualization
#'
#' @examples
#' data("pbmc_small")
#' JackStrawPlot(object = pbmc_small)
#'
JackStrawPlot <- function(
  object,
  dims = 1:5,
  cols = NULL,
  reduction = 'pca',
  xmax = 0.1,
  ymax = 0.3
) {
  pAll <- JS(object = object[[reduction]], slot = 'empirical')
  if (max(dims) > ncol(x = pAll)) {
    stop("Max dimension is ", ncol(x = pAll))
  }
  pAll <- pAll[, dims, drop = FALSE]
  pAll <- as.data.frame(x = pAll)
  data.plot <- Melt(x = pAll)
  colnames(x = data.plot) <- c("Contig", "PC", "Value")
  score.df <- JS(object = object[[reduction]], slot = 'overall')
  if (nrow(x = score.df) < max(dims)) {
    stop("Jackstraw procedure not scored for all the provided dims. Please run ScoreJackStraw.")
  }
  score.df <- score.df[dims, , drop = FALSE]
  if (nrow(x = score.df) == 0) {
    stop(paste0("JackStraw hasn't been scored. Please run ScoreJackStraw before plotting."))
  }
  data.plot$PC.Score <- rep(
    x = paste0("PC ", score.df[ ,"PC"], ": ", sprintf("%1.3g", score.df[ ,"Score"])),
    each = length(x = unique(x = data.plot$Contig))
  )
  data.plot$PC.Score <- factor(
    x = data.plot$PC.Score,
    levels = paste0("PC ", score.df[, "PC"], ": ", sprintf("%1.3g", score.df[, "Score"]))
  )
  if (is.null(x = cols)) {
    cols <- hue_pal()(length(x = dims))
  }
  if (length(x = cols) < length(x = dims)) {
    stop("Not enough colors for the number of dims selected")
  }
  gp <- ggplot(data = data.plot, mapping = aes(sample = .data[['Value']], color = .data[['PC.Score']])) +
    stat_qq(distribution = qunif) +
    labs(x = "Theoretical [runif(1000)]", y = "Empirical") +
    scale_color_manual(values = cols) +
    xlim(0, ymax) +
    ylim(0, xmax) +
    coord_flip() +
    geom_abline(intercept = 0, slope = 1, linetype = "dashed", na.rm = TRUE) +
    guides(color = guide_legend(title = "PC: p-value")) +
    theme_cowplot()
  return(gp)
}

#' Plot clusters as a tree
#'
#' Plots previously computed tree (from BuildClusterTree)
#'
#' @param object Seurat object
#' @param direction A character string specifying the direction of the tree (default is downwards)
#' Possible options: "rightwards", "leftwards", "upwards", and "downwards".
#' @param \dots Additional arguments to
#' \code{\link[ape:plot.phylo]{ape::plot.phylo}}
#'
#' @return Plots dendogram (must be precomputed using BuildClusterTree), returns no value
#'
#' @export
#' @concept visualization
#'
#' @examples
#' \dontrun{
#' if (requireNamespace("ape", quietly = TRUE)) {
#'   data("pbmc_small")
#'   pbmc_small <- BuildClusterTree(object = pbmc_small)
#'   PlotClusterTree(object = pbmc_small)
#' }
#' }
PlotClusterTree <- function(object, direction = "downwards", ...) {
  if (isFALSE(x = requireNamespace('ape', quietly = TRUE))) {
    stop(cluster.ape, call. = FALSE)
  }
  if (is.null(x = Tool(object = object, slot = "BuildClusterTree"))) {
    stop("Phylogenetic tree does not exist, build using BuildClusterTree")
  }
  data.tree <- Tool(object = object, slot = "BuildClusterTree")
  ape::plot.phylo(x = data.tree, direction = direction, ...)
  ape::nodelabels()
}

#' Visualize Dimensional Reduction genes
#'
#' Visualize top genes associated with reduction components
#'
#' @param object Seurat object
#' @param reduction Reduction technique to visualize results for
#' @param dims Number of dimensions to display
#' @param nfeatures Number of genes to display
#' @param col Color of points to use
#' @param projected Use reduction values for full dataset (i.e. projected
#' dimensional reduction values)
#' @param balanced Return an equal number of genes with + and - scores. If
#' FALSE (default), returns the top genes ranked by the scores absolute values
#' @param ncol Number of columns to display
#' @param combine Combine plots into a single \code{patchwork}
#' ggplot object. If \code{FALSE}, return a list of ggplot objects
#'
#' @return A \code{patchwork} ggplot object if
#' \code{combine = TRUE}; otherwise, a list of ggplot objects
#'
#' @importFrom patchwork wrap_plots
#' @importFrom cowplot theme_cowplot
#' @importFrom ggplot2 ggplot geom_point labs
#' @export
#' @concept visualization
#'
#' @examples
#' data("pbmc_small")
#' VizDimLoadings(object = pbmc_small)
#'
VizDimLoadings <- function(
  object,
  dims = 1:5,
  nfeatures = 30,
  col = 'blue',
  reduction = 'pca',
  projected = FALSE,
  balanced = FALSE,
  ncol = NULL,
  combine = TRUE
) {
  if (is.null(x = ncol)) {
    ncol <- 2
    if (length(x = dims) == 1) {
      ncol <- 1
    }
    if (length(x = dims) > 6) {
      ncol <- 3
    }
    if (length(x = dims) > 9) {
      ncol <- 4
    }
  }
  loadings <- Loadings(object = object[[reduction]], projected = projected)
  features <- lapply(
    X = dims,
    FUN = TopFeatures,
    object = object[[reduction]],
    nfeatures = nfeatures,
    projected = projected,
    balanced = balanced
  )
  features <- lapply(
    X = features,
    FUN = unlist,
    use.names = FALSE
  )
  loadings <- loadings[unlist(x = features), dims, drop = FALSE]
  names(x = features) <- colnames(x = loadings) <- as.character(x = dims)
  plots <- lapply(
    X = as.character(x = dims),
    FUN = function(i) {
      data.plot <- as.data.frame(x = loadings[features[[i]], i, drop = FALSE])
      colnames(x = data.plot) <- paste0(Key(object = object[[reduction]]), i)
      data.plot$feature <- factor(x = rownames(x = data.plot), levels = rownames(x = data.plot))
      plot <- ggplot(
        data = data.plot,
        mapping = aes(x = .data[[paste0(Key(object = object[[reduction]]), i)]], y = .data[['feature']])
      ) +
        geom_point(col = col) +
        labs(y = NULL) + theme_cowplot()
      return(plot)
    }
  )
  if (combine) {
    plots <- wrap_plots(plots, ncol = ncol)
  }
  return(plots)
}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Exported utility functions
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

#' Augments ggplot2-based plot with a PNG image.
#'
#' Creates "vector-friendly" plots. Does this by saving a copy of the plot as a PNG file,
#' then adding the PNG image with \code{\link[ggplot2]{annotation_raster}} to a blank plot
#' of the same dimensions as \code{plot}. Please note: original legends and axes will be lost
#' during augmentation.
#'
#' @param plot A ggplot object
#' @param width,height Width and height of PNG version of plot
#' @param dpi Plot resolution
#'
#' @return A ggplot object
#'
#' @importFrom png readPNG
#' @importFrom ggplot2 ggplot_build ggsave ggplot geom_blank annotation_raster ggtitle
#'
#' @export
#' @concept visualization
#'
#' @examples
#' \dontrun{
#' data("pbmc_small")
#' plot <- DimPlot(object = pbmc_small)
#' AugmentPlot(plot = plot)
#' }
#'
AugmentPlot <- function(plot, width = 10, height = 10, dpi = 100) {
  pbuild.params <- ggplot_build(plot = plot)$layout$panel_params[[1]]
  range.values <- c(
    pbuild.params$x.range,
    pbuild.params$y.range
  )
  xyparams <- GetXYAesthetics(
    plot = plot,
    geom = class(x = plot$layers[[1]]$geom)[1]
  )
  title <- plot$labels$title
  tmpfile <- tempfile(fileext = '.png')
  ggsave(
    filename = tmpfile,
    plot = plot + NoLegend() + NoAxes() + theme(plot.title = element_blank()),
    width = width,
    height = height,
    dpi = dpi
  )
  img <- readPNG(source = tmpfile)
  file.remove(tmpfile)
  blank <- ggplot(
    data = plot$data,
    mapping = aes(x = .data[[xyparams$x]], y = .data[[xyparams$y]])
  ) + geom_blank()
  blank <- blank + plot$theme + ggtitle(label = title)
  blank <- blank + annotation_raster(
    raster = img,
    xmin = range.values[1],
    xmax = range.values[2],
    ymin = range.values[3],
    ymax = range.values[4]
  )
  return(blank)
}

#' Automagically calculate a point size for ggplot2-based scatter plots
#'
#' It happens to look good
#'
#' @param data A data frame being passed to ggplot2
#' @param raster If TRUE, point size is set to 1
#'
#' @return The "optimal" point size for visualizing these data
#'
#' @export
#' @concept visualization
#'
#' @examples
#' df <- data.frame(x = rnorm(n = 10000), y = runif(n = 10000))
#' AutoPointSize(data = df)
#'
AutoPointSize <- function(data, raster = NULL) {
  return(ifelse(
    test = isTRUE(x = raster),
    yes = 1,
    no = min(1583 / nrow(x = data), 1)
  ))
}

#' Determine text color based on background color
#'
#' @param background A vector of background colors; supports R color names and
#' hexadecimal codes
#' @param threshold Intensity threshold for light/dark cutoff; intensities
#' greater than \code{theshold} yield \code{dark}, others yield \code{light}
#' @param w3c Use \href{https://www.w3.org/TR/WCAG20/}{W3C} formula for calculating
#' background text color; ignores \code{threshold}
#' @param dark Color for dark text
#' @param light Color for light text
#'
#' @return A named vector of either \code{dark} or \code{light}, depending on
#' \code{background}; names of vector are \code{background}
#'
#' @export
#' @concept visualization
#'
#' @source \url{https://stackoverflow.com/questions/3942878/how-to-decide-font-color-in-white-or-black-depending-on-background-color}
#'
#' @examples
#' BGTextColor(background = c('black', 'white', '#E76BF3'))
#'
BGTextColor <- function(
  background,
  threshold = 186,
  w3c = FALSE,
  dark = 'black',
  light = 'white'
) {
  if (w3c) {
    luminance <- Luminance(color = background)
    threshold <- 179
    return(ifelse(
      test = luminance > sqrt(x = 1.05 * 0.05) - 0.05,
      yes = dark,
      no = light
    ))
  }
  return(ifelse(
    test = Intensity(color = background) > threshold,
    yes = dark,
    no = light
  ))
}

#' @export
#' @concept visualization
#'
#' @rdname CustomPalette
#' @aliases BlackAndWhite
#'
#' @examples
#' df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
#' plot(df, col = BlackAndWhite())
#'
BlackAndWhite <- function(mid = NULL, k = 50) {
  return(CustomPalette(low = "white", high = "black", mid = mid, k = k))
}

#' @export
#' @concept visualization
#'
#' @rdname CustomPalette
#' @aliases BlueAndRed
#'
#' @examples
#' df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
#' plot(df, col = BlueAndRed())
#'
BlueAndRed <- function(k = 50) {
  return(CustomPalette(low = "#313695" , high = "#A50026", mid = "#FFFFBF", k = k))
}

#' Cell Selector
#'
#' Select points on a scatterplot and get information about them
#'
#' @param plot A ggplot2 plot
#' @param object An optional Seurat object; if passes, will return an object
#' with the identities of selected cells set to \code{ident}
#' @param ident An optional new identity class to assign the selected cells
#' @param ... Ignored
#'
#' @return If \code{object} is \code{NULL}, the names of the points selected;
#' otherwise, a Seurat object with the selected cells identity classes set to
#' \code{ident}
#'
#' @importFrom miniUI miniPage gadgetTitleBar miniTitleBarButton
#' miniContentPanel
#' @importFrom shiny fillRow plotOutput brushOpts reactiveValues observeEvent
#' stopApp brushedPoints renderPlot runGadget
#'
#' @export
#' @concept visualization
#'
#' @seealso \code{\link{DimPlot}} \code{\link{FeaturePlot}}
#'
#' @examples
#' \dontrun{
#' data("pbmc_small")
#' plot <- DimPlot(object = pbmc_small)
#' # Follow instructions in the terminal to select points
#' cells.located <- CellSelector(plot = plot)
#' cells.located
#' # Automatically set the identity class of selected cells and return a new Seurat object
#' pbmc_small <- CellSelector(plot = plot, object = pbmc_small, ident = 'SelectedCells')
#' }
#'
CellSelector <- function(plot, object = NULL, ident = 'SelectedCells', ...) {
  # Set up the gadget UI
  ui <- miniPage(
    gadgetTitleBar(
      title = "Cell Selector",
      left = miniTitleBarButton(inputId = "reset", label = "Reset")
    ),
    miniContentPanel(
      fillRow(
        plotOutput(
          outputId = "plot",
          height = '100%',
          brush = brushOpts(
            id = 'brush',
            delay = 100,
            delayType = 'debounce',
            clip = TRUE,
            resetOnNew = FALSE
          )
        )
      ),
    )
  )
  # Get some plot information
  if (inherits(x = plot, what = 'patchwork')) {
    if (length(x = plot$patches$plots)) {
      warning(
        "Multiple plots passed, using last plot",
        call. = FALSE,
        immediate. = TRUE
      )
    }
    class(x = plot) <- grep(
      pattern = 'patchwork',
      x = class(x = plot),
      value = TRUE,
      invert = TRUE
    )
  }
  xy.aes <- GetXYAesthetics(plot = plot)
  dark.theme <- !is.null(x = plot$theme$plot.background$fill) &&
    plot$theme$plot.background$fill == 'black'
  plot.data <- GGpointToBase(plot = plot, do.plot = FALSE)
  plot.data$selected_ <- FALSE
  rownames(x = plot.data) <- rownames(x = plot$data)
  colnames(x = plot.data) <- gsub(
    pattern = '-',
    replacement = '.',
    x = colnames(x = plot.data)
  )
  # Server function
  server <- function(input, output, session) {
    plot.env <- reactiveValues(data = plot.data)
    # Event handlers
    observeEvent(
      eventExpr = input$done,
      handlerExpr = {
        PlotBuild(data = plot.env$data, dark.theme = dark.theme)
        selected <- rownames(x = plot.data)[plot.env$data$selected_]
        if (inherits(x = object, what = 'Seurat')) {
          if (!all(selected %in% Cells(x = object))) {
            stop("Cannot find the selected cells in the Seurat object, please be sure you pass the same object used to generate the plot")
          }
          Idents(object = object, cells = selected) <- ident
          selected <- object
        }
        stopApp(returnValue = selected)
      }
    )
    observeEvent(
      eventExpr = input$reset,
      handlerExpr = {
        plot.env$data <- plot.data
        session$resetBrush(brushId = 'brush')
      }
    )
    observeEvent(
      eventExpr = input$brush,
      handlerExpr = {
        plot.env$data <- brushedPoints(
          df = plot.data,
          brush = input$brush,
          xvar = xy.aes$x,
          yvar = xy.aes$y,
          allRows = TRUE
        )
        plot.env$data$color <- ifelse(
          test = plot.env$data$selected_,
          yes = '#DE2D26',
          no = '#C3C3C3'
        )
      }
    )
    # Render the plot
    output$plot <- renderPlot(expr = PlotBuild(
      data = plot.env$data,
      dark.theme = dark.theme
    ))
  }
  return(runGadget(app = ui, server = server))
}

#' Move outliers towards center on dimension reduction plot
#'
#' @param object Seurat object
#' @param reduction Name of DimReduc to adjust
#' @param dims Dimensions to visualize
#' @param group.by Group (color) cells in different ways (for example, orig.ident)
#' @param outlier.sd Controls the outlier distance
#' @param reduction.key Key for DimReduc that is returned
#'
#' @return Returns a DimReduc object with the modified embeddings
#'
#' @export
#' @concept visualization
#'
#' @examples
#' \dontrun{
#' data("pbmc_small")
#' pbmc_small <- FindClusters(pbmc_small, resolution = 1.1)
#' pbmc_small <- RunUMAP(pbmc_small, dims = 1:5)
#' DimPlot(pbmc_small, reduction = "umap")
#' pbmc_small[["umap_new"]] <- CollapseEmbeddingOutliers(pbmc_small,
#'     reduction = "umap", reduction.key = 'umap_', outlier.sd = 0.5)
#' DimPlot(pbmc_small, reduction = "umap_new")
#' }
#'
CollapseEmbeddingOutliers <- function(
  object,
  reduction = 'umap',
  dims = 1:2,
  group.by = 'ident',
  outlier.sd = 2,
  reduction.key = 'UMAP_'
) {
  embeddings <- Embeddings(object = object[[reduction]])[, dims]
  idents <- FetchData(object = object, vars = group.by)
  data.medians <- sapply(X = dims, FUN = function(x) {
    tapply(X = embeddings[, x], INDEX = idents, FUN = median)
  })
  data.sd <- apply(X = data.medians, MARGIN = 2, FUN = sd)
  data.medians.scale <- as.matrix(x = scale(x = data.medians, center = TRUE, scale = TRUE))
  data.medians.scale[abs(x = data.medians.scale) < outlier.sd] <- 0
  data.medians.scale <- sign(x = data.medians.scale) * (abs(x = data.medians.scale) - outlier.sd)
  data.correct <- Sweep(
    x = data.medians.scale,
    MARGIN = 2,
    STATS = data.sd,
    FUN = "*"
  )
  data.correct <- data.correct[abs(x = apply(X = data.correct, MARGIN = 1, FUN = min)) > 0, ]
  new.embeddings <- embeddings
  for (i in rownames(x = data.correct)) {
    cells.correct <- rownames(x = idents)[idents[, "ident"] == i]
    new.embeddings[cells.correct, ] <- Sweep(
      x = new.embeddings[cells.correct,],
      MARGIN = 2,
      STATS = data.correct[i, ],
      FUN = "-"
    )
  }
  reduc <- CreateDimReducObject(
    embeddings = new.embeddings,
    loadings = Loadings(object = object[[reduction]]),
    assay = slot(object = object[[reduction]], name = "assay.used"),
    key = reduction.key
  )
  return(reduc)
}

#' Combine ggplot2-based plots into a single plot
#'
#' @param plots A list of gg objects
#' @param ncol Number of columns
#' @param legend Combine legends into a single legend
#' choose from 'right' or 'bottom'; pass 'none' to remove legends, or \code{NULL}
#' to leave legends as they are
#' @param ... Extra parameters passed to plot_grid
#'
#' @return A combined plot
#'
#' @importFrom cowplot plot_grid get_legend
#' @export
#' @concept visualization
#'
#' @examples
#' data("pbmc_small")
#' pbmc_small[['group']] <- sample(
#'   x = c('g1', 'g2'),
#'   size = ncol(x = pbmc_small),
#'   replace = TRUE
#' )
#' plot1 <- FeaturePlot(
#'   object = pbmc_small,
#'   features = 'MS4A1',
#'   split.by = 'group'
#' )
#' plot2 <- FeaturePlot(
#'   object = pbmc_small,
#'   features = 'FCN1',
#'   split.by = 'group'
#' )
#' CombinePlots(
#'   plots = list(plot1, plot2),
#'   legend = 'none',
#'   nrow = length(x = unique(x = pbmc_small[['group', drop = TRUE]]))
#' )
#'
CombinePlots <- function(plots, ncol = NULL, legend = NULL, ...) {
  .Deprecated(msg = "CombinePlots is being deprecated. Plots should now be combined using the patchwork system.")
  plots.combined <- if (length(x = plots) > 1) {
    if (!is.null(x = legend)) {
      if (legend != 'none') {
        plot.legend <- get_legend(plot = plots[[1]] + theme(legend.position = legend))
      }
      plots <- lapply(
        X = plots,
        FUN = function(x) {
          return(x + NoLegend())
        }
      )
    }
    plots.combined <- plot_grid(
      plotlist = plots,
      ncol = ncol,
      align = 'hv',
      ...
    )
    if (!is.null(x = legend)) {
      plots.combined <- switch(
        EXPR = legend,
        'bottom' = plot_grid(
          plots.combined,
          plot.legend,
          ncol = 1,
          rel_heights = c(1, 0.2)
        ),
        'right' = plot_grid(
          plots.combined,
          plot.legend,
          rel_widths = c(3, 0.3)
        ),
        plots.combined
      )
    }
    plots.combined
  } else {
    plots[[1]]
  }
  return(plots.combined)
}

