Comprehensive group splited cluster plot on dimension reduction with proportion

This function produces combined plot on group level (e.g. dataset, other metadata variable like biological conditions). Scatter plot of dimension reduction with cluster labeled is generated per group. Furthermore, a stacked barplot of cluster proportion within each group is also combined with the subplot of each group.

Usage

plotGroupClusterDimRed(
  object,
  useGroup = "dataset",
  useCluster = NULL,
  useDimRed = NULL,
  combinePlot = TRUE,
  droplevels = TRUE,
  relHeightMainLegend = c(5, 1),
  relHeightDRBar = c(10, 1),
  mainNRow = NULL,
  mainNCol = NULL,
  legendNRow = 1,
  ...
)

Arguments

object

A liger object with dimension reduction, grouping variable and cluster assignment in cellMeta(object).

useGroup

Variable name of the group division in metadata. Default "dataset".

useCluster

Name of variable in cellMeta(object). Default NULL uses default cluster.

useDimRed

Name of the variable storing dimensionality reduction result in cellMeta(object). Default NULL use default dimRed.

combinePlot

Whether to return combined plot. Default TRUE. If FALSE, will return a list containing only the scatter plots.

droplevels

Logical, whether to perform droplevels() on the selected grouping variable. Default TRUE will not show groups that are listed as categories but do not indeed have any cells.

relHeightMainLegend

Relative heights of the main combination panel and the legend at the bottom. Must be a numeric vector of 2 numbers. Default c(5, 1).

relHeightDRBar

Relative heights of the scatter plot and the barplot within each subpanel. Must be a numeric vector of 2 numbers. Default c(10, 1).

mainNRow, mainNCol

Arrangement of the main plotting region, for number of rows and columns. Default NULL will be automatically handled by plot_grid.

legendNRow

Arrangement of the legend, number of rows. Default 1.

...

Arguments passed on to .ggScatter, .ggplotLigerTheme

dotOrder: Controls the order that each dot is added to the plot. Choose from "shuffle", "ascending", or "descending". Default "shuffle", useful when coloring by categories that overlaps (e.g. "dataset"), "ascending" can be useful when coloring by a continuous variable (e.g. gene expression) where high values needs more highlight. NULL use default order.
dotSize,dotAlpha: Numeric, controls the size or transparency of all dots. Default getOption("ligerDotSize") (1) and 0.9.
raster: Logical, whether to rasterize the plot. Default NULL automatically rasterize the plot when number of total dots to be plotted exceeds 100,000.
labelText: Logical, whether to show text label at the median position of each categorical group specified by colorBy. Default TRUE. Does not work when continuous coloring is specified.
labelTextSize: Numeric, controls the size of label size when labelText = TRUE. Default 4.
seed: Random seed for reproducibility. Default 1.
baseSize: One-parameter control of all text sizes. Individual text element sizes can be controlled by other size arguments. "Title" sizes are 2 points larger than "text" sizes when being controlled by this.
panelBorder: Whether to show rectangle border of the panel instead of using ggplot classic bottom and left axis lines. Default FALSE.
colorValues: Character vector of colors for modifying category colors in a color legend. Passed to ggplot2::scale_color_manual(values). Default NULL uses internal selected palette when <= 26 categories are presented, otherwise ggplot hues.
naColor: The color code for NA values. Default "#DEDEDE". scale_colour_gradient2. Default NULL.
plotly: Whether to use plotly to enable web based interactive browsing for the plot. Requires installation of package "plotly". Default FALSE.

Value

ggplot object when only one feature (e.g. cluster variable, gene, factor) is set. List object when multiple of those are specified.

Examples

plotGroupClusterDimRed(pbmcPlot)
#> ℹ Plotting feature "leiden_cluster_ctrl" on 600 cells
#> ✔ Plotting feature "leiden_cluster_ctrl" on 600 cells ... done
#> 
#> ℹ Plotting feature "leiden_cluster_stim" on 600 cells
#> ✔ Plotting feature "leiden_cluster_stim" on 600 cells ... done
#>