dittoBarPlot: Outputs a stacked bar plot to show the percent composition of...

In dtm2451/dittoSeq: User Friendly Single-Cell and Bulk RNA Sequencing Visualization

Outputs a stacked bar plot to show the percent composition of samples, groups, clusters, or other groupings

Description

Outputs a stacked bar plot to show the percent composition of samples, groups, clusters, or other groupings

Usage

dittoBarPlot(
  object,
  var,
  group.by,
  scale = c("percent", "count"),
  split.by = NULL,
  cells.use = NULL,
  retain.factor.levels = FALSE,
  data.out = FALSE,
  do.hover = FALSE,
  color.panel = dittoColors(),
  colors = seq_along(color.panel),
  split.nrow = NULL,
  split.ncol = NULL,
  split.adjust = list(),
  y.breaks = NA,
  min = 0,
  max = NULL,
  var.labels.rename = NULL,
  var.labels.reorder = NULL,
  x.labels = NULL,
  x.labels.rotate = TRUE,
  x.reorder = NULL,
  theme = theme_classic(),
  xlab = group.by,
  ylab = "make",
  main = "make",
  sub = NULL,
  legend.show = TRUE,
  legend.title = NULL
)

Arguments

object	A Seurat, SingleCellExperiment, or SummarizedExperiment object.
var	String name of a metadata that contains discrete data, or a factor or vector containing such data for all cells/samples in the target object.
group.by	String name of a metadata to use for separating the cells/samples into discrete groups.
scale	"count" or "percent". Sets whether data should be shown as counts versus percentage.
split.by	1 or 2 strings naming discrete metadata to use for splitting the cells/samples into multiple plots with ggplot faceting. When 2 metadatas are named, c(row,col), the first is used as rows and the second is used for columns of the resulting grid. When 1 metadata is named, shape control can be achieved with split.nrow and split.ncol
cells.use	String vector of cells'/samples' names OR an integer vector specifying the indices of cells/samples which should be included. Alternatively, a Logical vector, the same length as the number of cells in the object, which sets which cells to include. Note: When cells.use is combined with scale = "percent", left out cells are not considered in calculating percentages. Percents will always total to 1.
retain.factor.levels	Logical which controls whether factor identities of var and group.by data should be respected. Set to TRUE to faithfully reflect ordering of groupings encoded in factor levels, but Note that this will also force retention of groupings that could otherwise be removed via cells.use.
data.out	Logical. When set to TRUE, changes the output, from the plot alone, to a list containing the plot ("p") and a data.frame ("data") containing the underlying data.
do.hover	Logical which sets whether the ggplot output should be converted to a ggplotly object with data about individual bars displayed when you hover your cursor over them.
color.panel	String vector which sets the colors to draw from. dittoColors() by default.
colors	Integer vector, which sets the indexes / order, of colors from color.panel to actually use. (Provides an alternative to directly modifying color.panel.)
split.nrow, split.ncol	Integers which set the dimensions of faceting/splitting when a single metadata is given to split.by.
split.adjust	A named list which allows extra parameters to be pushed through to the faceting function call. List elements should be valid inputs to the faceting functions, e.g. 'list(scales = "free")'. For options, when giving 1 metadata to split.by, see facet_wrap, OR when giving 2 metadatas to split.by, see facet_grid.
y.breaks	Numeric vector which sets the plot's tick marks / major gridlines. c(break1,break2,break3,etc.)
min, max	Scalars which control the zoom of the plot. These inputs set the minimum / maximum values of the y-axis. Default = set based on the limits of the data, 0 to 1 for scale = "percent", or 0 to maximum count for 0 to 1 for scale = "count".
var.labels.rename	String vector for renaming the distinct identities of var values. Hint: use metaLevels or unique(<var-data>) to assess current values.
var.labels.reorder	Integer vector. A sequence of numbers, from 1 to the number of distinct var value identities, for rearranging the order of labels' groupings within the plot. Method: Make a first plot without this input. Then, treating the top-most grouping as index 1, and the bottom-most as index n. Values of var.labels.reorder should be these indices, but in the order that you would like them rearranged to be.
x.labels	String vector which will replace the x-axis groupings' labels. Regardless of x.reorder, the first component of x.labels sets the name for the left-most x-axis grouping.
x.labels.rotate	Logical which sets whether the x-axis grouping labels should be rotated.
x.reorder	Integer vector. A sequence of numbers, from 1 to the number of groupings, for rearranging the order of x-axis groupings. Method: Make a first plot without this input. Then, treating the leftmost grouping as index 1, and the rightmost as index n. Values of x.reorder should be these indices, but in the order that you would like them rearranged to be. Recommendation for advanced users: If you find yourself coming back to this input too many times, an alternative solution that can be easier long-term is to make the target data into a factor, and to put its levels in the desired order: factor(data, levels = c("level1", "level2", ...)). metaLevels can be used to quickly get the identities that need to be part of this 'levels' input.
theme	A ggplot theme which will be applied before dittoSeq adjustments. Default = theme_classic(). See https://ggplot2.tidyverse.org/reference/ggtheme.html for other options and ideas.
xlab	String which sets the x-axis title. Default is group.by so it defaults to the name of the grouping information. Set to NULL to remove.
ylab	String which sets the y-axis title. Default = "make" and if left as make, a title will be automatically generated.
main	String, sets the plot title
sub	String, sets the plot subtitle
legend.show	Logical which sets whether the legend should be displayed.
legend.title	String which adds a title to the legend.