#' Create a custom color palette
#'
#' Creates a custom color palette based on low, middle, and high color values
#'
#' @param low low color
#' @param high high color
#' @param mid middle color. Optional.
#' @param k number of steps (colors levels) to include between low and high values
#'
#' @return A color palette for plotting
#'
#' @importFrom grDevices col2rgb rgb
#' @export
#' @concept visualization
#'
#' @rdname CustomPalette
#' @examples
#' myPalette <- CustomPalette()
#' myPalette
#'
CustomPalette <- function(
  low = "white",
  high = "red",
  mid = NULL,
  k = 50
) {
  low <- col2rgb(col = low) / 255
  high <- col2rgb(col = high) / 255
  if (is.null(x = mid)) {
    r <- seq(from = low[1], to = high[1], len = k)
    g <- seq(from = low[2], to = high[2], len = k)
    b <- seq(from = low[3], to = high[3], len = k)
  } else {
    k2 <- round(x = k / 2)
    mid <- col2rgb(col = mid) / 255
    r <- c(
      seq(from = low[1], to = mid[1], len = k2),
      seq(from = mid[1], to = high[1], len = k2)
    )
    g <- c(
      seq(from = low[2], to = mid[2], len = k2),
      seq(from = mid[2], to = high[2],len = k2)
    )
    b <- c(
      seq(from = low[3], to = mid[3], len = k2),
      seq(from = mid[3], to = high[3], len = k2)
    )
  }
  return(rgb(red = r, green = g, blue = b))
}

#' Discrete colour palettes from pals
#'
#' These are included here because pals depends on a number of compiled
#' packages, and this can lead to increases in run time for Travis,
#' and generally should be avoided when possible.
#'
#' These palettes are a much better default for data with many classes
#' than the default ggplot2 palette.
#'
#' Many thanks to Kevin Wright for writing the pals package.
#'
#' @param n Number of colours to be generated.
#' @param palette Options are
#' "alphabet", "alphabet2", "glasbey", "polychrome", "stepped", and "parade".
#' Can be omitted and the function will use the one based on the requested n.
#' @param shuffle Shuffle the colors in the selected palette.
#'
#' @return A vector of colors
#'
#' @details
#' Taken from the pals package (Licence: GPL-3).
#' \url{https://cran.r-project.org/package=pals}
#' Credit: Kevin Wright
#'
#' @export
#' @concept visualization
#'
DiscretePalette <- function(n, palette = NULL, shuffle = FALSE) {
  palettes <- list(
    alphabet = c(
      "#F0A0FF", "#0075DC", "#993F00", "#4C005C", "#191919", "#005C31",
      "#2BCE48", "#FFCC99", "#808080", "#94FFB5", "#8F7C00", "#9DCC00",
      "#C20088", "#003380", "#FFA405", "#FFA8BB", "#426600", "#FF0010",
      "#5EF1F2", "#00998F", "#E0FF66", "#740AFF", "#990000", "#FFFF80",
      "#FFE100", "#FF5005"
    ),
    alphabet2 = c(
      "#AA0DFE", "#3283FE", "#85660D", "#782AB6", "#565656", "#1C8356",
      "#16FF32", "#F7E1A0", "#E2E2E2", "#1CBE4F", "#C4451C", "#DEA0FD",
      "#FE00FA", "#325A9B", "#FEAF16", "#F8A19F", "#90AD1C", "#F6222E",
      "#1CFFCE", "#2ED9FF", "#B10DA1", "#C075A6", "#FC1CBF", "#B00068",
      "#FBE426", "#FA0087"
    ),
    glasbey = c(
      "#0000FF", "#FF0000", "#00FF00", "#000033", "#FF00B6", "#005300",
      "#FFD300", "#009FFF", "#9A4D42", "#00FFBE", "#783FC1", "#1F9698",
      "#FFACFD", "#B1CC71", "#F1085C", "#FE8F42", "#DD00FF", "#201A01",
      "#720055", "#766C95", "#02AD24", "#C8FF00", "#886C00", "#FFB79F",
      "#858567", "#A10300", "#14F9FF", "#00479E", "#DC5E93", "#93D4FF",
      "#004CFF", "#F2F318"
    ),
    polychrome = c(
      "#5A5156", "#E4E1E3", "#F6222E", "#FE00FA", "#16FF32", "#3283FE",
      "#FEAF16", "#B00068", "#1CFFCE", "#90AD1C", "#2ED9FF", "#DEA0FD",
      "#AA0DFE", "#F8A19F", "#325A9B", "#C4451C", "#1C8356", "#85660D",
      "#B10DA1", "#FBE426", "#1CBE4F", "#FA0087", "#FC1CBF", "#F7E1A0",
      "#C075A6", "#782AB6", "#AAF400", "#BDCDFF", "#822E1C", "#B5EFB5",
      "#7ED7D1", "#1C7F93", "#D85FF7", "#683B79", "#66B0FF", "#3B00FB"
    ),
    stepped = c(
      "#990F26", "#B33E52", "#CC7A88", "#E6B8BF", "#99600F", "#B3823E",
      "#CCAA7A", "#E6D2B8", "#54990F", "#78B33E", "#A3CC7A", "#CFE6B8",
      "#0F8299", "#3E9FB3", "#7ABECC", "#B8DEE6", "#3D0F99", "#653EB3",
      "#967ACC", "#C7B8E6", "#333333", "#666666", "#999999", "#CCCCCC"
    ),
    parade = c(
      '#ff6969', '#9b37ff', '#cd3737', '#69cdff', '#ffff69', '#69cdcd',
      '#9b379b', '#3737cd', '#ffff9b', '#cdff69', '#ff9b37', '#37ffff',
      '#9b69ff', '#37cd69', '#ff3769', '#ff3737', '#37ff9b', '#cdcd37',
      '#3769cd', '#37cdff', '#9b3737', '#ff699b', '#9b9bff', '#cd9b37',
      '#69ff37', '#cd3769', '#cd69cd', '#cd6937', '#3737ff', '#cdcd69',
      '#ff9b69', '#cd37cd', '#9bff37', '#cd379b', '#cd6969', '#69ff9b',
      '#ff379b', '#9bff9b', '#6937ff', '#69cd37', '#cdff37', '#9bff69',
      '#9b37cd', '#ff37ff', '#ff37cd', '#ffff37', '#37cd9b', '#379bff',
      '#ffcd37', '#379b37', '#ff9bff', '#379b9b', '#69ffcd', '#379bcd',
      '#ff69ff', '#ff9b9b', '#37ff69', '#ff6937', '#6969ff', '#699bff',
      '#ffcd69', '#69ffff', '#37ff37', '#6937cd', '#37cd37', '#3769ff',
      '#cd69ff', '#6969cd', '#9bcd37', '#69ff69', '#37cdcd', '#cd37ff',
      '#37379b', '#37ffcd', '#69cd69', '#ff69cd', '#9bffff', '#9b9b37'
    )
  )
  if (is.null(x = n)) {
    return(names(x = palettes))
  }
  if (is.null(x = palette)) {
    if (n <= 26) {
      palette <- "alphabet"
    } else if (n <= 32) {
      palette <- "glasbey"
    } else {
      palette <- "polychrome"
    }
  }
  palette.vec <- palettes[[palette]]
  if (n > length(x = palette.vec)) {
    warning("Not enough colours in specified palette")
  }
  if (isTRUE(shuffle)) {
    palette.vec <- sample(palette.vec)
  }
  palette <- palette.vec[seq_len(length.out = n)]
  return(palette)
}

#' @rdname CellSelector
#' @export
#' @concept visualization
#'
FeatureLocator <- function(plot, ...) {
  .Defunct(
    new = 'CellSelector',
    package = 'Seurat',
    msg = "'FeatureLocator' has been replaced by 'CellSelector'"
  )
}

#' Hover Locator
#'
#' Get quick information from a scatterplot by hovering over points
#'
#' @param plot A ggplot2 plot
#' @param information An optional dataframe or matrix of extra information to be displayed on hover
#' @param dark.theme Plot using a dark theme?
#' @param axes Display or hide x- and y-axes
#' @param ... Extra parameters to be passed to \code{\link[plotly]{layout}}
#'
#' @importFrom ggplot2 ggplot_build
#' @importFrom plotly plot_ly layout add_annotations
#' @export
#' @concept visualization
#'
#' @seealso \code{\link[plotly]{layout}} \code{\link[ggplot2]{ggplot_build}}
#' \code{\link{DimPlot}} \code{\link{FeaturePlot}}
#'
#' @examples
#' \dontrun{
#' data("pbmc_small")
#' plot <- DimPlot(object = pbmc_small)
#' HoverLocator(plot = plot, information = FetchData(object = pbmc_small, vars = 'percent.mito'))
#' }
#'
HoverLocator <- function(
  plot,
  information = NULL,
  axes = TRUE,
  dark.theme = FALSE,
  ...
) {
  # Use GGpointToBase because we already have ggplot objects
  # with colors (which are annoying in plotly)
  plot.build <- suppressWarnings(expr = GGpointToPlotlyBuild(
    plot = plot,
    information = information,
    ...
  ))
  data <- ggplot_build(plot = plot)$plot$data
  # Set up axis labels here
  # Also, a bunch of stuff to get axis lines done properly
  if (axes) {
    xaxis <- list(
      title = names(x = data)[1],
      showgrid = FALSE,
      zeroline = FALSE,
      showline = TRUE
    )
    yaxis <- list(
      title = names(x = data)[2],
      showgrid = FALSE,
      zeroline = FALSE,
      showline = TRUE
    )
  } else {
    xaxis <- yaxis <- list(visible = FALSE)
  }
  # Check for dark theme
  if (dark.theme) {
    title <- list(color = 'white')
    xaxis <- c(xaxis, color = 'white')
    yaxis <- c(yaxis, color = 'white')
    plotbg <- 'black'
  } else {
    title = list(color = 'black')
    plotbg = 'white'
  }
  # The `~' means pull from the data passed (this is why we reset the names)
  # Use I() to get plotly to accept the colors from the data as is
  # Set hoverinfo to 'text' to override the default hover information
  # rather than append to it
  p <- layout(
    p = plot_ly(
      data = plot.build,
      x = ~x,
      y = ~y,
      type = 'scatter',
      mode = 'markers',
      color = ~I(color),
      hoverinfo = 'text',
      text = ~feature
    ),
    xaxis = xaxis,
    yaxis = yaxis,
    title = plot$labels$title,
    titlefont = title,
    paper_bgcolor = plotbg,
    plot_bgcolor = plotbg,
    ...
  )
  # Add labels
  label.layer <- which(x = sapply(
    X = plot$layers,
    FUN = function(x) {
      return(inherits(x = x$geom, what = c('GeomText', 'GeomTextRepel')))
    }
  ))
  if (length(x = label.layer) == 1) {
    p <- add_annotations(
      p = p,
      x = plot$layers[[label.layer]]$data[, 1],
      y = plot$layers[[label.layer]]$data[, 2],
      xref = "x",
      yref = "y",
      text = plot$layers[[label.layer]]$data[, 3],
      xanchor = 'right',
      showarrow = FALSE,
      font = list(size = plot$layers[[label.layer]]$aes_params$size * 4)
    )
  }
  return(p)
}

#' Get the intensity and/or luminance of a color
#'
#' @param color A vector of colors
#'
#' @return A vector of intensities/luminances for each color
#'
#' @name contrast-theory
#' @rdname contrast-theory
#'
#' @importFrom grDevices col2rgb
#'
#' @export
#' @concept visualization
#'
#' @source \url{https://stackoverflow.com/questions/3942878/how-to-decide-font-color-in-white-or-black-depending-on-background-color}
#'
#' @examples
#' Intensity(color = c('black', 'white', '#E76BF3'))
#'
Intensity <- function(color) {
  intensities <- apply(
    X = col2rgb(col = color),
    MARGIN = 2,
    FUN = function(col) {
      col <- rbind(as.vector(x = col), c(0.299, 0.587, 0.114))
      return(sum(apply(X = col, MARGIN = 2, FUN = prod)))
    }
  )
  names(x = intensities) <- color
  return(intensities)
}

#' Label clusters on a ggplot2-based scatter plot
#'
#' @param plot A ggplot2-based scatter plot
#' @param id Name of variable used for coloring scatter plot
#' @param clusters Vector of cluster ids to label
#' @param labels Custom labels for the clusters
#' @param split.by Split labels by some grouping label, useful when using
#' \code{\link[ggplot2]{facet_wrap}} or \code{\link[ggplot2]{facet_grid}}
#' @param repel Use \code{geom_text_repel} to create nicely-repelled labels
#' @param geom Name of geom to get X/Y aesthetic names for
#' @param box Use geom_label/geom_label_repel (includes a box around the text
#' labels)
#' @param position How to place the label if repel = FALSE. If "median", place
#' the label at the median position. If "nearest" place the label at the
#' position of the nearest data point to the median.
#' @param ... Extra parameters to \code{\link[ggrepel]{geom_text_repel}}, such as \code{size}
#'
#' @return A ggplot2-based scatter plot with cluster labels
#'
#' @importFrom stats median na.omit
#' @importFrom ggrepel geom_text_repel geom_label_repel
#' @importFrom ggplot2 geom_text geom_label layer_scales
#' @importFrom RANN nn2
#'
#' @export
#' @concept visualization
#'
#' @seealso \code{\link[ggrepel]{geom_text_repel}} \code{\link[ggplot2]{geom_text}}
#'
#' @examples
#' data("pbmc_small")
#' plot <- DimPlot(object = pbmc_small)
#' LabelClusters(plot = plot, id = 'ident')
#'
LabelClusters <- function(
  plot,
  id,
  clusters = NULL,
  labels = NULL,
  split.by = NULL,
  repel = TRUE,
  box = FALSE,
  geom = 'GeomPoint',
  position = "median",
  ...
) {
  xynames <- unlist(x = GetXYAesthetics(plot = plot, geom = geom), use.names = TRUE)
  plot_data <- plot$data
  data_nested <- FALSE
  if ((geom == "GeomPolygon") || is.null(plot_data) || (length(plot_data) == 0)) {
    # When plotting polygons, data is within the layers slot, not the data slot
    if (!is.null(plot$layers[[2]]$data)) {
      plot_data <- plot$layers[[2]]$data
    }
    data_nested <- TRUE
  }
  if (!id %in% colnames(x = plot_data)) {
    stop("Cannot find variable ", id, " in plotting data")
  }
  if (!is.null(x = split.by) && !split.by %in% colnames(x = plot_data)) {
    warning("Cannot find splitting variable ", split.by, " in plotting data")
    split.by <- NULL
  }
  data <- plot_data[, c(xynames, id, split.by)]
  id_values <- data[, id]
  possible.clusters <- as.character(x = na.omit(object = unique(x = id_values)))
  groups <- clusters %||% possible.clusters
  if (any(!groups %in% possible.clusters)) {
    stop("The following clusters were not found: ", paste(groups[!groups %in% possible.clusters], collapse = ","))
  }
  pb <- ggplot_build(plot = plot)
  if (data_nested || (geom == "GeomSpatial")) {
    colors_to_plot <- pb$plot$plot_env$cols
    data$color <- colors_to_plot[as.character(x = data[, id])]
  } else {
    # Retrieve colour from built data
    col_choice <- intersect(c("colour", "color"), names(pb$data[[1]]))
    if (length(col_choice) > 0) {
      data <- cbind(data, color = pb$data[[1]][[col_choice[1]]])
    } else {
      data <- cbind(data, color = NA_character_)
    }
  }
  labels.loc <- lapply(
    X = groups,
    FUN = function(group) {
      data.use <- data[data[, id] == group, , drop = FALSE]
      data.medians <- if (!is.null(x = split.by)) {
        do.call(
          what = 'rbind',
          args = lapply(
            X = unique(x = data.use[, split.by]),
            FUN = function(split) {
              split_by_values <- data.use[, split.by] == split
              split_data <- data.use[split_by_values == split, , drop = FALSE]
              # Extract coordinates
              coord_data <- data.use[data.use[, split.by] == split, xynames, drop = FALSE]
              medians <- apply(
                X = coord_data,
                MARGIN = 2,
                FUN = median,
                na.rm = TRUE
              )
              medians <- as.data.frame(x = t(x = medians))
              medians[, split.by] <- split
              return(medians)
            }
          )
        )
      } else {
        # Extract coordinates
        coord_data <- data.use[, xynames, drop = FALSE]
        as.data.frame(x = t(x = apply(
          X = coord_data,
          MARGIN = 2,
          FUN = median,
          na.rm = TRUE
        )))
      }
      data.medians[, id] <- group
      data.medians$color <- data.use$color[1]
      return(data.medians)
    }
  )
  if (position == "nearest") {
    labels.loc <- lapply(X = labels.loc, FUN = function(x) {
      group.data <- data[as.character(x = data[, id]) == as.character(x[3]), ]
      coord_matrix <- as.matrix(group.data[, 1:2])
      nearest.point <- nn2(data = coord_matrix, query = as.matrix(x = x[c(1,2)]), k = 1)$nn.idx
      x[1:2] <- coord_matrix[nearest.point, ]
      return(x)
    })
  }
  labels.loc <- do.call(what = 'rbind', args = labels.loc)
  data_levels <- levels(data[, id])
  labels.loc[, id] <- factor(x = labels.loc[, id], levels = data_levels)
  labels <- labels %||% groups
  if (length(x = unique(x = labels.loc[, id])) != length(x = labels)) {
    stop("Length of labels (", length(x = labels),  ") must be equal to the number of clusters being labeled (", length(x = unique(x = labels.loc[, id])), ").")
  }
  names(x = labels) <- groups
  for (group in groups) {
    labels.loc[labels.loc[, id] == group, id] <- labels[group]
  }
  if (box) {
    # Use fill for box color and default to cluster color if not provided
    dots <- list(...)
    if ("fill" %in% names(x = dots)) {
      box.color <- dots$fill
      dots$fill <- NULL # Make sure fill isn't passed to the geom twice
    } else {
      box.color <- labels.loc$color
    }
    geom.use <- ifelse(test = repel, yes = geom_label_repel, no = geom_label)
    plot <- plot + exec(
      .fn = geom.use,
      data = labels.loc,
      mapping = aes(x = .data[[xynames['x']]], y = .data[[xynames['y']]], label = .data[[id]]),
      fill = box.color,
      show.legend = FALSE,
      inherit.aes = FALSE,
      !!!dots
    )
  } else {
    geom.use <- ifelse(test = repel, yes = geom_text_repel, no = geom_text)
    plot <- plot + geom.use(
      data = labels.loc,
      mapping = aes(x = .data[[xynames['x']]], y = .data[[xynames['y']]], label = .data[[id]]),
      show.legend = FALSE,
      inherit.aes = FALSE,
      ...
    )
  }
  return(plot)
}

#' Add text labels to a ggplot2 plot
#'
#' @param plot A ggplot2 plot with a GeomPoint layer
#' @param points A vector of points to label; if \code{NULL}, will use all points in the plot
#' @param labels A vector of labels for the points; if \code{NULL}, will use
#' rownames of the data provided to the plot at the points selected
#' @param repel Use \code{geom_text_repel} to create a nicely-repelled labels; this
#' is slow when a lot of points are being plotted. If using \code{repel}, set \code{xnudge}
#' and \code{ynudge} to 0
#' @param xnudge,ynudge Amount to nudge X and Y coordinates of labels by
#' @param ... Extra parameters passed to \code{geom_text}
#'
#' @return A ggplot object
#'
#' @importFrom ggrepel geom_text_repel
#' @importFrom ggplot2 geom_text
#' @export
#' @concept visualization
#'
#' @aliases Labeler
#' @seealso \code{\link[ggplot2]{geom_text}}
#'
#' @examples
#' data("pbmc_small")
#' ff <- TopFeatures(object = pbmc_small[['pca']])
#' cc <- TopCells(object = pbmc_small[['pca']])
#' plot <- FeatureScatter(object = pbmc_small, feature1 = ff[1], feature2 = ff[2])
#' LabelPoints(plot = plot, points = cc)
#'
LabelPoints <- function(
  plot,
  points,
  labels = NULL,
  repel = FALSE,
  xnudge = 0.3,
  ynudge = 0.05,
  ...
) {
  xynames <- GetXYAesthetics(plot = plot)
  points <- points %||% rownames(x = plot$data)
  if (is.numeric(x = points)) {
    points <- rownames(x = plot$data)
  }
  points <- intersect(x = points, y = rownames(x = plot$data))
  if (length(x = points) == 0) {
    stop("Cannot find points provided")
  }
  labels <- labels %||% points
  labels <- as.character(x = labels)
  label.data <- plot$data[points, ]
  label.data$labels <- labels
  geom.use <- ifelse(test = repel, yes = geom_text_repel, no = geom_text)
  if (repel) {
    if (!all(c(xnudge, ynudge) == 0)) {
      message("When using repel, set xnudge and ynudge to 0 for optimal results")
    }
  }
  plot <- plot + geom.use(
    mapping = aes(x = .data[[xynames$x]], y = .data[[xynames$y]], label = .data[['labels']]),
    data = label.data,
    nudge_x = xnudge,
    nudge_y = ynudge,
    ...
  )
  return(plot)
}

#' @name contrast-theory
#' @rdname contrast-theory
#'
#' @importFrom grDevices col2rgb
#'
#' @export
#' @concept visualization
#'
#' @examples
#' Luminance(color = c('black', 'white', '#E76BF3'))
#'
Luminance <- function(color) {
  luminance <- apply(
    X = col2rgb(col = color),
    MARGIN = 2,
    function(col) {
      col <- as.vector(x = col) / 255
      col <- sapply(
        X = col,
        FUN = function(x) {
          return(ifelse(
            test = x <= 0.03928,
            yes = x / 12.92,
            no = ((x + 0.055) / 1.055) ^ 2.4
          ))
        }
      )
      col <- rbind(col, c(0.2126, 0.7152, 0.0722))
      return(sum(apply(X = col, MARGIN = 2, FUN = prod)))
    }
  )
  names(x = luminance) <- color
  return(luminance)
}

#' @export
#' @concept visualization
#'
#' @rdname CustomPalette
#' @aliases PurpleAndYellow
#'
#' @examples
#' df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
#' plot(df, col = PurpleAndYellow())
#'
PurpleAndYellow <- function(k = 50) {
  return(CustomPalette(low = "magenta", high = "yellow", mid = "black", k = k))
}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Seurat themes
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

#' Seurat Themes
#'
#' Various themes to be applied to ggplot2-based plots
#' \describe{
#'   \item{\code{SeuratTheme}}{The curated Seurat theme, consists of ...}
#'   \item{\code{DarkTheme}}{A dark theme, axes and text turn to white, the background becomes black}
#'   \item{\code{NoAxes}}{Removes axis lines, text, and ticks}
#'   \item{\code{NoLegend}}{Removes the legend}
#'   \item{\code{FontSize}}{Sets axis and title font sizes}
#'   \item{\code{NoGrid}}{Removes grid lines}
#'   \item{\code{SeuratAxes}}{Set Seurat-style axes}
#'   \item{\code{SpatialTheme}}{A theme designed for spatial visualizations (eg \code{\link{PolyFeaturePlot}}, \code{\link{PolyDimPlot}})}
#'   \item{\code{RestoreLegend}}{Restore a legend after removal}
#'   \item{\code{RotatedAxis}}{Rotate X axis text 45 degrees}
#'   \item{\code{BoldTitle}}{Enlarges and emphasizes the title}
#' }
#'
#' @param ... Extra parameters to be passed to \code{theme}
#'
#' @return A ggplot2 theme object
#'
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @seealso \code{\link[ggplot2]{theme}}
#' @aliases SeuratTheme
#'
SeuratTheme <- function() {
  return(DarkTheme() + NoLegend() + NoGrid() + SeuratAxes())
}

#' @importFrom ggplot2 theme element_text
#'
#' @rdname SeuratTheme
#' @export
#' @concept visualization
#'
#' @aliases CenterTitle
#'
CenterTitle <- function(...) {
  return(theme(plot.title = element_text(hjust = 0.5), validate = TRUE, ...))
}

#' @importFrom ggplot2 theme element_rect element_text element_line margin
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases DarkTheme
#'
#' @examples
#' # Generate a plot with a dark theme
#' library(ggplot2)
#' df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
#' p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red'))
#' p + DarkTheme(legend.position = 'none')
#'
DarkTheme <- function(...) {
  #   Some constants for easier changing in the future
  black.background <- element_rect(fill = 'black')
  black.background.no.border <- element_rect(fill = 'black', linewidth = 0)
  font.margin <- 4
  white.text <- element_text(
    colour = 'white',
    margin = margin(
      t = font.margin,
      r = font.margin,
      b = font.margin,
      l = font.margin
    )
  )
  white.line <- element_line(colour = 'white', linewidth = 1)
  no.line <- element_line(linewidth = 0)
  #   Create the dark theme
  dark.theme <- theme(
    #   Set background colors
    plot.background = black.background,
    panel.background = black.background,
    legend.background = black.background,
    legend.box.background = black.background.no.border,
    legend.key = black.background.no.border,
    strip.background = element_rect(fill = 'grey50', colour = NA),
    #   Set text colors
    plot.title = white.text,
    plot.subtitle = white.text,
    axis.title = white.text,
    axis.text = white.text,
    legend.title = white.text,
    legend.text = white.text,
    strip.text = white.text,
    #   Set line colors
    axis.line.x = white.line,
    axis.line.y = white.line,
    panel.grid = no.line,
    panel.grid.minor = no.line,
    #   Validate the theme
    validate = TRUE,
    #   Extra parameters
    ...
  )
  return(dark.theme)
}

#' @param x.text,y.text X and Y axis text sizes
#' @param x.title,y.title X and Y axis title sizes
#' @param main Plot title size
#'
#' @importFrom ggplot2 theme element_text
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases FontSize
#'
FontSize <- function(
  x.text = NULL,
  y.text = NULL,
  x.title = NULL,
  y.title = NULL,
  main = NULL,
  ...
) {
  font.size <- theme(
    # Set font sizes
    axis.text.x = element_text(size = x.text),
    axis.text.y = element_text(size = y.text),
    axis.title.x = element_text(size = x.title),
    axis.title.y = element_text(size = y.title),
    plot.title = element_text(size = main),
    # Validate the theme
    validate = TRUE,
    # Extra parameters
    ...
  )
}

#' @param keep.text Keep axis text
#' @param keep.ticks Keep axis ticks
#'
#' @importFrom ggplot2 theme element_blank
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases NoAxes
#'
#' @examples
#' # Generate a plot with no axes
#' library(ggplot2)
#' df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
#' p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red'))
#' p + NoAxes()
#'
NoAxes <- function(..., keep.text = FALSE, keep.ticks = FALSE) {
  blank <- element_blank()
  no.axes.theme <- theme(
    # Remove the axis elements
    axis.line.x = blank,
    axis.line.y = blank,
    # Validate the theme
    validate = TRUE,
    ...
  )
  if (!keep.text) {
    no.axes.theme <- no.axes.theme + theme(
      axis.text.x = blank,
      axis.text.y = blank,
      axis.title.x = blank,
      axis.title.y = blank,
      validate = TRUE,
      ...
    )
  }
  if (!keep.ticks){
    no.axes.theme <- no.axes.theme + theme(
      axis.ticks.x = blank,
      axis.ticks.y = blank,
      validate = TRUE,
      ...
    )
  }
  return(no.axes.theme)
}

#' @importFrom ggplot2 theme
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases NoLegend
#'
#' @examples
#' # Generate a plot with no legend
#' library(ggplot2)
#' df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
#' p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red'))
#' p + NoLegend()
#'
NoLegend <- function(...) {
  no.legend.theme <- theme(
    # Remove the legend
    legend.position = 'none',
    # Validate the theme
    validate = TRUE,
    ...
  )
  return(no.legend.theme)
}

#' @importFrom ggplot2 theme element_blank
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases NoGrid
#'
#' @examples
#' # Generate a plot with no grid lines
#' library(ggplot2)
#' df <- data.frame(x = rnorm(n = 100, mean = 20, sd = 2), y = rbinom(n = 100, size = 100, prob = 0.2))
#' p <- ggplot(data = df, mapping = aes(x = x, y = y)) + geom_point(mapping = aes(color = 'red'))
#' p + NoGrid()
#'
NoGrid <- function(...) {
  no.grid.theme <- theme(
    # Set grid lines to blank
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    # Validate the theme
    validate = TRUE,
    ...
  )
  return(no.grid.theme)
}

#' @importFrom ggplot2 theme element_text
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases SeuratAxes
#'
SeuratAxes <- function(...) {
  axes.theme <- theme(
    # Set axis things
    axis.title = element_text(face = 'bold', color = '#990000', size = 16),
    axis.text = element_text(vjust = 0.5, size = 12),
    # Validate the theme
    validate = TRUE,
    ...
  )
  return(axes.theme)
}

#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases SpatialTheme
#'
SpatialTheme <- function(...) {
  return(DarkTheme() + NoAxes() + NoGrid() + NoLegend(...))
}

#' @param position A position to restore the legend to
#'
#' @importFrom ggplot2 theme
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases RestoreLegend
#'
RestoreLegend <- function(..., position = 'right') {
  restored.theme <- theme(
    # Restore legend position
    legend.position = 'right',
    # Validate the theme
    validate = TRUE,
    ...
  )
  return(restored.theme)
}

#' @importFrom ggplot2 theme element_text
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases RotatedAxis
#'
RotatedAxis <- function(...) {
  rotated.theme <- theme(
    # Rotate X axis text
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Validate the theme
    validate = TRUE,
    ...
  )
  return(rotated.theme)
}

#' @importFrom ggplot2 theme element_text
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases BoldTitle
#'
BoldTitle <- function(...) {
  bold.theme <- theme(
    # Make the title bold
    plot.title = element_text(size = 20, face = 'bold'),
    # Validate the theme
    validate = TRUE,
    ...
  )
  return(bold.theme)
}

#' @importFrom ggplot2 theme element_rect
#' @export
#' @concept visualization
#'
#' @rdname SeuratTheme
#' @aliases WhiteBackground
#'
WhiteBackground <- function(...) {
  white.rect = element_rect(fill = 'white')
  white.theme <- theme(
    # Make the plot, panel, and legend key backgrounds white
    plot.background = white.rect,
    panel.background = white.rect,
    legend.key = white.rect,
    # Validate the theme
    validate = TRUE,
    ...
  )
  return(white.theme)
}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Fortify Methods
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

#' Prepare Coordinates for Spatial Plots
#'
#' @inheritParams SeuratObject::GetTissueCoordinates
#' @param model A \code{\link[SeuratObject:Segmentation-class]{Segmentation}},
#' \code{\link[SeuratObject:Centroids-class]{Centroids}},
#' or \code{\link[SeuratObject:Molecules-class]{Molecules}} object
#' @param data Extra data to be used for annotating the cell segmentations; the
#' easiest way to pass data is a one-column
#' \code{\link[base:data.frame]{data frame}} with the values to color by and
#' the cell names are rownames
#' @param ... Arguments passed to other methods
#'
#' @name fortify-Spatial
#' @rdname fortify-Spatial
#'
#' @importFrom SeuratObject GetTissueCoordinates
#'
#' @keywords internal
#'
#' @method fortify Centroids
#' @export
#'
#' @aliases fortify
#'
fortify.Centroids <- function(model, data, ...) {
  df <- GetTissueCoordinates(object = model, full = FALSE)
  if (missing(x = data)) {
    data <- NULL
  }
  data <- .PrepImageData(data = data, cells = lengths(x = model), ...)
  df <- cbind(df, data)
  return(df)
}

#' @rdname fortify-Spatial
#' @method fortify Molecules
#'
#' @importFrom SeuratObject FetchData
#'
#' @export
#'
fortify.Molecules <- function(
  model,
  data,
  nmols = NULL,
  seed = NA_integer_,
  ...
) {
  return(FetchData(object = model, vars = data, nmols = nmols, seed = seed, ...))
}

#' @rdname fortify-Spatial
#' @method fortify Segmentation
#' @export
#'
fortify.Segmentation <- function(model, data, ...) {
  df <- GetTissueCoordinates(object = model, full = TRUE)
  if (missing(x = data)) {
    data <- NULL
  }
  data <- .PrepImageData(data = data, cells = lengths(x = model), ...)
  df <- cbind(df, data)
  return(df)
}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Internal
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

#' @importFrom SeuratObject Features Key Keys Molecules
#'
.MolsByFOV <- function(object, fov, molecules) {
  keys <- Key(object = object)[fov]
  keyed.mols <- sapply(
    X = names(x = keys),
    FUN = function(img) {
      if (is.null(x = Molecules(object = object[[img]]))) {
        return(NULL)
      }
      key <- keys[img]
      mols <- grep(pattern = paste0('^', key), x = molecules, value = TRUE)
      names(x = mols) <- mols
      mols <- gsub(pattern = paste0('^', key), replacement = '', x = mols)
      keyed <- sapply(
        X = SeuratObject::Keys(object = object[[img]]),
        FUN = function(x) {
          return(grep(pattern = paste0('^', x), x = mols, value = TRUE))
        }
      )
      keyed <- unlist(x = keyed)
      names(x = keyed) <- gsub(
        pattern = '^.*\\.',
        replacement = '',
        x = names(x = keyed)
      )
      missing <- mols[!mols %in% keyed]
      missing <- missing[missing %in% Features(x = object[[img]])]
      if (length(x = missing)) {
        # TODO: replace with default molecules
        default <- Molecules(object = object[[img]])[1L]
        mn <- names(x = missing)
        missing <- paste0(
          SeuratObject::Key(object = object[[img]][[default]]),
          missing
        )
        names(x = missing) <- mn
      }
      return(c(missing, keyed))
    },
    simplify = FALSE,
    USE.NAMES = TRUE
  )
  found <- names(x = unlist(x = keyed.mols))
  found <- gsub(pattern = '^.*\\.', replacement = '', x = found)
  missing <- setdiff(x = molecules, y = found)
  names(x = missing) <- missing
  for (img in fov) {
    imissing <- missing
    for (i in seq_along(along.with = imissing)) {
      for (lkey in Keys(object = object[[img]])) {
        imissing[[i]] <- gsub(
          pattern = paste0('^', lkey),
          replacement = '',
          x = imissing[[i]]
        )
      }
    }
    imissing <- names(x = imissing[imissing %in% Features(x = object[[img]])])
    keyed.mols[[img]] <- c(keyed.mols[[img]], imissing)
  }
  keyed.mols <- Filter(f = length, x = keyed.mols)
  keyed.mols <- sapply(X = keyed.mols, FUN = unname, simplify = FALSE)
  return(keyed.mols)
}

# Calculate bandwidth for use in ggplot2-based smooth scatter plots
#
# Inspired by MASS::bandwidth.nrd and graphics:::.smoothScatterCalcDensity
#
# @param data A two-column data frame with X and Y coordinates for a plot
#
# @return The calculated bandwidth
#
#' @importFrom stats quantile var
#
Bandwidth <- function(data) {
  r <- diff(x = apply(
    X = data,
    MARGIN = 2,
    FUN = quantile,
    probs = c(0.05, 0.95),
    na.rm = TRUE,
    names = FALSE
  ))
  h <- abs(x = r[2L] - r[1L]) / 1.34
  h <- ifelse(test = h == 0, yes = 1, no = h)
  bandwidth <- 4 * 1.06 *
    min(sqrt(x = apply(X = data, MARGIN = 2, FUN = var)), h) *
    nrow(x = data) ^ (-0.2)
  return(bandwidth)
}

# Blend expression values together
#
# @param data A two-column data frame with expression values for two features
#
# @return A three-column data frame with transformed and blended expression values
#
BlendExpression <- function(data) {
  if (ncol(x = data) != 2) {
    stop("'BlendExpression' only blends two features")
  }
  features <- colnames(x = data)
  data <- as.data.frame(x = apply(
    X = data,
    MARGIN = 2,
    FUN = function(x) {
      return(round(x = 9 * (x - min(x)) / (max(x) - min(x))))
    }
  ))
  data[, 3] <- data[, 1] + data[, 2] * 10
  colnames(x = data) <- c(features, paste(features, collapse = '_'))
  for (i in 1:ncol(x = data)) {
    data[, i] <- factor(x = data[, i])
  }
  return(data)
}

# Create a heatmap of blended colors
#
# @param color.matrix A color matrix of blended colors
#
# @return A ggplot object
#
#' @importFrom grid unit
#' @importFrom cowplot theme_cowplot
#' @importFrom ggplot2 ggplot scale_fill_manual geom_raster
#' theme scale_y_continuous scale_x_continuous scale_fill_manual
#
# @seealso \code{\link{BlendMatrix}}
#
BlendMap <- function(color.matrix) {
  color.heat <- matrix(
    data = 1:prod(dim(x = color.matrix)) - 1,
    nrow = nrow(x = color.matrix),
    ncol = ncol(x = color.matrix),
    dimnames = list(
      1:nrow(x = color.matrix),
      1:ncol(x = color.matrix)
    )
  )
  xbreaks <- seq.int(from = 0, to = nrow(x = color.matrix), by = 2)
  ybreaks <- seq.int(from = 0, to = ncol(x = color.matrix), by = 2)
  color.heat <- Melt(x = color.heat)
  color.heat$rows <- as.numeric(x = as.character(x = color.heat$rows))
  color.heat$cols <- as.numeric(x = as.character(x = color.heat$cols))
  color.heat$vals <- factor(x = color.heat$vals)
  plot <- ggplot(
    data = color.heat,
    mapping = aes(x = .data[['rows']], y = .data[['cols']], fill = .data[['vals']])
  ) +
    geom_raster(show.legend = FALSE) +
    theme(plot.margin = unit(x = rep.int(x = 0, times = 4), units = 'cm')) +
    scale_x_continuous(breaks = xbreaks, expand = c(0, 0), labels = xbreaks) +
    scale_y_continuous(breaks = ybreaks, expand = c(0, 0), labels = ybreaks) +
    scale_fill_manual(values = as.vector(x = color.matrix)) +
    theme_cowplot()
  return(plot)
}