Details

The function creates a dataframe containing counts and percent makeup of var identities for each x-axis grouping (determined by the group.by input). If a set of cells/samples to use is indicated with the cells.use input, only those cells/samples are used for counts and percent makeup calculations. Then, a vertical bar plot is generated (ggplot2::geom_col()) showing either percent makeup if scale = "percent", which is the default, or raw counts if scale = "count".

Value

A ggplot plot where discrete data, grouped by sample, condition, cluster, etc. on the x-axis, is shown on the y-axis as either counts or percent-of-total-per-grouping in a stacked barplot.

Alternatively, if data.out = TRUE, a list containing the plot ("p") and a dataframe of the underlying data ("data").

Alternatively, if do.hover = TRUE, a plotly conversion of the ggplot output in which underlying data can be retrieved upon hovering the cursor over the plot.

Many characteristics of the plot can be adjusted using discrete inputs

• Colors can be adjusted with color.panel and/or colors.

• y-axis zoom and tick marks can be adjusted using min, max, and y.breaks.

• Titles can be adjusted with main, sub, xlab, ylab, and legend.title arguments.

• The legend can be removed by setting legend.show = FALSE.

• x-axis labels and groupings can be changed / reordered using x.labels and x.reorder, and rotation of these labels can be turned off with x.labels.rotate = FALSE.

• y-axis var-group labels and their order can be changed / reordered using var.labels and var.labels.reorder.

Author(s)

Daniel Bunis

See Also

dittoFreqPlot for a data representation that focuses on pre-sample frequencies of each the var-data values individually, rather than emphasizing total makeup of samples/groups.

Examples

example(importDittoBulk, echo = FALSE)
myRNA

dittoBarPlot(myRNA, "clustering", group.by = "groups")
dittoBarPlot(myRNA, "clustering", group.by = "groups",
    scale = "count")

# Reordering the x-axis groupings to have "C" (#3) come first
dittoBarPlot(myRNA, "clustering", group.by = "groups",
    x.reorder = c(3,1,2,4))

### Accessing underlying data:
# as dataframe
dittoBarPlot(myRNA, "clustering", group.by = "groups",
    data.out = TRUE)
# through hovering the cursor over the relevant parts of the plot
if (requireNamespace("plotly", quietly = TRUE)) {
    dittoBarPlot(myRNA, "clustering", group.by = "groups",
        do.hover = TRUE)
    }

### Previous Version Compatibility
# Mistakenly, dittoBarPlot used to remove factor identities entirely from the
#  data it used. This manifests as ignorance of a user's set orderings for
#  their data. That is nolonger done by default, but to recreate old plots,
#  restoring this behavior can be achieved with 'retain.factor.levels = FALSE'
# Set factor level ordering for a metadata we'll give to 'group.by'
myRNA$groups_reverse_levels <- factor(
    myRNA$groups,
    levels = c("D", "C", "B", "A"))
# dittoBarPlot will now respect this level order by default. 
dittoBarPlot(myRNA, "clustering", group.by = "groups_reverse_levels")
# But that respect can be turned off...
dittoBarPlot(myRNA, "clustering", group.by = "groups_reverse_levels",
    retain.factor.levels = FALSE)

dtm2451/dittoSeq documentation built on May 5, 2024, 11:19 a.m.