# Create a color matrix of blended colors
#
# @param n Dimensions of blended matrix (n x n)
# @param col.threshold The color cutoff from weak signal to strong signal; ranges from 0 to 1.
# @param two.colors Two colors used for the blend expression.
#
# @return An n x n matrix of blended colors
#
#' @importFrom grDevices col2rgb
#
BlendMatrix <- function(
  n = 10,
  col.threshold = 0.5,
  two.colors = c("#ff0000", "#00ff00"),
  negative.color = "black"
) {
  if (0 > col.threshold || col.threshold > 1) {
    stop("col.threshold must be between 0 and 1")
  }
  C0 <- as.vector(col2rgb(negative.color, alpha = TRUE))
  C1 <- as.vector(col2rgb(two.colors[1], alpha = TRUE))
  C2 <- as.vector(col2rgb(two.colors[2], alpha = TRUE))
  blend_alpha <- (C1[4] + C2[4])/2
  C0 <- C0[-4]
  C1 <- C1[-4]
  C2 <- C2[-4]
  merge.weight <- min(255 / (C1 + C2 +  C0 + 0.01))
  sigmoid <- function(x) {
    return(1 / (1 + exp(-x)))
  }
  blend_color <- function(
    i,
    j,
    col.threshold,
    n,
    C0,
    C1,
    C2,
    alpha,
    merge.weight
  ) {
    c.min <- sigmoid(5 * (1 / n - col.threshold))
    c.max <- sigmoid(5 * (1 - col.threshold))
    c1_weight <- sigmoid(5 * (i / n - col.threshold))
    c2_weight <- sigmoid(5 * (j / n - col.threshold))
    c0_weight <-  sigmoid(5 * ((i + j) / (2 * n) - col.threshold))
    c1_weight <- (c1_weight - c.min) / (c.max - c.min)
    c2_weight <- (c2_weight - c.min) / (c.max - c.min)
    c0_weight <- (c0_weight - c.min) / (c.max - c.min)
    C1_length <- sqrt(sum((C1 - C0) ** 2))
    C2_length <- sqrt(sum((C2 - C0) ** 2))
    C1_unit <- (C1 - C0) / C1_length
    C2_unit <- (C2 - C0) / C2_length
    C1_weight <- C1_unit * c1_weight
    C2_weight <- C2_unit * c2_weight
    C_blend <- C1_weight * (i - 1) * C1_length / (n - 1) + C2_weight * (j - 1) * C2_length / (n - 1) + (i - 1) * (j - 1) * c0_weight * C0 / (n - 1) ** 2 + C0
    C_blend[C_blend > 255] <- 255
    C_blend[C_blend < 0] <- 0
    return(rgb(
      red = C_blend[1],
      green = C_blend[2],
      blue = C_blend[3],
      alpha = alpha,
      maxColorValue = 255
    ))
  }
  blend_matrix <- matrix(nrow = n, ncol = n)
  for (i in 1:n) {
    for (j in 1:n) {
      blend_matrix[i, j] <- blend_color(
        i = i,
        j = j,
        col.threshold = col.threshold,
        n = n,
        C0 = C0,
        C1 = C1,
        C2 = C2,
        alpha = blend_alpha,
        merge.weight = merge.weight
      )
    }
  }
  return(blend_matrix)
}

# Convert R colors to hexadecimal
#
# @param ... R colors
#
# @return The hexadecimal representations of input colors
#
#' @importFrom grDevices rgb col2rgb
#
Col2Hex <- function(...) {
  colors <- as.character(x = c(...))
  alpha <- rep.int(x = 255, times = length(x = colors))
  if (sum(sapply(X = colors, FUN = grepl, pattern = '^#')) != 0) {
    hex <- colors[which(x = grepl(pattern = '^#', x = colors))]
    hex.length <- sapply(X = hex, FUN = nchar)
    if (9 %in% hex.length) {
      hex.alpha <- hex[which(x = hex.length == 9)]
      hex.vals <- sapply(X = hex.alpha, FUN = substr, start = 8, stop = 9)
      dec.vals <- sapply(X = hex.vals, FUN = strtoi, base = 16)
      alpha[match(x = hex[which(x = hex.length == 9)], table = colors)] <- dec.vals
    }
  }
  colors <- t(x = col2rgb(col = colors))
  colors <- mapply(
    FUN = function(i, alpha) {
      return(rgb(colors[i, , drop = FALSE], alpha = alpha, maxColorValue = 255))
    },
    i = 1:nrow(x = colors),
    alpha = alpha
  )
  return(colors)
}

# Plot feature expression by identity
#
# Basically combines the codebase for VlnPlot and RidgePlot
#
# @param object Seurat object
# @param type Plot type, choose from 'ridge', 'violin', or 'splitViolin'
# @param features Features to plot (gene expression, metrics, PC scores,
# anything that can be retreived by FetchData)
# @param idents Which classes to include in the plot (default is all)
# @param ncol Number of columns if multiple plots are displayed
# @param sort Sort identity classes (on the x-axis) by the average expression of the attribute being potted,
# or, if stack is True, sort both identity classes and features by hierarchical clustering
# @param y.max Maximum y axis value
# @param same.y.lims Set all the y-axis limits to the same values
# @param adjust Adjust parameter for geom_violin
# @param pt.size Point size for points
# @param alpha Alpha value for points
# @param cols Colors to use for plotting
# @param group.by Group (color) cells in different ways (for example, orig.ident)
# @param split.by A variable to split the plot by
# @param log plot Y axis on log scale
# @param slot Slot to pull expression data from (e.g. "counts" or "data")
# @param stack Horizontally stack plots for multiple feature
# @param combine Combine plots into a single \code{\link[patchwork]{patchwork}ed}
# ggplot object. If \code{FALSE}, return a list of ggplot objects
# @param fill.by Color violins/ridges based on either 'feature' or 'ident'
# @param flip flip plot orientation (identities on x-axis)
# @param add.noise determine if adding a small noise for plotting
# @param raster Convert points to raster format, default is \code{NULL} which
# automatically rasterizes if plotting more than 100,000 cells
#
# @return A \code{\link[patchwork]{patchwork}ed} ggplot object if
# \code{combine = TRUE}; otherwise, a list of ggplot objects
#
#' @importFrom scales hue_pal
#' @importFrom ggplot2 xlab ylab
#' @importFrom patchwork wrap_plots
#
ExIPlot <- function(
  object,
  features,
  type = 'violin',
  idents = NULL,
  ncol = NULL,
  sort = FALSE,
  assay = NULL,
  y.max = NULL,
  same.y.lims = FALSE,
  adjust = 1,
  cols = NULL,
  pt.size = 0,
  alpha = 1,
  group.by = NULL,
  split.by = NULL,
  log = FALSE,
  slot = deprecated(),
  layer = 'data',
  stack = FALSE,
  combine = TRUE,
  fill.by = NULL,
  flip = FALSE,
  add.noise = TRUE,
  raster = NULL,
  raster.dpi = NULL
) {
  if (is_present(arg = slot)) {
    layer <- layer %||% slot
  }
  assay <- assay %||% DefaultAssay(object = object)
  DefaultAssay(object = object) <- assay
  cells <- Cells(x = object, assay = NULL)
  if (isTRUE(x = stack)) {
    if (!is.null(x = ncol)) {
      warning(
        "'ncol' is ignored with 'stack' is TRUE",
        call. = FALSE,
        immediate. = TRUE
      )
    }
    if (!is.null(x = y.max)) {
      warning(
        "'y.max' is ignored when 'stack' is TRUE",
        call. = FALSE,
        immediate. = TRUE
      )
    }
  } else {
    ncol <- ncol %||% ifelse(
      test = length(x = features) > 9,
      yes = 4,
      no = min(length(x = features), 3)
    )
  }
  if (!is.null(x = idents)) {
    cells <- intersect(
      x = names(x = Idents(object = object)[Idents(object = object) %in% idents]),
      y = cells
    )
  }
  data <- FetchData(object = object, vars = features, layer = layer, cells = cells)
  pt.size <- pt.size %||% AutoPointSize(data = object)
  features <- colnames(x = data)
  data <- data[cells, , drop = FALSE]
  idents <- if (is.null(x = group.by)) {
    Idents(object = object)[cells]
  } else {
    object[[group.by, drop = TRUE]][cells]
  }
  if (!is.factor(x = idents)) {
    idents <- factor(x = idents)
  }
  if (is.null(x = split.by)) {
    split <- NULL
  } else {
    split <- FetchData(object,split.by)[cells,split.by]
    if (!is.factor(x = split)) {
      split <- factor(x = split)
    }
    if (is.null(x = cols)) {
      cols <- hue_pal()(length(x = levels(x = idents)))
      cols <- Interleave(cols, InvertHex(hexadecimal = cols))
    } else if (length(x = cols) == 1 && cols == 'interaction') {
      split <- interaction(idents, split)
      cols <- hue_pal()(length(x = levels(x = idents)))
    } else {
      cols <- Col2Hex(cols)
    }
    if (length(x = cols) < length(x = levels(x = split))) {
      cols <- Interleave(cols, InvertHex(hexadecimal = cols))
    }
    cols <- rep_len(x = cols, length.out = length(x = levels(x = split)))
    names(x = cols) <- levels(x = split)
    if ((length(x = cols) > 2) & (type == "splitViolin")) {
      warning("Split violin is only supported for <3 groups, using multi-violin.")
      type <- "violin"
    }
  }
  if (same.y.lims && is.null(x = y.max)) {
    y.max <- max(data)
  }
  if (isTRUE(x = stack)) {
    return(MultiExIPlot(
      type = type,
      data = data,
      idents = idents,
      split = split,
      sort = sort,
      same.y.lims = same.y.lims,
      adjust = adjust,
      cols = cols,
      pt.size = pt.size,
      log = log,
      fill.by = fill.by,
      add.noise = add.noise,
      flip = flip
    ))
  }
  plots <- lapply(
    X = features,
    FUN = function(x) {
      return(SingleExIPlot(
        type = type,
        data = data[, x, drop = FALSE],
        idents = idents,
        split = split,
        sort = sort,
        y.max = y.max,
        adjust = adjust,
        cols = cols,
        pt.size = pt.size,
        alpha = alpha,
        log = log,
        add.noise = add.noise,
        raster = raster,
        raster.dpi = raster.dpi
      ))
    }
  )
  label.fxn <- switch(
    EXPR = type,
    'violin' = if (stack) {
      xlab
    } else {
      ylab
    },
    "splitViolin" = if (stack) {
      xlab
    } else {
      ylab
    },
    'ridge' = xlab,
    stop("Unknown ExIPlot type ", type, call. = FALSE)
  )
  for (i in 1:length(x = plots)) {
    key <- paste0(unlist(x = strsplit(x = features[i], split = '_'))[1], '_')
    obj <- names(x = which(x = Key(object = object) == key))
    if (length(x = obj) == 1) {
      if (inherits(x = object[[obj]], what = 'DimReduc')) {
        plots[[i]] <- plots[[i]] + label.fxn(label = 'Embeddings Value')
      } else if (inherits(x = object[[obj]], what = 'Assay') ||
                 inherits(x = object[[obj]], what = 'Assay5')) {
        next
      } else {
        warning("Unknown object type ", class(x = object), immediate. = TRUE, call. = FALSE)
        plots[[i]] <- plots[[i]] + label.fxn(label = NULL)
      }
    } else if (!features[i] %in% rownames(x = object)) {
      plots[[i]] <- plots[[i]] + label.fxn(label = NULL)
    }
  }
  if (combine) {
    plots <- wrap_plots(plots, ncol = ncol)
    if (length(x = features) > 1) {
      plots <- plots & NoLegend()
    }
  }
  return(plots)
}

# Make a theme for facet plots
#
# @inheritParams SeuratTheme
# @export
#
# @rdname SeuratTheme
# @aliases FacetTheme
#
FacetTheme <- function(...) {
  return(theme(
    strip.background = element_blank(),
    strip.text = element_text(face = 'bold'),
    # Validate the theme
    validate = TRUE,
    ...
  ))
}

#' @importFrom RColorBrewer brewer.pal
#' @importFrom grDevices colorRampPalette
#'
#'
SpatialColors <- colorRampPalette(colors = rev(x = brewer.pal(n = 11, name = "Spectral")))

# Feature plot palettes
#
FeaturePalettes <- list(
  'Spatial' = SpatialColors(n = 100),
  'Seurat' = c('lightgrey', 'blue')
)

# Splits features into groups based on log expression levels
#
# @param object Seurat object
# @param assay Assay for expression data
# @param min.cells Only compute for features in at least this many cells
# @param ngroups Number of groups to split into
#
# @return A Seurat object with the feature group stored as a factor in
# metafeatures
#
#' @importFrom Matrix rowMeans rowSums
#
GetFeatureGroups <- function(object, assay, min.cells = 5, ngroups = 6) {
  cm <- GetAssayData(object = object[[assay]], layer = "counts")
  # subset to keep only genes detected in at least min.cells cells
  cm <- cm[rowSums(cm > 0) >= min.cells, ]
  # use the geometric mean of the features to group them
  # (using the arithmetic mean would usually not change things much)
  # could use sctransform:::row_gmean here but not exported
  feature.gmean <- exp(x = rowMeans(log1p(x = cm))) - 1
  feature.grp.breaks <- seq(
    from = min(log10(x = feature.gmean)) - 10*.Machine$double.eps,
    to = max(log10(x = feature.gmean)),
    length.out = ngroups + 1
  )
  feature.grp <- cut(
    x = log10(x = feature.gmean),
    breaks = feature.grp.breaks,
    ordered_result = TRUE
  )
  feature.grp <- factor(
    x = feature.grp,
    levels = rev(x = levels(x = feature.grp)),
    ordered = TRUE
  )
  names(x = feature.grp) <- names(x = feature.gmean)
  return(feature.grp)
}

# Get X and Y aesthetics from a plot for a certain geom
#
# @param plot A ggplot2 object
# @param geom Geom class to filter to
# @param plot.first Use plot-wide X/Y aesthetics before geom-specific aesthetics
#
# @return A named list with values 'x' for the name of the x aesthetic and 'y' for the y aesthetic
#
#' @importFrom rlang as_label
#
GetXYAesthetics <- function(plot, geom = 'GeomPoint', plot.first = TRUE) {
  geoms <- sapply(
    X = plot$layers,
    FUN = function(layer) {
      return(class(x = layer$geom)[1])
    }
  )
  # handle case where raster is set to True
  if (geom == "GeomPoint" && "GeomScattermore" %in% geoms){
    geom <- "GeomScattermore"
  }
  geoms <- which(x = geoms == geom)
  if (length(x = geoms) == 0) {
    stop("Cannot find a geom of class ", geom)
  }
  geoms <- min(geoms)
  if (plot.first) {
    # x <- as.character(x = plot$mapping$x %||% plot$layers[[geoms]]$mapping$x)[2]
    x <- as_label(x = plot$mapping$x %||% plot$layers[[geoms]]$mapping$x)
    # y <- as.character(x = plot$mapping$y %||% plot$layers[[geoms]]$mapping$y)[2]
    y <- as_label(x = plot$mapping$y %||% plot$layers[[geoms]]$mapping$y)
  } else {
    x <- as_label(x = plot$layers[[geoms]]$mapping$x %||% plot$mapping$x)
    y <- as_label(x = plot$layers[[geoms]]$mapping$y %||% plot$mapping$y)
  }
  return(list('x' = x, 'y' = y))
}

# For plotting the tissue image
#' @importFrom ggplot2 ggproto Geom aes ggproto_parent alpha draw_key_point
#' @importFrom grid unit gpar editGrob pointsGrob viewport gTree addGrob grobName
#'
GeomSpatial <- ggproto(
  "GeomSpatial",
  Geom,
  required_aes = c("x", "y"),
  extra_params = c("na.rm", "image", "image.alpha", "image.scale", "crop"),
  default_aes = aes(
    shape = 21,
    colour = "black",
    point.size.factor = 1.0,
    fill = NA,
    alpha = NA,
    stroke = NA,
    stroke.alpha = NA
  ),
  setup_data = function(self, data, params) {
    data <- ggproto_parent(Geom, self)$setup_data(data, params)
    data
  },
  draw_key = draw_key_point,
  draw_panel = function(data, panel_scales, coord, image, image.alpha, image.scale, crop) {
    img.dim <- dim(image)

    # This should be in native units, where
    # Locations and sizes are relative to the x- and yscales for the current viewport.
    if (!crop) {
      # needs to be consistent with the origin being in the top-left
      panel_scales$x$continuous_range <- c(0, img.dim[[2]])
      panel_scales$y$continuous_range <- c(-img.dim[[1]], 0)
      panel_scales$x.range <- c(0, img.dim[[2]])
      panel_scales$y.range <- c(-img.dim[[1]], 0)
    }
    z <- coord$transform(
      data.frame(x = c(0, img.dim[[2]]), y = c(0, img.dim[[1]])),
      panel_scales
    )
    # Construct viewport for the plot based on img dimensions and plot coords
    wdth <- z$x[2] - z$x[1]
    hgth <- z$y[2] - z$y[1]
    plot_viewport <- viewport(
      x = unit(x = z$x[1], units = "npc"),
      y = unit(x = z$y[1], units = "npc"),
      width = unit(x = wdth, units = "npc"),
      height = unit(x = hgth, units = "npc"),
      just = c("left", "top")
    )

    # Calculate spot size relative to the image size to be consistent across different images and scales
    spot.radius <- Radius(object = image, scale = image.scale)
    base.point.size <- if (length(spot.radius) == 1L) {
      unit(spot.radius, "npc")
    } else {
      unit(rep_len(1, nrow(data)), "mm")
    }
    point.size <- base.point.size * data$point.size.factor
    coords <- coord$transform(data, panel_scales)

    stroke_color <- alpha(
      "black",
      ifelse(is.na(coords$stroke.alpha), coords$alpha, coords$stroke.alpha)
    )

    pts <- pointsGrob(
      x = coords$x,
      y = coords$y,
      pch = data$shape,
      size = point.size,
      gp = gpar(
        fill = alpha(colour = coords$fill, alpha = coords$alpha),
        col = stroke_color,
        lwd = coords$stroke
      )
    )
    canvas_viewport <- viewport()
    gt <- gTree(vp = canvas_viewport) # empty gTree to add the image and points to
    # Only draw the image if it has a non-zero alpha value, otherwise just draw the points
    if (image.alpha > 0) {
      img_grob <- GetImage(image)
      img <- editGrob(grob = img_grob, vp = plot_viewport)
      if (image.alpha != 1) {
        img$raster = as.raster(
          x = matrix(
            data = alpha(colour = img$raster, alpha = image.alpha),
            nrow = nrow(x = img$raster),
            ncol = ncol(x = img$raster),
            byrow = TRUE)
        )
      }
      gt <- addGrob(gTree = gt, child = img)
    }
    gt <- addGrob(gTree = gt, child = pts)
    # Replacement for ggname
    gt$name <- grobName(grob = gt, prefix = 'geom_spatial')
    return(gt)
  }
)

# influenced by: https://stackoverflow.com/questions/49475201/adding-tables-to-ggplot2-with-facet-wrap-in-r
# https://ggplot2.tidyverse.org/articles/extending-ggplot2.html
#' @importFrom ggplot2 layer
#'
#'
geom_spatial <-  function(
  mapping = NULL,
  data = NULL,
  image = image,
  image.alpha = image.alpha,
  image.scale = image.scale,
  crop = crop,
  stat = "identity",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
) {
  layer(
    geom = GeomSpatial,
    mapping = mapping,
    data = data,
    stat = stat,
    position = position,
    show.legend = show.legend,
    inherit.aes = inherit.aes,
    params = list(
      na.rm = na.rm,
      image = image,
      image.alpha = image.alpha,
      image.scale = image.scale,
      crop = crop,
      ...
    )
  )
}

#' @importFrom grid viewport editGrob grobName
#' @importFrom ggplot2 ggproto Geom ggproto_parent
#
GeomSpatialInteractive <- ggproto(
  "GeomSpatialInteractive",
  Geom,
  setup_data = function(self, data, params) {
    data <- ggproto_parent(parent = Geom, self = self)$setup_data(data, params)
    data
  },
  draw_group = function(data, panel_scales, coord) {
    vp <- viewport(x = data$x, y = data$y)
    g <- editGrob(grob = data$grob[[1]], vp = vp)
    # Replacement for ggname
    g$name <- grobName(grob = g, prefix = 'geom_spatial_interactive')
    return(g)
    # return(ggname(prefix = "geom_spatial", grob = g))
  },
  required_aes = c("grob","x","y")
)

#' @importFrom ggplot2 layer
#
geom_spatial_interactive <-  function(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = FALSE,
  ...
) {
  layer(
    geom = GeomSpatialInteractive,
    mapping = mapping,
    data = data,
    stat = stat,
    position = position,
    show.legend = show.legend,
    inherit.aes = inherit.aes,
    params = list(na.rm = na.rm, ...)
  )
}

# A split violin plot geom
#
#' @importFrom scales zero_range
#' @importFrom ggplot2 GeomPolygon
#' @importFrom grid grobTree grobName
#
# @author jan-glx on StackOverflow
# @references \url{https://stackoverflow.com/questions/35717353/split-violin-plot-with-ggplot2}
# @seealso \code{\link[ggplot2]{geom_violin}}
#
GeomSplitViolin <- ggproto(
  "GeomSplitViolin",
  GeomViolin,
  # setup_data = function(data, params) {
  #   data$width <- data$width %||% params$width %||% (resolution(data$x, FALSE) * 0.9)
  #   data <- plyr::ddply(data, "group", transform, xmin = x - width/2, xmax = x + width/2)
  #   e <- globalenv()
  #   name <- paste(sample(x = letters, size = 5), collapse = '')
  #   message("Saving initial data to ", name)
  #   e[[name]] <- data
  #   return(data)
  # },
  draw_group = function(self, data, ..., draw_quantiles = NULL) {
    data$xminv <- data$x - data$violinwidth * (data$x - data$xmin)
    data$xmaxv <- data$x + data$violinwidth * (data$xmax - data$x)
    grp <- data[1, 'group']
    if (grp %% 2 == 1) {
      data$x <- data$xminv
      data.order <- data$y
    } else {
      data$x <- data$xmaxv
      data.order <- -data$y
    }
    newdata <- data[order(data.order), , drop = FALSE]
    newdata <- rbind(
      newdata[1, ],
      newdata,
      newdata[nrow(x = newdata), ],
      newdata[1, ]
    )
    newdata[c(1, nrow(x = newdata) - 1, nrow(x = newdata)), 'x'] <- round(x = newdata[1, 'x'])
    grob <- if (length(x = draw_quantiles) > 0 & !zero_range(x = range(data$y))) {
      stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <= 1))
      quantiles <- QuantileSegments(data = data, draw.quantiles = draw_quantiles)
      aesthetics <- data[rep.int(x = 1, times = nrow(x = quantiles)), setdiff(x = names(x = data), y = c("x", "y")), drop = FALSE]
      aesthetics$alpha <- rep.int(x = 1, nrow(x = quantiles))
      both <- cbind(quantiles, aesthetics)
      quantile.grob <- GeomPath$draw_panel(both, ...)
      grobTree(GeomPolygon$draw_panel(newdata, ...), name = quantile.grob)
    }
    else {
      GeomPolygon$draw_panel(newdata, ...)
    }
    grob$name <- grobName(grob = grob, prefix = 'geom_split_violin')
    return(grob)
  }
)

# Create a split violin plot geom
#
# @inheritParams ggplot2::geom_violin
#
#' @importFrom ggplot2 layer
#
# @author jan-glx on StackOverflow
# @references \url{https://stackoverflow.com/questions/35717353/split-violin-plot-with-ggplot2}
# @seealso \code{\link[ggplot2]{geom_violin}}
#
geom_split_violin <- function(
  mapping = NULL,
  data = NULL,
  stat = 'ydensity',
  position = 'identity',
  ...,
  draw_quantiles = NULL,
  trim = TRUE,
  scale = 'area',
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
) {
  return(layer(
    data = data,
    mapping = mapping,
    stat = stat,
    geom = GeomSplitViolin,
    position = position,
    show.legend = show.legend,
    inherit.aes = inherit.aes,
    params = list(
      trim = trim,
      scale = scale,
      draw_quantiles = draw_quantiles,
      na.rm = na.rm,
      ...
    )
  ))
}

# Convert a ggplot2 scatterplot to base R graphics
#
# @param plot A ggplot2 scatterplot
# @param do.plot Create the plot with base R graphics
# @param cols A named vector of column names to pull. Vector names must be 'x',
# 'y', 'colour', 'shape', and/or 'size'; vector values must be the names of
# columns in plot data that correspond to these values. May pass only values that
# differ from the default (eg. \code{cols = c('size' = 'point.size.factor')})
# @param ... Extra parameters passed to PlotBuild
#
# @return A dataframe with the data that created the ggplot2 scatterplot
#
#' @importFrom ggplot2 ggplot_build
#
GGpointToBase <- function(
  plot,
  do.plot = TRUE,
  cols = c(
    'x' = 'x',
    'y' = 'y',
    'colour' = 'colour',
    'shape' = 'shape',
    'size' = 'size'
  ),
  ...
) {
  plot.build <- ggplot_build(plot = plot)
  default.cols <- c(
    'x' = 'x',
    'y' = 'y',
    'colour' = 'colour',
    'shape' = 'shape',
    'size' = 'size'
  )
  cols <- cols %||% default.cols
  if (is.null(x = names(x = cols))) {
    if (length(x = cols) > length(x = default.cols)) {
      warning(
        "Too many columns provided, selecting only first ",
        length(x = default.cols),
        call. = FALSE,
        immediate. = TRUE
      )
      cols <- cols[1:length(x = default.cols)]
    }
    names(x = cols) <- names(x = default.cols)[1:length(x = cols)]
  }
  cols <- c(
    cols[intersect(x = names(x = default.cols), y = names(x = cols))],
    default.cols[setdiff(x = names(x = default.cols), y = names(x = cols))]
  )
  cols <- cols[names(x = default.cols)]
  build.use <- which(x = vapply(
    X = plot.build$data,
    FUN = function(dat) {
      return(all(cols %in% colnames(x = dat)))
    },
    FUN.VALUE = logical(length = 1L)
  ))
  if (length(x = build.use) == 0) {
    stop("GGpointToBase only works on geom_point ggplot objects")
  }
  build.data <- plot.build$data[[min(build.use)]]
  plot.data <- build.data[, cols]
  names(x = plot.data) <- c(
    plot.build$plot$labels$x,
    plot.build$plot$labels$y,
    'color',
    'pch',
    'cex'
  )
  if (do.plot) {
    PlotBuild(data = plot.data, ...)
  }
  return(plot.data)
}

# Convert a ggplot2 scatterplot to plotly graphics
#
# @inheritParams GGpointToBase
# @param information Extra information for hovering
# @param ... Ignored
#
# @return A dataframe with the data that greated the ggplot2 scatterplot
#' @importFrom ggplot2 ggplot_build
#
GGpointToPlotlyBuild <- function(
  plot,
  information = NULL,
  cols = eval(expr = formals(fun = GGpointToBase)$cols),
  ...
) {
  CheckDots(...)
  plot.build <- GGpointToBase(plot = plot, do.plot = FALSE, cols = cols)
  data <- ggplot_build(plot = plot)$plot$data
  rownames(x = plot.build) <- rownames(data)
  # Reset the names to 'x' and 'y'
  names(x = plot.build) <- c(
    'x',
    'y',
    names(x = plot.build)[3:length(x = plot.build)]
  )
  # Add the hover information we're looking for
  if (is.null(x = information)) {
    plot.build$feature <- rownames(x = data)
  } else {
    info <- apply(
      X = information,
      MARGIN = 1,
      FUN = function(x, names) {
        return(paste0(names, ': ', x, collapse = '<br>'))
      },
      names = colnames(x = information)
    )
    data.info <- data.frame(
      feature = paste(rownames(x = information), info, sep = '<br>'),
      row.names = rownames(x = information)
    )
    plot.build <- merge(x = plot.build, y = data.info, by = 0)
    rownames(x = plot.build) <- plot.build$Row.names
    plot.build <- plot.build[, which(x = colnames(x = plot.build) != 'Row.names'), drop = FALSE]
  }
  return(plot.build)
}

#' @importFrom stats quantile
#'
InvertCoordinate <- function(x, MARGIN = 2) {
  if (!is.null(x = x)) {
    switch(
      EXPR = MARGIN,
      '1' = {
        rmin <- 'left'
        rmax <- 'right'
        cmin <- 'xmin'
        cmax <- 'xmax'
      },
      '2' = {
        rmin <- 'bottom'
        rmax <- 'top'
        cmin <- 'ymin'
        cmax <- 'ymax'
      },
      stop("'MARGIN' must be either 1 or 2", call. = FALSE)
    )
    # Fix the range so that rmin becomes rmax and vice versa
    # Needed for both points and brushes
    range <- x$range
    x$range[[rmin]] <- range[[rmax]]
    x$range[[rmax]] <- range[[rmin]]
    # Fix the cmin and cmax values, if provided
    # These are used for brush boundaries
    coords <- c(x[[cmin]], x[[cmax]])
    if (all(!is.null(x = coords))) {
      names(x = coords) <- c(cmin, cmax)
      x[[cmin]] <- quantile(
        x = x$range[[rmin]]:x$range[[rmax]],
        probs = 1 - (coords[cmax] / x$range[[rmax]]),
        names = FALSE
      )
      x[[cmax]] <- quantile(
        x = x$range[[rmin]]:x$range[[rmax]],
        probs = 1 - (coords[cmin] / x$range[[rmax]]),
        names = FALSE
      )
    }
  }
  return(x)
}

# Invert a Hexadecimal color
#
# @param hexadecimal A character vector of hexadecimal colors
#
# @return Hexadecimal representations of the inverted color
#
# @author Matt Lagrandeur
# @references \url{http://www.mattlag.com/scripting/hexcolorinverter.php}
#
InvertHex <- function(hexadecimal) {
  return(vapply(
    X = toupper(x = hexadecimal),
    FUN = function(hex) {
      hex <- unlist(x = strsplit(
        x = gsub(pattern = '#', replacement = '', x = hex),
        split = ''
      ))
      key <- toupper(x = as.hexmode(x = 15:0))
      if (!all(hex %in% key)) {
        stop('All hexadecimal colors must be valid hexidecimal numbers from 0-9 and A-F')
      }
      if (length(x = hex) == 8) {
        alpha <- hex[7:8]
        hex <- hex[1:6]
      } else if (length(x = hex) == 6) {
        alpha <- NULL
      } else {
        stop("All hexidecimal colors must be either 6 or 8 characters in length, excluding the '#'")
      }
      value <- rev(x = key)
      inv.hex <- vapply(
        X = hex,
        FUN = function(x) {
          return(value[grep(pattern = x, x = key)])
        },
        FUN.VALUE = character(length = 1L)
      )
      inv.hex <- paste(inv.hex, collapse = '')
      return(paste0('#', inv.hex, paste(alpha, collapse = '')))
    },
    FUN.VALUE = character(length = 1L),
    USE.NAMES = FALSE
  ))
}

# Make label information for ggplot2-based scatter plots
#
# @param data A three-column data frame (accessed with \code{plot$data})
# The first column should be the X axis, the second the Y, and the third should be grouping information
#
# @return A dataframe with three columns: centers along the X axis, centers along the Y axis, and group information
#
#' @importFrom stats median na.omit
#
MakeLabels <- function(data) {
  groups <- as.character(x = na.omit(object = unique(x = data[, 3])))
  labels <- lapply(
    X = groups,
    FUN = function(group) {
      data.use <- data[data[, 3] == group, 1:2]
      return(apply(X = data.use, MARGIN = 2, FUN = median, na.rm = TRUE))
    }
  )
  names(x = labels) <- groups
  labels <- as.data.frame(x = t(x = as.data.frame(x = labels)))
  labels[, colnames(x = data)[3]] <- groups
  return(labels)
}

# Plot expression of multiple features by identity on a plot
#
# @param data Data to plot
# @param idents Idents to use
# @param type Make either a 'ridge' or 'violin' plot
# @param sort Sort identity classes and features based on hierarchical clustering
# @param same.y.lims Indicates whether to use the same ylim for each feature
# @param adjust Adjust parameter for geom_violin
# @param cols Colors to use for plotting
# @param log plot Y axis on log scale
# @param fill.by Color violins/ridges based on either 'feature' or 'ident'
# @param seed.use Random seed to use. If NULL, don't set a seed
# @param flip flip plot orientation (identities on x-axis)
#
# @return A ggplot-based Expression-by-Identity plot
#
#' @importFrom cowplot theme_cowplot
#' @importFrom utils globalVariables
#' @importFrom stats rnorm dist hclust
#' @importFrom ggridges geom_density_ridges theme_ridges
#' @importFrom ggplot2 ggplot facet_grid theme labs geom_rect
#' geom_violin geom_jitter ylim position_jitterdodge scale_fill_manual
#' scale_y_log10 scale_x_log10 scale_y_discrete scale_x_continuous
#' scale_y_continuous waiver
#'
MultiExIPlot <- function(
  data,
  idents,
  split = NULL,
  type = 'violin',
  sort = FALSE,
  same.y.lims = same.y.lims,
  adjust = 1,
  pt.size = 0,
  cols = NULL,
  seed.use = 42,
  log = FALSE,
  fill.by = NULL,
  add.noise = TRUE,
  flip = NULL
) {
  if (!(fill.by %in% c("feature", "ident"))) {
    stop("`fill.by` must be either `feature` or `ident`")
  }
  if (!is.null(x = seed.use)) {
    set.seed(seed = seed.use)
  }
  if (!is.data.frame(x = data) || ncol(x = data) < 2) {
    stop("MultiExIPlot requires a data frame with >1 column")
  }
  data <- Melt(x = data)
  data <- data.frame(
    feature = data$cols,
    expression = data$vals,
    ident = rep_len(x = idents, length.out = nrow(x = data))
  )
  if ((is.character(x = sort) && nchar(x = sort) > 0) || sort) {
    data$feature <- as.vector(x = data$feature)
    data$ident <- as.vector(x = data$ident)
    # build matrix of average expression (#-features by #-idents), lexical ordering
    avgs.matrix <- sapply(
      X = split(x = data, f = data$ident),
      FUN = function(df) {
        return(tapply(
          X = df$expression,
          INDEX = df$feature,
          FUN = mean
        ))
      }
    )
    idents.order <- hclust(d = dist(x = t(x = L2Norm(mat = avgs.matrix, MARGIN = 2))))$order
    avgs.matrix <- avgs.matrix[,idents.order]
    avgs.matrix <- L2Norm(mat = avgs.matrix, MARGIN = 1)
    # order feature clusters by position of their "rank-1 idents"
    position <- apply(X = avgs.matrix, MARGIN = 1, FUN = which.max)
    mat <- hclust(d = dist(x = avgs.matrix))$merge
    orderings <- list()
    for (i in 1:nrow(mat)) {
      x <- if (mat[i,1] < 0) -mat[i,1] else orderings[[mat[i,1]]]
      y <- if (mat[i,2] < 0) -mat[i,2] else orderings[[mat[i,2]]]
      x.pos <- min(x = position[x])
      y.pos <- min(x = position[y])
      orderings[[i]] <- if (x.pos < y.pos) {
        c(x, y)
      } else {
        c(y, x)
      }
    }
    features.order <- orderings[[length(x = orderings)]]
    data$feature <- factor(
      x = data$feature,
      levels = unique(x = sort(x = data$feature))[features.order]
    )
    data$ident <- factor(
      x = data$ident,
      levels = unique(x = sort(x = data$ident))[rev(x = idents.order)]
    )
  } else {
    data$feature <- factor(x = data$feature, levels = unique(x = data$feature))
  }
  if (log) {
    noise <- rnorm(n = nrow(x = data)) / 200
    data$expression <- data$expression + 1
  } else {
    noise <- rnorm(n = nrow(x = data)) / 100000
  }
  if (!add.noise) {
    noise <- noise*0
  }
  for (f in unique(x = data$feature)) {
    if (all(data$expression[(data$feature == f)] == data$expression[(data$feature == f)][1])) {
      warning(
        "All cells have the same value of ",
        f,
        call. = FALSE,
        immediate. = TRUE
      )
    } else {
      data$expression[(data$feature == f)] <- data$expression[(data$feature == f)] + noise[(data$feature == f)]
    }
  }
  if (type == 'violin' && !is.null(x = split)) {
    data$split <- rep_len(x = split, length.out = nrow(data))
    vln.geom <- geom_violin
    fill.by <- 'split'
  } else if (type == 'splitViolin' && !is.null(x = split)) {
    data$split <- rep_len(x = split, length.out = nrow(data))
    vln.geom <- geom_split_violin
    fill.by <- 'split'
    type <- 'violin'
  } else {
    vln.geom <- geom_violin
  }
  switch(
    EXPR = type,
    'violin' = {
      geom <- list(vln.geom(scale = 'width', adjust = adjust, trim = TRUE))
    },
    'ridge' = {
      geom <- list(
        geom_density_ridges(scale = 4),
        theme_ridges(),
        scale_y_discrete(expand = c(0.01, 0))
      )
    },
    stop("Unknown plot type: ", type)
  )
  if (flip) {
    x <- 'ident'
    x.label <- 'Identity'
    y <- 'expression'
    y.label <- 'Expression Level'
  } else {
    y <- 'ident'
    y.label <- 'Identity'
    x <- 'expression'
    x.label <- 'Expression Level'
  }
  plot <- ggplot(
    data = data,
    mapping = aes(x = .data[[x]], y = .data[[y]], fill = .data[[fill.by]])[c(2, 3, 1)]
  ) +
    labs(x = x.label, y = y.label, fill = NULL) +
    theme_cowplot()
  for (layer in geom) {
    plot <- plot + layer
  }
  if (flip) {
    plot <- plot +
      scale_y_continuous(
        expand = c(0, 0),
        labels = function(x) c(rep(x = '', times = length(x)-2), x[length(x) - 1], '')) +
      facet_grid(feature ~ ., scales = (if (same.y.lims) 'fixed' else 'free')) +
      FacetTheme(
        panel.spacing = unit(0, 'lines'),
        panel.background = element_rect(fill = NA, color = "black"),
        axis.text.y = element_text(size = 7),
        axis.text.x = element_text(angle = 45, hjust = 1),
        strip.text.y.right = element_text(angle = 0))
  } else {
    plot <- plot +
      scale_x_continuous(
        expand = c(0, 0),
        labels = function(x) c(rep(x = '', times = length(x)-2), x[length(x) - 1], '')) +
      facet_grid(. ~ feature, scales = (if (same.y.lims) 'fixed' else 'free')) +
      FacetTheme(
        panel.spacing = unit(0, 'lines'),
        panel.background = element_rect(fill = NA, color = "black"),
        axis.text.x = element_text(size = 7),
        strip.text.x = element_text(angle = -90))
  }
  if (log) {
    plot <- plot + scale_x_log10()
  }
  if (!is.null(x = cols)) {
    if (!is.null(x = split)) {
      idents <- unique(x = as.vector(x = data$ident))
      splits <- unique(x = as.vector(x = data$split))
      labels <- if (length(x = splits) == 2) {
        splits
      } else {
        unlist(x = lapply(
          X = idents,
          FUN = function(pattern, x) {
            x.mod <- gsub(
              pattern = paste0(pattern, '.'),
              replacement = paste0(pattern, ': '),
              x = x,
              fixed = TRUE
            )
            x.keep <- grep(pattern = ': ', x = x.mod, fixed = TRUE)
            x.return <- x.mod[x.keep]
            names(x = x.return) <- x[x.keep]
            return(x.return)
          },
          x = unique(x = as.vector(x = data$split))
        ))
      }
      if (is.null(x = names(x = labels))) {
        names(x = labels) <- labels
      }
    } else {
      labels <- levels(x = droplevels(data$ident))
    }
    plot <- plot + scale_fill_manual(values = cols, labels = labels)
  }
  return(plot)
}

# Create a scatterplot with data from a ggplot2 scatterplot
#
# @param plot.data The original ggplot2 scatterplot data
# This is taken from ggplot2::ggplot_build
# @param dark.theme Plot using a dark theme
# @param smooth Use a smooth scatterplot instead of a standard scatterplot
# @param ... Extra parameters passed to graphics::plot or graphics::smoothScatter
#
#' @importFrom graphics axis plot smoothScatter
#
PlotBuild <- function(data, dark.theme = FALSE, smooth = FALSE, ...) {
  #   Do we use a smooth scatterplot?
  #   Take advantage of functions as first class objects
  #   to dynamically choose normal vs smooth scatterplot
  myplot <- ifelse(test = smooth, yes = smoothScatter, no = plot)
  CheckDots(..., fxns = myplot)
  if (dark.theme) {
    par(bg = 'black')
    axes = FALSE
    col.lab = 'white'
  } else {
    axes = 'TRUE'
    col.lab = 'black'
  }
  myplot(
    data[, c(1, 2)],
    col = data$color,
    pch = data$pch,
    cex = vapply(
      X = data$cex,
      FUN = function(x) {
        return(max(x / 2, 0.5))
      },
      FUN.VALUE = numeric(1)
    ),
    axes = axes,
    col.lab = col.lab,
    col.main = col.lab,
    ...
  )
  if (dark.theme) {
    axis(
      side = 1,
      at = NULL,
      labels = TRUE,
      col.axis = col.lab,
      col = col.lab
    )
    axis(
      side = 2,
      at = NULL,
      labels = TRUE,
      col.axis = col.lab,
      col = col.lab
    )
  }
}

# Locate points on a plot and return them
#
# @param plot A ggplot2 plot
# @param recolor Do we recolor the plot to highlight selected points?
# @param dark.theme Plot using a dark theme
# @param ... Exptra parameters to PlotBuild
#
# @return A dataframe of x and y coordinates for points selected
#
#' @importFrom graphics locator
# @importFrom SDMTools pnt.in.poly
#
PointLocator <- function(plot, recolor = TRUE, dark.theme = FALSE, ...) {
  .Defunct(new = "CellSelector")
  # #   Convert the ggplot object to a data.frame
  # PackageCheck('SDMTools')
  # plot.data <- GGpointToBase(plot = plot, dark.theme = dark.theme, ...)
  # npoints <- nrow(x = plot.data)
  # cat("Click around the cluster of points you wish to select\n")
  # cat("ie. select the vertecies of a shape around the cluster you\n")
  # cat("are interested in. Press <Esc> when finished (right click for R-terminal users)\n\n")
  # polygon <- locator(n = npoints, type = 'l')
  # polygon <- data.frame(polygon)
  # #   pnt.in.poly returns a data.frame of points
  # points.all <- SDMTools::pnt.in.poly(
  #   pnts = plot.data[, c(1, 2)],
  #   poly.pnts = polygon
  # )
  # #   Find the located points
  # points.located <- points.all[which(x = points.all$pip == 1), ]
  # #   If we're recoloring, do the recolor
  # if (recolor) {
  #   no <- ifelse(test = dark.theme, yes = 'white', no = '#C3C3C3')
  #   points.all$color <- ifelse(test = points.all$pip == 1, yes = '#DE2D26', no = no)
  #   plot.data$color <- points.all$color
  #   PlotBuild(data = plot.data, dark.theme = dark.theme, ...)
  # }
  # return(points.located[, c(1, 2)])
}

# Create quantile segments for quantiles on violin plots in ggplot2
#
# @param data Data being plotted
# @param draw.quantiles Quantiles to draw
#
#' @importFrom stats approxfun
#
# @author Hadley Wickham (I presume)
# @seealso \code{\link[ggplot2]{geom_violin}}
#
QuantileSegments <- function(data, draw.quantiles) {
  densities <- cumsum(x = data$density) / sum(data$density)
  ecdf <- approxfun(x = densities, y = data$y)
  ys <- ecdf(v = draw.quantiles)
  violin.xminvs <- approxfun(x = data$y, y = data$xminv)(v = ys)
  violin.xmaxvs <- approxfun(x = data$y, y = data$xmaxv)(v = ys)
  return(data.frame(
    x = as.vector(x = t(x = data.frame(violin.xminvs, violin.xmaxvs))),
    y = rep(x = ys, each = 2),
    group = rep(x = ys, each = 2)
  ))
}

# Scale vector to min and max cutoff values
#
# @param vec a vector
# @param cutoffs A two-length vector of cutoffs to be passed to \code{\link{SetQuantile}}
#
# @return Returns a vector
#
ScaleColumn <- function(vec, cutoffs) {
  if (!length(x = cutoffs) == 2) {
    stop("Two cutoffs (a low and high) are needed")
  }
  cutoffs <- sapply(
    X = cutoffs,
    FUN = SetQuantile,
    data = vec
  )
  vec[vec < min(cutoffs)] <- min(cutoffs)
  vec[vec > max(cutoffs)] <- max(cutoffs)
  return(vec)
}

# Set highlight information
#
# @param cells.highlight Cells to highlight
# @param cells.all A character vector of all cell names
# @param sizes.highlight Sizes of cells to highlight
# @param cols.highlight Colors to highlight cells as
# @param col.base Base color to use for unselected cells
# @param pt.size Size of unselected cells
# @param raster Convert points to raster format, default is \code{NULL} which
# automatically rasterizes if plotting more than 100,000 cells
#
# @return A list will cell highlight information
# \describe{
#   \item{plot.order}{An order to plot cells in}
#   \item{highlight}{A vector giving group information for each cell}
#   \item{size}{A vector giving size information for each cell}
#   \item{color}{Colors for highlighting in the order of plot.order}
# }
#
SetHighlight <- function(
  cells.highlight,
  cells.all,
  sizes.highlight,
  cols.highlight,
  col.base = 'black',
  pt.size = 1,
  raster = NULL
) {
  if (is.character(x = cells.highlight)) {
    cells.highlight <- list(cells.highlight)
  } else if (is.data.frame(x = cells.highlight) || !is.list(x = cells.highlight)) {
    cells.highlight <- as.list(x = cells.highlight)
  }
  cells.highlight <- lapply(
    X = cells.highlight,
    FUN = function(cells) {
      cells.return <- if (is.character(x = cells)) {
        cells[cells %in% cells.all]
      } else {
        cells <- as.numeric(x = cells)
        cells <- cells[cells <= length(x = cells.all)]
        cells.all[cells]
      }
      return(cells.return)
    }
  )
  cells.highlight <- Filter(f = length, x = cells.highlight)
  names.highlight <- if (is.null(x = names(x = cells.highlight))) {
    paste0('Group_', 1L:length(x = cells.highlight))
  } else {
    names(x = cells.highlight)
  }
  sizes.highlight <- rep_len(
    x = sizes.highlight,
    length.out = length(x = cells.highlight)
  )
  cols.highlight <- c(
    col.base,
    rep_len(x = cols.highlight, length.out = length(x = cells.highlight))
  )
  size <- rep_len(x = pt.size, length.out = length(x = cells.all))
  highlight <- rep_len(x = NA_character_, length.out = length(x = cells.all))
  if (length(x = cells.highlight) > 0) {
    for (i in 1:length(x = cells.highlight)) {
      cells.check <- cells.highlight[[i]]
      index.check <- match(x = cells.check, cells.all)
      highlight[index.check] <- names.highlight[i]
      size[index.check] <- sizes.highlight[i]
    }
  }

  # Check for raster
  if (isTRUE(x = raster)) {
    size <- sizes.highlight[1]
  }

  plot.order <- sort(x = unique(x = highlight), na.last = TRUE)
  plot.order[is.na(x = plot.order)] <- 'Unselected'
  highlight[is.na(x = highlight)] <- 'Unselected'
  highlight <- factor(x = highlight, levels = plot.order)
  return(list(
    plot.order = plot.order,
    highlight = highlight,
    size = size,
    color = cols.highlight
  ))
}

#' @importFrom shiny brushedPoints
#
ShinyBrush <- function(plot.data, brush, outputs, inverts = character(length = 0L)) {#}, selected = NULL) {
  selected <- NULL
  if (!is.null(x = brush)) {
    if (brush$outputId %in% outputs) {
      selected <- rownames(x = brushedPoints(df = plot.data, brush = brush))
    } else if (brush$outputId %in% inverts) {
      selected <- rownames(x = brushedPoints(
        df = plot.data,
        brush = InvertCoordinate(x = brush)
      ))
    }
  }
  return(selected)
}

globalVariables(names = '..density..', package = 'Seurat')
#' A single correlation plot
#'
#' @param data A data frame with two columns to be plotted
#' @param col.by A vector or factor of values to color the plot by
#' @param cols An optional vector of colors to use
#' @param pt.size Point size for the plot
#' @param smooth Make a smoothed scatter plot
#' @param rows.highlight A vector of rows to highlight (like cells.highlight in
#' \code{\link{SingleDimPlot}})
#' @param legend.title Optional legend title
#' @param raster Convert points to raster format, default is \code{NULL}
#' which will automatically use raster if the number of points plotted is
#' greater than 100,000
#' @param raster.dpi the pixel resolution for rastered plots, passed to geom_scattermore().
#' Default is c(512, 512)
#' @param plot.cor ...
#' @param jitter Jitter for easier visualization of crowded points
#'
#' @return A ggplot2 object
#'
#' @importFrom stats cor
#' @importFrom cowplot theme_cowplot
#' @importFrom RColorBrewer brewer.pal.info
#' @importFrom ggplot2 ggplot geom_point labs scale_color_brewer
#' scale_color_manual guides stat_density2d aes scale_fill_continuous
#' @importFrom scattermore geom_scattermore
#'
#' @keywords internal
#'
#' @export
#'
SingleCorPlot <- function(
  data,
  col.by = NULL,
  cols = NULL,
  pt.size = NULL,
  smooth = FALSE,
  rows.highlight = NULL,
  legend.title = NULL,
  na.value = 'grey50',
  span = NULL,
  raster = NULL,
  raster.dpi = NULL,
  plot.cor = TRUE,
  jitter = TRUE
) {
  pt.size <- pt.size %||% AutoPointSize(data = data, raster = raster)
  if ((nrow(x = data) > 1e5) & is.null(x = raster)){
    message("Rasterizing points since number of points exceeds 100,000.",
            "\nTo disable this behavior set `raster=FALSE`")
  }
  raster <- raster %||% (nrow(x = data) > 1e5)
  if (!is.null(x = raster.dpi)) {
    if (!is.numeric(x = raster.dpi) || length(x = raster.dpi) != 2)
      stop("'raster.dpi' must be a two-length numeric vector")
  }
  orig.names <- colnames(x = data)
  names.plot <- colnames(x = data) <- gsub(
    pattern = '-',
    replacement = '.',
    x = colnames(x = data),
    fixed = TRUE
  )
  names.plot <- colnames(x = data) <- gsub(
    pattern = ':',
    replacement = '.',
    x = colnames(x = data),
    fixed = TRUE
  )
  names.plot <- colnames(x = data) <- gsub(
    pattern = ' ',
    replacement = '.',
    x = colnames(x = data),
    fixed = TRUE
  )
  names.plot <- colnames(x = data) <- gsub(
    pattern = ')',
    replacement = '.',
    x = colnames(x = data),
    fixed = TRUE
  )
  names.plot <- colnames(x = data) <- gsub(
    pattern = '(',
    replacement = '.',
    x = colnames(x = data),
    fixed = TRUE
  )
  if (ncol(x = data) < 2) {
    msg <- "Too few variables passed"
    if (ncol(x = data) == 1) {
      msg <- paste0(msg, ', only have ', colnames(x = data)[1])
    }
    stop(msg, call. = FALSE)
  }
  plot.cor <- if (isTRUE(x = plot.cor)) {
    round(x = cor(x = data[, 1], y = data[, 2]), digits = 2)
  }
  else(
    ""
  )
  if (!is.null(x = rows.highlight)) {
    highlight.info <- SetHighlight(
      cells.highlight = rows.highlight,
      cells.all = rownames(x = data),
      sizes.highlight = pt.size,
      cols.highlight = 'red',
      col.base = 'black',
      pt.size = pt.size,
      raster = raster
    )
    cols <- highlight.info$color
    col.by <- factor(
      x = highlight.info$highlight,
      levels = rev(x = highlight.info$plot.order)
    )
    plot.order <- order(col.by)
    data <- data[plot.order, ]
    col.by <- col.by[plot.order]
  }
  if (!is.null(x = col.by)) {
    data$colors <- col.by
  }
  plot <- ggplot(
    data = data,
    mapping = aes(x = .data[[names.plot[1]]], y = .data[[names.plot[2]]])
    ) +
    labs(
      x = orig.names[1],
      y = orig.names[2],
      title = plot.cor,
      color = legend.title
    )
  if (smooth) {
    # density <- kde2d(x = data[, names.plot[1]], y = data[, names.plot[2]], h = Bandwidth(data = data[, names.plot]), n = 200)
    # density <- data.frame(
    #   expand.grid(
    #     x = density$x,
    #     y = density$y
    #   ),
    #   density = as.vector(x = density$z)
    # )
    plot <- plot + stat_density2d(
      mapping = aes(fill = ..density.. ^ 0.25),
      geom = 'tile',
      contour = FALSE,
      n = 200,
      h = Bandwidth(data = data[, names.plot])
    ) +
      # geom_tile(
      #   mapping = aes_string(
      #     x = 'x',
      #     y = 'y',
      #     fill = 'density'
      #   ),
      #   data = density
      # ) +
      scale_fill_continuous(low = 'white', high = 'dodgerblue4') +
      guides(fill = "none")
  }
  position <- NULL
  if (jitter) {
    position <- 'jitter'
  } else {
    position <- 'identity'
  }
  if (!is.null(x = col.by)) {
    if (raster) {
      plot <- plot + geom_scattermore(
        mapping = aes(color = .data[['colors']]),
        position = position,
        pointsize = pt.size,
        pixels = raster.dpi
      )
    } else {
      plot <- plot + geom_point(
        mapping = aes(color = .data[['colors']]),
        position = position,
        size = pt.size
      )
    }
  } else {
    if (raster) {
      plot <- plot + geom_scattermore(position = position, pointsize = pt.size, pixels = raster.dpi)
    } else {
      plot <- plot + geom_point(position = position, size = pt.size)
    }
  }
  if (!is.null(x = cols)) {
    cols.scale <- if (length(x = cols) == 1 && cols %in% rownames(x = brewer.pal.info)) {
      scale_color_brewer(palette = cols)
    } else {
      scale_color_manual(values = cols, na.value = na.value)
    }
    plot <- plot + cols.scale
    if (!is.null(x = rows.highlight)) {
      plot <- plot + guides(color = "none")
    }
  }
  plot <- plot + theme_cowplot() + theme(plot.title = element_text(hjust = 0.5))
  if (!is.null(x = span)) {
    plot <- plot + geom_smooth(
      mapping = aes(x = .data[[names.plot[1]]], y = .data[[names.plot[2]]]),
      method = 'loess',
      span = span
    )
  }
  return(plot)
}

#' Plot a single dimension
#'
#' @param data Data to plot
#' @param dims A two-length numeric vector with dimensions to use
#' @param col.by ...
#' @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
#' @param pt.size Adjust point size for plotting
#' @param shape.by If NULL, all points are circles (default). You can specify
#' any cell attribute (that can be pulled with \code{\link{FetchData}})
#' allowing for both different colors and different shapes on cells.
#' @param alpha Alpha value for plotting (default is 1)
#' @param alpha.by Mapping variable for the point alpha value
#' @param stroke.size Adjust stroke (outline) size of points
#' @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 repel Repel labels
#' @param label.size Sets size of 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 raster Convert points to raster format, default is \code{NULL}
#' which will automatically use raster if the number of points plotted is
#' greater than 100,000
#' @param raster.dpi the pixel resolution for rastered plots, passed to geom_scattermore().
#' Default is c(512, 512)
#'
#' @return A ggplot2 object
#'
#' @importFrom cowplot theme_cowplot
#' @importFrom RColorBrewer brewer.pal.info
#' @importFrom ggplot2 ggplot geom_point labs guides scale_color_brewer
#' scale_color_manual element_rect guide_legend discrete_scale
#'
#' @keywords internal
#'
#' @export
#'
SingleDimPlot <- function(
  data,
  dims,
  col.by = NULL,
  cols = NULL,
  pt.size = NULL,
  shape.by = NULL,
  alpha = 1,
  alpha.by = NULL,
  stroke.size = NULL,
  order = NULL,
  label = FALSE,
  repel = FALSE,
  label.size = 4,
  cells.highlight = NULL,
  cols.highlight = '#DE2D26',
  sizes.highlight = 1,
  na.value = 'grey50',
  raster = NULL,
  raster.dpi = NULL
) {
  if ((nrow(x = data) > 1e5) & is.null(x = raster)){
    message("Rasterizing points since number of points exceeds 100,000.",
            "\nTo disable this behavior set `raster=FALSE`")
  }
  raster <- raster %||% (nrow(x = data) > 1e5)
  # IF raster is TRUE and order is specified, points are plotted with ggrastr::geom_point_rast to maintain 
  # correct ordering of points
  order_rast <- isTRUE(raster) && (!(isFALSE(x = order) || is.null(x = order)))
  if (order_rast) {
    # Check if ggrastr installed correctly and in namespace
    if (isFALSE(x = requireNamespace('ggrastr', quietly = TRUE))){
      stop("Please install ggrastr from CRAN to enable ordered rasterization.")
    }
  }
  pt.size <- pt.size %||% AutoPointSize(data = data, raster = raster)
  if (is.null(x = stroke.size)) {
    stroke.size <- 0.600075815011372
  }

  if (!is.null(x = cells.highlight) && pt.size != AutoPointSize(data = data, raster = raster) && sizes.highlight != pt.size && isTRUE(x = raster)) {
    warning("When `raster = TRUE` highlighted and non-highlighted cells must be the same size. Plot will use the value provided to 'sizes.highlight'.")
  }

  if (!is.null(x = raster.dpi)) {
    if (!is.numeric(x = raster.dpi) || length(x = raster.dpi) != 2)
      stop("'raster.dpi' must be a two-length numeric vector")
  }
  if (length(x = dims) != 2) {
    stop("'dims' must be a two-length vector")
  }
  if (!is.data.frame(x = data)) {
    data <- as.data.frame(x = data)
  }
  if (is.character(x = dims) && !all(dims %in% colnames(x = data))) {
    stop("Cannot find dimensions to plot in data")
  } else if (is.numeric(x = dims)) {
    dims <- colnames(x = data)[dims]
  }
  if (!is.null(x = cells.highlight)) {
    if (inherits(x = cells.highlight, what = "data.frame")) {
      stop("cells.highlight cannot be a dataframe. ",
           "Please supply a vector or list")
    }
    highlight.info <- SetHighlight(
      cells.highlight = cells.highlight,
      cells.all = rownames(x = data),
      sizes.highlight = sizes.highlight %||% pt.size,
      cols.highlight = cols.highlight,
      col.base = cols[1] %||% '#C3C3C3',
      pt.size = pt.size,
      raster = raster
    )
    order <- highlight.info$plot.order
    data$highlight <- highlight.info$highlight
    col.by <- 'highlight'
    pt.size <- highlight.info$size
    cols <- highlight.info$color
  }
  if (!is.null(x = order) && !is.null(x = col.by)) {
    if (typeof(x = order) == "logical") {
      if (order) {
        data <- data[order(!is.na(x = data[, col.by]), data[, col.by]), ]
      }
    } else {
      order <- rev(x = c(
        order,
        setdiff(x = unique(x = data[, col.by]), y = order)
      ))
      data[, col.by] <- factor(x = data[, col.by], levels = order)
      new.order <- order(x = data[, col.by])
      data <- data[new.order, ]
      if (length(x = pt.size) == length(x = new.order)) {
        pt.size <- pt.size[new.order]
      }
    }
  }
  if (!is.null(x = col.by) && !col.by %in% colnames(x = data)) {
    warning("Cannot find ", col.by, " in plotting data, not coloring plot")
    col.by <- NULL
  } else {
    # col.index <- grep(pattern = col.by, x = colnames(x = data), fixed = TRUE)
    col.index <- match(x = col.by, table = colnames(x = data))
    if (grepl(pattern = '^\\d', x = col.by)) {
      # Do something for numbers
      col.by <- paste0('x', col.by)
    } else if (grepl(pattern = '-', x = col.by)) {
      # Do something for dashes
      col.by <- gsub(pattern = '-', replacement = '.', x = col.by)
    }
    colnames(x = data)[col.index] <- col.by
  }
  if (!is.null(x = shape.by) && !shape.by %in% colnames(x = data)) {
    warning("Cannot find ", shape.by, " in plotting data, not shaping plot")
  }
  if (!is.null(x = alpha.by) && !alpha.by %in% colnames(x = data)) {
    warning(
      "Cannot find alpha variable ",
      alpha.by,
      " in data, setting to NULL",
      call. = FALSE,
      immediate. = TRUE
    )
    alpha.by <- NULL
  }

  # Modify optional parameters to work with tidyeval when NULL and safe naming of color/col.by
  # see https://github.com/tidyverse/ggplot2/issues/6208
  # see https://github.com/tidyverse/ggplot2/pull/6215/commits/eaeee6d75cc60118919ca4a06a04cd4549af123c
  optional  <- list(color = col.by, shape = shape.by, alpha = alpha.by)
  is_symbol <- lengths(optional) > 0
  optional  <- c(rlang::data_syms(optional[is_symbol]), optional[!is_symbol])
  # Separate plotting aesthetics based on whether alpha is defined by a scalar or a mapping variable
  use.alpha <- is.null(x = alpha.by) 
  
  plot <- ggplot(data = data)
  plot <- if (isTRUE(x = raster)) {
    # Use geom_point_rast when generating rasterized plots with specified order
    # (although slower, orders points correctly whereas geom_scattermore does not)
    if (!order_rast) {
      if (use.alpha) {
        plot + geom_scattermore(
          mapping = aes(x = .data[[dims[1]]], y = .data[[dims[2]]], !!!optional),
          pointsize = pt.size,
          alpha = alpha,
          pixels = raster.dpi
        )
      } else {
        plot + geom_scattermore(
          mapping = aes(x = .data[[dims[1]]], y = .data[[dims[2]]], !!!optional),
          pointsize = pt.size,
          pixels = raster.dpi
        )
      }
    } else {
      rlang::warn(message = "Seurat uses ggrastr::rasterise to maintain point order with rasterization.", 
                  .frequency = "once",
                  .frequency_id = "Seurat-ggrastr-rasterise")
      if (use.alpha) {
        plot + ggrastr::rasterise(geom_point(
          mapping = aes(x = .data[[dims[1]]], y = .data[[dims[2]]], !!!optional),
          size = pt.size,
          alpha = alpha
        ))
      } else {
        plot + ggrastr::rasterise(geom_point(
          mapping = aes(x = .data[[dims[1]]], y = .data[[dims[2]]], !!!optional),
          size = pt.size
        ))
      }
    }
  } else {
    if (use.alpha) {
      plot + geom_point(
        mapping = aes(x = .data[[dims[1]]], y = .data[[dims[2]]], !!!optional),
        size = pt.size,
        alpha = alpha,
        stroke = stroke.size
      )
    } else {
      plot + geom_point(
        mapping = aes(x = .data[[dims[1]]], y = .data[[dims[2]]], !!!optional),
        size = pt.size,
        stroke = stroke.size
      )
    }
  }
  plot <- plot +
    guides(color = guide_legend(override.aes = list(size = 3, alpha = 1))) +
    labs(color = NULL, title = col.by) +
    CenterTitle()
  if (label && !is.null(x = col.by)) {
    plot <- LabelClusters(
      plot = plot,
      id = col.by,
      repel = repel,
      size = label.size
    )
  }
  if (!is.null(x = cols)) {
    if (length(x = cols) == 1 && (is.numeric(x = cols) || cols %in% rownames(x = brewer.pal.info))) {
      scale <- scale_color_brewer(palette = cols, na.value = na.value)
    } else if (length(x = cols) == 1 && (cols %in% c('alphabet', 'alphabet2', 'glasbey', 'polychrome', 'stepped'))) {
      colors <- DiscretePalette(length(unique(data[[col.by]])), palette = cols)
      scale <- scale_color_manual(values = colors, na.value = na.value)
    } else {
      scale <- scale_color_manual(values = cols, na.value = na.value)
    }
    plot <- plot + scale
  }
  plot <- plot + theme_cowplot()
  return(plot)
}

#' Plot a single expression by identity on a plot
#'
#' @param data Data to plot
#' @param idents Idents to use
#' @param split Use a split violin plot
#' @param type Make either a \dQuote{ridge} or \dQuote{violin} plot
#' @param sort Sort identity classes (on the x-axis) by the average
#' expression of the attribute being potted
#' @param y.max Maximum Y value to plot
#' @param adjust Adjust parameter for geom_violin
#' @param pt.size Size of points for violin plots
#' @param alpha Alpha vlaue for violin plots
#' @param cols Colors to use for plotting
#' @param seed.use Random seed to use. If NULL, don't set a seed
#' @param log plot Y axis on log10 scale
#' @param add.noise determine if adding small noise for plotting
#' @param raster Convert points to raster format. Requires 'ggrastr' to be installed.
#' default is \code{NULL} which automatically rasterizes if ggrastr is installed and
#' number of points exceed 100,000.
#' @param raster.dpi the dpi for raster layer, default is 300.
#' See \code{\link[ggrastr]{rasterize}} for more info.
#'
#' @return A ggplot-based Expression-by-Identity plot
#'
#' @importFrom stats rnorm
#' @importFrom utils globalVariables
#' @importFrom ggridges geom_density_ridges theme_ridges
#' @importFrom ggplot2 ggplot theme labs geom_violin geom_jitter
#' ylim position_jitterdodge scale_fill_manual scale_y_log10 scale_x_log10
#' scale_y_discrete scale_x_continuous waiver
#' @importFrom cowplot theme_cowplot
#'
#' @keywords internal
#' @export
#'
SingleExIPlot <- function(
  data,
  idents,
  split = NULL,
  type = 'violin',
  sort = FALSE,
  y.max = NULL,
  adjust = 1,
  pt.size = 0,
  alpha = 1,
  cols = NULL,
  seed.use = 42,
  log = FALSE,
  add.noise = TRUE,
  raster = NULL,
  raster.dpi = NULL
) {
   if (!is.null(x = raster) && isTRUE(x = raster)){
     if (isFALSE(x = requireNamespace('ggrastr', quietly = TRUE))) {
      stop("Please install ggrastr from CRAN to enable rasterization.")
    }
  }
  if (isTRUE(x = requireNamespace('ggrastr', quietly = TRUE))) {
    # Set rasterization to true if ggrastr is installed and
    # number of points exceeds 100,000
    if ((nrow(x = data) > 1e5) & is.null(x = raster)){
      message("Rasterizing points since number of points exceeds 100,000.",
              "\nTo disable this behavior set `raster=FALSE`")
      # change raster to TRUE
      raster <- TRUE
    }
  }
  if (!is.null(x = seed.use)) {
    set.seed(seed = seed.use)
  }
  if (!is.data.frame(x = data) || ncol(x = data) != 1) {
    stop("'SingleExIPlot requires a data frame with 1 column")
  }
  feature <- colnames(x = data)
  data$ident <- idents
  if ((is.character(x = sort) && nchar(x = sort) > 0) || sort) {
    data$ident <- factor(
      x = data$ident,
      levels = names(x = rev(x = sort(
        x = tapply(
          X = data[, feature],
          INDEX = data$ident,
          FUN = mean
        ),
        decreasing = grepl(pattern = paste0('^', tolower(x = sort)), x = 'decreasing')
      )))
    )
  }
  if (log) {
    noise <- rnorm(n = length(x = data[, feature])) / 200
    data[, feature] <- data[, feature] + 1
  } else {
    noise <- rnorm(n = length(x = data[, feature])) / 100000
  }
  if (!add.noise) {
    noise <-  noise * 0
  }
  if (all(data[, feature] == data[, feature][1])) {
    warning(paste0("All cells have the same value of ", feature, "."))
  } else{
    data[, feature] <- data[, feature] + noise
  }
  axis.label <- 'Expression Level'
  y.max <- y.max %||% max(data[, feature][is.finite(x = data[, feature])])
  if (type == 'violin' && !is.null(x = split)) {
    data$split <- split
    vln.geom <- geom_violin
    fill <- 'split'
  } else if (type == 'splitViolin' && !is.null(x = split )) {
    data$split <- split
    vln.geom <- geom_split_violin
    fill <- 'split'
    type <- 'violin'
  } else {
    vln.geom <- geom_violin
    fill <- 'ident'
  }
  switch(
    EXPR = type,
    'violin' = {
      x <- data_sym("ident")
      y <- data_sym(feature)
      xlab <- 'Identity'
      ylab <- axis.label
      geom <- list(
        vln.geom(scale = 'width', adjust = adjust, trim = TRUE),
        theme(axis.text.x = element_text(angle = 45, hjust = 1))
      )
      if (is.null(x = split)) {
        if (isTRUE(x = raster)) {
          jitter <- ggrastr::rasterize(geom_jitter(height = 0, size = pt.size, alpha = alpha, show.legend = FALSE), dpi = raster.dpi)
        } else {
          jitter <- geom_jitter(height = 0, size = pt.size, alpha = alpha, show.legend = FALSE)
        }
      } else {
        if (isTRUE(x = raster)) {
          jitter <- ggrastr::rasterize(geom_jitter(
            position = position_jitterdodge(jitter.width = 0.4, dodge.width = 0.9),
            size = pt.size,
            alpha = alpha,
            show.legend = FALSE
          ), dpi = raster.dpi)
        } else {
          jitter <- geom_jitter(
            position = position_jitterdodge(jitter.width = 0.4, dodge.width = 0.9),
            size = pt.size,
            alpha = alpha,
            show.legend = FALSE
          )
        }
      }
      log.scale <- scale_y_log10()
      axis.scale <- ylim
    },
    'ridge' = {
      x <- data_sym(feature)
      y <- data_sym("ident")
      xlab <- axis.label
      ylab <- 'Identity'
      geom <- list(
        geom_density_ridges(scale = 4),
        theme_ridges(),
        scale_y_discrete(expand = c(0.01, 0)),
        scale_x_continuous(expand = c(0, 0))
      )
      jitter <- geom_jitter(width = 0, size = pt.size, alpha = alpha, show.legend = FALSE)
      log.scale <- scale_x_log10()
      axis.scale <- function(...) {
        invisible(x = NULL)
      }
    },
    stop("Unknown plot type: ", type)
  )
  plot <- ggplot(
    data = data,
    mapping = aes(x = !!x, y = !!y, fill = .data[[fill]])[c(2, 3, 1)]
  ) +
    labs(x = xlab, y = ylab, title = feature, fill = NULL) +
    theme_cowplot() +
    theme(plot.title = element_text(hjust = 0.5))
  for (layer in geom) {
    plot <- plot + layer
  }
  plot <- plot + if (log) {
    log.scale
  } else {
    axis.scale(min(data[, feature]), y.max)
  }
  if (pt.size > 0) {
    plot <- plot + jitter
  }
  if (!is.null(x = cols)) {
    if (!is.null(x = split)) {
      idents <- unique(x = as.vector(x = data$ident))
      splits <- unique(x = as.vector(x = data$split))
      labels <- if (length(x = splits) == 2) {
        splits
      } else {
        unlist(x = lapply(
          X = idents,
          FUN = function(pattern, x) {
            x.mod <- gsub(
              pattern = paste0(pattern, '.'),
              replacement = paste0(pattern, ': '),
              x = x,
              fixed = TRUE
            )
            x.keep <- grep(pattern = ': ', x = x.mod, fixed = TRUE)
            x.return <- x.mod[x.keep]
            names(x = x.return) <- x[x.keep]
            return(x.return)
          },
          x = unique(x = as.vector(x = data$split))
        ))
      }
      if (is.null(x = names(x = labels))) {
        names(x = labels) <- labels
      }
    } else {
      labels <- levels(x = droplevels(data$ident))
    }
    plot <- plot + scale_fill_manual(values = cols, labels = labels)
  }
  return(plot)
}

#' A single heatmap from base R using \code{\link[graphics]{image}}
#'
#' @param data matrix of data to plot
#' @param order optional vector of cell names to specify order in plot
#' @param title Title for plot
#'
#' @return No return, generates a base-R heatmap using \code{\link[graphics]{image}}
#'
#' @importFrom graphics axis image par plot.new title
#'
#' @keywords internal
#'
#' @export
#'
SingleImageMap <- function(data, order = NULL, title = NULL) {
  if (!is.null(x = order)) {
    data <- data[order, ]
  }
  par(mar = c(1, 1, 3, 3))
  plot.new()
  image(
    x = as.matrix(x = data),
    axes = FALSE,
    add = TRUE,
    col = PurpleAndYellow()
  )
  axis(
    side = 4,
    at = seq(from = 0, to = 1, length = ncol(x = data)),
    labels = colnames(x = data),
    las = 1,
    tick = FALSE,
    mgp = c(0, -0.5, 0),
    cex.axis = 0.75
  )
  title(main = title)
}

#' Single Spatial Plot
#'
#' @param data A data frame with at least the following columns:
#' \itemize{
#'  \item \dQuote{\code{x}}: Spatial-resolved \emph{x} coordinates, will be
#'   plotted on the \emph{y}-axis
#'  \item \dQuote{\code{y}}: Spatially-resolved \emph{y} coordinates, will be
#'   plotted on the \emph{x}-axis
#'  \item \dQuote{\code{cell}}: Cell name
#'  \item \dQuote{\code{boundary}}: Segmentation boundary label; when plotting
#'   multiple segmentation layers, the order of boundary transparency is set by
#'   factor levels for this column
#' }
#' Can pass \code{NA} to \code{data} suppress segmentation visualization
#' @param col.by Name of column in \code{data} to color cell segmentations by;
#' pass \code{NA} to suppress coloring
#' @param col.factor Are the colors a factor or discrete?
#' @param cols Colors for cell segmentations; can be one of the
#' following:
#' \itemize{
#'  \item \code{NULL} for default ggplot2 colors
#'  \item A numeric value or name of a
#'   \link[RColorBrewer:RColorBrewer]{color brewer palette}
#'  \item Name of a palette for \code{\link{DiscretePalette}}
#'  \item A vector of colors equal to the length of unique levels of
#'   \code{data$col.by}
#' }
#' @param shuffle.cols Randomly shuffle colors when a palette or
#' vector of colors is provided to \code{cols}
#' @param size Point size for cells when plotting centroids
#' @param molecules A data frame with spatially-resolved molecule coordinates;
#' should have the following columns:
#' \itemize{
#'  \item \dQuote{\code{x}}: Spatial-resolved \emph{x} coordinates, will be
#'   plotted on the \emph{y}-axis
#'  \item \dQuote{\code{y}}: Spatially-resolved \emph{y} coordinates, will be
#'   plotted on the \emph{x}-axis
#'  \item \dQuote{\code{molecule}}: Molecule name
#' }
#' @param mols.size Point size for molecules
#' @param mols.cols A vector of color for molecules. The "Set1" palette from
#' RColorBrewer is used by default.
#' @param mols.alpha Alpha value for molecules, should be between 0 and 1
#' @param alpha Alpha value, should be between 0 and 1; when plotting multiple
#' boundaries, \code{alpha} is equivalent to max alpha
#' @param border.color Color of cell segmentation border; pass \code{NA}
#' to suppress borders for segmentation-based plots
#' @param border.size Thickness of cell segmentation borders; pass \code{NA}
#' to suppress borders for centroid-based plots
#' @param na.value Color value for \code{NA} segmentations when
#' using custom scale
#' @param ... Ignored
#'
#' @return A ggplot object
#'
#' @importFrom rlang is_na
#' @importFrom SeuratObject %NA% %!NA%
#' @importFrom RColorBrewer brewer.pal.info
#' @importFrom ggplot2 geom_point geom_polygon ggplot guides
#' guide_legend scale_alpha_manual scale_color_manual scale_fill_brewer
#' scale_fill_manual
#'
#' @keywords internal
#'
#' @export
#'
SingleImagePlot <- function(
  data,
  col.by = NA,
  col.factor = TRUE,
  cols = NULL,
  shuffle.cols = FALSE,
  size = 0.1,
  molecules = NULL,
  mols.size = 0.1,
  mols.cols = NULL,
  mols.alpha = 1.0,
  alpha = molecules %iff% 0.3 %||% 0.6,
  border.color = 'white',
  border.size = NULL,
  na.value = 'grey50',
  dark.background = TRUE,
  ...
) {
  # Check input data
  if (!is_na(x = data)) {
    if (!all(c('x', 'y', 'cell', 'boundary') %in% colnames(x = data))) {
      stop("Invalid data coordinates")
    }
    if (!is_na(x = col.by)) {
      if (!col.by  %in% colnames(x = data)) {
        warning(
          "Cannot find 'col.by' ('",
          col.by,
          "') in data coordinates",
          immediate. = TRUE
        )
        col.by <- NA
      } else if (isTRUE(x = col.factor) && !is.factor(x = data[[col.by]])) {
        data[[col.by]] <- factor(
          x = data[[col.by]],
          levels = unique(x = data[[col.by]])
        )
      } else if (isFALSE(x = col.factor) && is.factor(x = data[[col.by]])) {
        data[[col.by]] <- as.vector(x = data[[col.by]])
      }
    }
    if (is_na(x = col.by) && !is.null(x = cols)) {
      col.by <- RandomName(length = 7L)
      data[[col.by]] <- TRUE
    }
    if (!is.factor(x = data$boundary)) {
      data$boundary <- factor(
        x = data$boundary,
        levels = unique(x = data$boundary)
      )
    }
    # Determine alphas
    if (is.na(x = alpha)) {
      alpha <- 1L
    }
    alpha.min <- ifelse(
      test = alpha < 1L,
      yes = 1 * (10 ^ .FindE(x = alpha)),
      no = 0.1
    )
    if (alpha.min == alpha) {
      alpha.min <- 1 * (10 ^ (.FindE(x = alpha) - 1))
    }
    alphas <- .Cut(
      min = alpha.min,
      max = alpha,
      n = length(x = levels(x = data$boundary))
    )
  }

  # Normalize the optional column name
  col_clean <- if (is.null(col.by) || is.na(col.by) || !nzchar(col.by)) NULL else col.by
  # Build optional aes for fill
  aes_extra <- if (is.null(col_clean)) list() else list(fill = sym(col_clean))


  # Assemble plot
  plot <- ggplot(
    data = data,
    mapping = aes(
      x = .data[["y"]],
      y = .data[["x"]],
      alpha = .data[["boundary"]],
      !!!aes_extra
    )
  )
  if (!is_na(x = data)) {
    plot <- plot +
      scale_alpha_manual(values = alphas) +
      if (anyDuplicated(x = data$cell)) {
        if (is.null(border.size)) {
          border.size <- 0.3
        }
        geom_polygon(
          mapping = aes(group = .data[['cell']]),
          color = border.color,
          linewidth = border.size
        )
      } else {
        # Default to no borders when plotting centroids
        if (is.null(border.size)) {
          border.size <- 0.0
        }
        geom_point(
          shape = 21,
          color = border.color,
          stroke = border.size,
          size = size
        )
      }
    if (!is.null(x = cols)) {
      plot <- plot + if (is.numeric(x = cols) || cols[1L] %in% rownames(x = brewer.pal.info)) {
        palette <- brewer.pal(n = length(x = levels(x = data[[col.by]])), cols)
        if (length(palette) < length(x = levels(x = data[[col.by]]))) {
          num.blank <- length(x = levels(x = data[[col.by]])) - length(palette)
          palette <- c(palette, rep(na.value, num.blank))
        }
        if (isTRUE(shuffle.cols)) {
          palette <- sample(palette)
        }
        scale_fill_manual(values = palette, na.value = na.value)
      } else if (cols[1] %in% DiscretePalette(n = NULL)) {
        scale_fill_manual(
          values = DiscretePalette(
            n = length(x = levels(x = data[[col.by]])),
            palette = cols,
            shuffle = shuffle.cols
          ),
          na.value = na.value
        )
      } else {
        if (isTRUE(shuffle.cols)) {
          cols <- sample(cols)
        }
        scale_fill_manual(values = cols, na.value = na.value)
      }
    }
    if (length(x = levels(x = data$boundary)) == 1L) {
      plot <- plot + guides(alpha = 'none')
    }
    # Adjust guides
    if (isTRUE(x = col.factor) && length(x = levels(x = data[[col.by]])) <= 1L) {
      plot <- plot + guides(fill = 'none')
    }
  }
  # Add molecule sets
  if (is.data.frame(x = molecules)) {
    if (all(c('x', 'y', 'molecule') %in% colnames(x = molecules))) {
      if (!is.factor(x = molecules$molecule)) {
        molecules$molecule <- factor(
          x = molecules$molecule,
          levels = unique(x = molecules$molecule)
        )
      }
      plot <- plot + geom_point(
        mapping = aes(fill = NULL, alpha = NULL, color = .data[["molecule"]]),
        data = molecules,
        size = mols.size,
        alpha = mols.alpha,
        show.legend = c(color = TRUE, fill = FALSE, alpha = FALSE)
      ) +
        guides(color = guide_legend(override.aes = list(size = 3L))) +
        scale_color_manual(
          name = 'Molecules',
          values = mols.cols %||% brewer.pal(
            n = length(x = levels(x = molecules$molecule)),
            name = "Set1"
          ),
          guide = guide_legend()
        )
    } else {
      warning("Invalid molecule coordinates", immediate. = TRUE)
    }
  }

  if (isTRUE(dark.background)) {
    plot <- plot + DarkTheme()
  }
  return(plot)
}

# A single polygon plot
#
# @param data Data to plot
# @param group.by Grouping variable
# @param ... Extra parameters passed to \code{\link[cowplot]{theme_cowplot}}
#
# @return A ggplot-based plot
#
#' @importFrom cowplot theme_cowplot
#' @importFrom ggplot2 ggplot geom_polygon
#
# @seealso \code{\link[cowplot]{theme_cowplot}}
#
SinglePolyPlot <- function(data, group.by, ...) {
  plot <- ggplot(data = data, mapping = aes(x = .data[['x']], y = .data[['y']])) +
    geom_polygon(mapping = aes(fill = .data[[group.by]], group = .data[['cell']])) +
    coord_fixed() +
    theme_cowplot(...)
  return(plot)
}

#' A single heatmap from ggplot2 using geom_raster
#'
#' @param data A matrix or data frame with data to plot
#' @param raster switch between geom_raster and geom_tile
#' @param cell.order ...
#' @param feature.order ...
#' @param colors A vector of colors to use
#' @param disp.min Minimum display value (all values below are clipped)
#' @param disp.max Maximum display value (all values above are clipped)
#' @param limits A two-length numeric vector with the limits for colors on the plot
#' @param group.by A vector to group cells by, should be one grouping identity per cell
#'
#' @return A ggplot2 object
#
#' @importFrom ggplot2 ggplot geom_raster scale_fill_gradient
#' scale_fill_gradientn theme element_blank labs geom_point guides
#' guide_legend geom_tile
#'
#' @keywords internal
#'
#' @export
#
SingleRasterMap <- function(
  data,
  raster = TRUE,
  cell.order = NULL,
  feature.order = NULL,
  colors = PurpleAndYellow(),
  disp.min = -2.5,
  disp.max = 2.5,
  limits = NULL,
  group.by = NULL
) {
  data <- MinMax(data = data, min = disp.min, max = disp.max)
  data <- Melt(x = t(x = data))
  colnames(x = data) <- c('Feature', 'Cell', 'Expression')
  if (!is.null(x = feature.order)) {
    data$Feature <- factor(x = data$Feature, levels = unique(x = feature.order))
  }
  if (!is.null(x = group.by)) {
    data$Identity <- group.by[data$Cell]
  }
  if (!is.null(x = cell.order)) {
    data$Cell <- factor(x = data$Cell, levels = unique(x = cell.order))
  }
  limits <- limits %||% c(min(data$Expression), max(data$Expression))
  if (length(x = limits) != 2 || !is.numeric(x = limits)) {
    stop("limits' must be a two-length numeric vector")
  }
  my_geom <- ifelse(test = raster, yes = geom_raster, no = geom_tile)
  plot <- ggplot(data = data) +
    my_geom(mapping = aes(x = .data[['Cell']], y = .data[['Feature']], fill = .data[['Expression']])) +
    theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
    scale_fill_gradientn(limits = limits, colors = colors, na.value = "white") +
    labs(x = NULL, y = NULL, fill = group.by %iff% 'Expression') +
    WhiteBackground() + NoAxes(keep.text = TRUE)
  if (!is.null(x = group.by)) {
    plot <- plot + geom_point(
      mapping = aes(x = .data[['Cell']], y = .data[['Feature']], color = .data[['Identity']]),
      alpha = 0
    ) +
      guides(color = guide_legend(override.aes = list(alpha = 1)))
  }
  return(plot)
}

#' Base plotting function for all Spatial plots
#'
#' @param data Data.frame with info to be plotted
#' @param image \code{SpatialImage} object to be plotted
#' @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
#' @param image.alpha Adjust the opacity of the background images. Set to 0 to
#' remove.
#' @param image.scale Choose the scale factor ("lowres"/"hires") to apply in
#' order to matchthe plot with the specified `image` - defaults to "lowres"
#' @param pt.alpha Adjust the opacity of the points if plotting a
#' \code{SpatialDimPlot}
#' @param crop Crop the plot in to focus on points plotted. Set to \code{FALSE}
#' to show entire background image.
#' @param pt.size.factor Sets the size of the points relative to spot.radius
#' @param stroke Control the width of the border around the spots
#' @param stroke.alpha Control the opacity of spot borders (when stroke is specified). 
#' Set to \code{NA} to use the same alpha as the fill.
#' @param shape Control the shape of the spots - same as the ggplot2 parameter.
#' The default is 21, which plots cirlces - use 22 to plot squares.
#' @param col.by Mapping variable for the point color
#' @param alpha.by Mapping variable for the point alpha value
#' @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
#' cols.highlight
#' @param cols.highlight A vector of colors to highlight the cells as; ordered
#' the same as the groups in cells.highlight; last color corresponds to
#' unselected cells.
#' @param geom Switch between normal spatial geom and geom to enable hover
#' functionality
#' @param na.value Color for spots with NA values
#'
#' @return A ggplot2 object
#'
#' @importFrom tibble tibble
#' @importFrom ggplot2 ggplot coord_fixed geom_point xlim ylim
#' coord_cartesian labs theme_void theme scale_fill_brewer
#' scale_y_reverse annotation_custom after_scale
#'
#' @keywords internal
#'
#' @export
#'
SingleSpatialPlot <- function(
  data,
  image,
  cols = NULL,
  image.alpha = 1,
  image.scale = "lowres",
  pt.alpha = NULL,
  crop = TRUE,
  pt.size.factor = NULL,
  shape = 21,
  stroke = NA,
  stroke.alpha = NA,
  col.by = NULL,
  alpha.by = NULL,
  cells.highlight = NULL,
  cols.highlight = c('#DE2D26', 'grey50'),
  geom = c('spatial', 'interactive', 'poly', 'poly_starmap'),
  na.value = 'grey50'
) {
  geom <- match.arg(arg = geom)
  image_dim <- dim(image)
  image.height <- image_dim[1]
  image.width <- image_dim[2]
  ggplot_version_4 <- packageVersion("ggplot2") >= "4.0.0"

  # Helper to set coord limits based on image dimensions
  coord_limits <- function() {
    xlim <- if (!crop) c(0, image.width) else NULL
    ylim <- NULL
    if (!crop) {
      if (!ggplot_version_4) {
        message("Changing coordinate limits to work with ggplot2 < 4.0.0.")
        ylim <- c(image.height, 0)
      } else {
        ylim <- c(0, image.height)
      }
    }
    list(x = xlim, y = ylim)
  }

  # Helper to create custom annotation layer for the background image 
  build_image_annotation <- function() {
    if (image.alpha == 0) {
      return(NULL)
    }
    image_to_plot <- GetImage(object = image, mode = "raw")
    if (image.alpha < 1) {
      rgba_image <- array(data = NA, dim = c(image_dim[1:2], 4))
      rgba_image[, , 1:3] <- image_to_plot
      rgba_image[, , 4] <- image.alpha
      image_to_plot <- rgba_image
    }
    image.grob <- rasterGrob(
      image_to_plot,
      width = unit(1, "npc"),
      height = unit(1, "npc"),
      interpolate = FALSE
    )
    if (!ggplot_version_4) {
      message("Changing image annotation limits to work with ggplot2 < 4.0.0.")
      return(annotation_custom(
        grob = image.grob,
        xmin = 0,
        xmax = image.width,
        ymin = -image.height,
        ymax = 0
      ))
    }
    return(annotation_custom(
      grob = image.grob,
      xmin = 0,
      xmax = image.width,
      ymin = 0,
      ymax = image.height
    ))
  }

  limits <- coord_limits()

  if (!is.null(col.by) && !col.by %in% colnames(data)) {
    warning("Cannot find '", col.by, "' in data, not coloring", call. = FALSE, immediate. = TRUE)
    col.by <- NULL
  }

  if (!is.null(x = cells.highlight)) {
    highlight.info <- SetHighlight(
      cells.highlight = cells.highlight,
      cells.all = rownames(x = data),
      sizes.highlight = pt.size.factor,
      cols.highlight = cols.highlight[1],
      col.base = cols.highlight[2]
    )
    order <- highlight.info$plot.order
    data$highlight <- highlight.info$highlight
    col.by <- 'highlight'
    levels(x = data$ident) <- c(order, setdiff(x = levels(x = data$ident), y = order))
    data <- data[order(data$ident), ]
  }

  col.by.plot <- col.by %iff% data_sym(col.by) # for use in aes() if not NULL
  col.by <- col.by %iff% paste0("`", col.by, "`") # quote + wrap in backticks for safe direct use within [[ ]]
  col.by.clean <- gsub("`", "", col.by) # quote

  alpha.by <- alpha.by %iff% data_sym(alpha.by)

  xname <- colnames(x = data)[1]
  yname <- colnames(x = data)[2]

  plot <- ggplot(data = data, aes(
    x = .data[[xname]],
    y = .data[[yname]],
    fill = !!col.by.plot,
    alpha = !!alpha.by
  ))

  plot <- switch(
    EXPR = geom,
    'spatial' = {
      geom_spatial_layer <- if (is.null(pt.alpha)) {
        geom_spatial(
          point.size.factor = pt.size.factor,
          data = data,
          image = image,
          image.alpha = image.alpha,
          image.scale = image.scale,
          crop = crop,
          shape = shape,
          stroke = stroke,
          stroke.alpha = stroke.alpha
        )
      } else {
        geom_spatial(
          point.size.factor = pt.size.factor,
          data = data,
          image = image,
          image.alpha = image.alpha,
          image.scale = image.scale,
          crop = crop,
          shape = shape,
          stroke = stroke,
          stroke.alpha = stroke.alpha,
          alpha = pt.alpha
        )
      }
      plot + geom_spatial_layer +
        coord_fixed(xlim = limits$x, ylim = limits$y) + 
        scale_y_reverse() +
        theme_void()
    },
    'interactive' = {
      plot + geom_spatial_interactive(
        data = tibble(
          grob = list(
            GetImage(
              object = image,
              mode = 'grob'
            )
          )
        ),
        mapping = aes(grob = .data[['grob']]),
        x = 0.5,
        y = 0.5
      ) +
        geom_point(mapping = aes(color = .data[[col.by]])) +
        xlim(0, ncol(x = image)) +
        ylim(nrow(x = image), 0) +
        coord_cartesian(expand = FALSE)
    },
    'poly' = {
      # define aesthetics for geom_polygon layer based on if pt.alpha (SDP) or alpha.by (SFP) is set
      geom_poly_layer <- if (is.null(pt.alpha)) {
        if (is.na(x = stroke.alpha)) {
          geom_polygon(
            data = data,
            aes(x = .data[[xname]], y = .data[[yname]], fill = !!col.by.plot, alpha = !!alpha.by, group = .data[['cell']], color = after_scale(alpha("black", alpha))),
            linewidth = stroke
          )
        } else {
          geom_polygon(
            data = data,
            aes(x = .data[[xname]], y = .data[[yname]], fill = !!col.by.plot, alpha = !!alpha.by, group = .data[['cell']]),
            color = alpha("black", stroke.alpha),
            linewidth = stroke
          )
        }
      } else {
        geom_polygon(
          data = data,
          aes(x = .data[[xname]], y = .data[[yname]], fill = !!col.by.plot, group = .data[['cell']]),
          alpha = pt.alpha,
          color = if (is.na(x = stroke.alpha)) alpha("black", pt.alpha) else alpha("black", stroke.alpha),
          linewidth = stroke
        )
      }
      ggplot() +
        build_image_annotation() +
        geom_poly_layer +
        scale_y_reverse() +
        coord_fixed(xlim = limits$x, ylim = limits$y) +
        theme_void()
    },
    'poly_starmap' = {
      data$cell <- rownames(x = data)
      data[, c('x', 'y')] <- NULL
      data <- merge(
        x = data,
        y = GetTissueCoordinates(
          object = image,
          qhulls = TRUE,
          scale = image.scale
        ),
        by = "cell"
      )
      plot + geom_polygon(
        data = data,
        mapping = aes(fill = !!col.by.plot, group = .data[['cell']])
      ) + coord_fixed() + theme_cowplot()
    },
    stop("Unknown geom, choose from 'spatial' or 'interactive'", call. = FALSE)
  )
  scale.aesthetic <- if (geom == "interactive") "color" else "fill"
  if (!is.null(x = cells.highlight)) {
    plot <- plot + if (scale.aesthetic == "fill") {
      scale_fill_manual(values = cols.highlight)
    } else {
      scale_color_manual(values = cols.highlight)
    }
  }
  if (!is.null(x = cols) && is.null(x = cells.highlight)) {
    if (length(x = col.by.clean) == 1L && nzchar(x = col.by.clean)) {
      data[[col.by.clean]] <- as.character(data[[col.by.clean]])
      plot <- plot + .BuildDiscreteScale(data = data, cols = cols, col.by = col.by.clean, na.value = na.value, aesthetic = scale.aesthetic)
    }
  }
  plot <- plot + NoAxes() + theme(panel.background = element_blank())
  return(plot)
}

# Reimplementation of ggplot2 coord$transform
#
# @param data A data frame with x-coordinates in the first column and y-coordinates
# in the second
# @param xlim,ylim X- and Y-limits for the transformation, must be two-length
# numeric vectors
#
# @return \code{data} with transformed coordinates
#
#' @importFrom ggplot2 transform_position
#' @importFrom scales rescale squish_infinite
#
Transform <- function(data, xlim = c(-Inf, Inf), ylim = c(-Inf, Inf)) {
  # Quick input argument checking
  if (!all(sapply(X = list(xlim, ylim), FUN = length) == 2)) {
    stop("'xlim' and 'ylim' must be two-length numeric vectors", call. = FALSE)
  }
  # Save original names
  df.names <- colnames(x = data)
  colnames(x = data)[1:2] <- c('x', 'y')
  # Rescale the X and Y values
  data <- transform_position(
    df = data,
    trans_x = function(df) {
      return(rescale(x = df, from = xlim))
    },
    trans_y = function(df) {
      return(rescale(x = df, from = ylim))
    }
  )
  # Something that ggplot2 does
  data <- transform_position(
    df = data,
    trans_x = squish_infinite,
    trans_y = squish_infinite
  )
  # Restore original names
  colnames(x = data) <- df.names
  return(data)
}

#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# S4 Methods
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

setMethod(
  f = '.PrepImageData',
  signature = c(data = 'data.frame', cells = 'rle'),
  definition = function(data, cells, ...) {
    data <- sapply(
      X = colnames(x = data),
      FUN = function(x) {
        j <- data[[x]]
        names(x = j) <- rownames(x = data)
        return(.PrepImageData(data = j, cells = cells, name = x))
      },
      simplify = FALSE,
      USE.NAMES = TRUE
    )
    return(do.call(what = 'cbind', args = data))
  }
)

#' @importFrom methods getMethod
setMethod(
  f = '.PrepImageData',
  signature = c(data = 'factor', cells = 'rle'),
  definition = function(data, cells, name, ...) {
    f <- getMethod(
      f = '.PrepImageData',
      signature = c(data = 'vector', cells = 'rle')
    )
    return(f(data = data, cells = cells, name = name, ...))
  }
)

setMethod(
  f = '.PrepImageData',
  signature = c(data = 'list', cells = 'rle'),
  definition = function(data, cells, ...) {
    .NotYetImplemented()
  }
)

setMethod(
  f = '.PrepImageData',
  signature = c(data = 'NULL', cells = 'rle'),
  definition = function(data, cells, ...) {
    return(SeuratObject::EmptyDF(n = sum(cells$lengths)))
  }
)

setMethod(
  f = '.PrepImageData',
  signature = c(data = 'vector', cells = 'rle'),
  definition = function(data, cells, name, ...) {
    name <- as.character(x = name)
    if (name %in% c('x', 'y', 'cell')) {
      stop("'name' cannot be 'x', 'y', or 'cell'", call. = FALSE)
    }
    cnames <- cells$values
    if (is.null(x = names(x = data))) {
      mlen <- min(sapply(X = list(data, cnames), FUN = length))
      names(x = data)[1:mlen] <- cnames[1:mlen]
    }
    if (anyDuplicated(x = names(x = data))) {
      dup <- duplicated(x = names(x = data))
      warning(
        sum(dup),
        ifelse(test = sum(dup) == 1, yes = ' cell', no = ' cells'),
        ' duplicated, using only the first occurance',
        call. = FALSE,
        immediate. = TRUE
      )
    }
    if (length(x = data) < length(x = cnames)) {
      mcells <- setdiff(x = cnames, y = names(x = data))
      warning(
        "Missing data for some cells, filling with NA",
        call. = FALSE,
        immediate. = TRUE
      )
      data[mcells] <- NA
    } else if (length(x = data) > length(x = cnames)) {
      warning(
        "More cells provided than present",
        call. = FALSE,
        immediate. = TRUE
      )
    }
    data <- data.frame(rep.int(x = data[cnames], times = cells$lengths))
    colnames(x = data) <- name
    return(data)
  }
)
