multi_dittoDimPlotVaryCells: Generates multiple dittoDimPlots, for a single feature, where...

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

Description

Generates multiple dittoDimPlots, for a single feature, where each showing different cells

Usage

multi_dittoDimPlotVaryCells(
  object,
  var,
  vary.cells.meta,
  vary.cells.levels = metaLevels(vary.cells.meta, object),
  show.titles = TRUE,
  show.allcells.plot = TRUE,
  allcells.main = "All Cells",
  show.legend.single = TRUE,
  show.legend.plots = FALSE,
  show.legend.allcells.plot = FALSE,
  nrow = NULL,
  ncol = NULL,
  list.out = FALSE,
  OUT.List = NULL,
  ...,
  assay = .default_assay(object),
  slot = .default_slot(object),
  adjustment = NULL,
  min = NULL,
  max = NULL,
  color.panel = dittoColors(),
  colors = seq_along(color.panel),
  data.out = FALSE,
  do.hover = FALSE
)

Arguments

object	A Seurat, SingleCellExperiment, or SummarizedExperiment object.
var	String name of a "gene" or "metadata" (or "ident" for a Seurat object) to use for coloring the plots. This is the data that will be displayed, using colors, for each cell/sample. Alternatively, can be a vector of same length as there are cells/samples in the object. Discrete or continuous data both work.
vary.cells.meta	String name of a metadata that should be used for selecting which cells to show in each "VaryCells" dittoDimPlot.
vary.cells.levels	The values/groupings of the vary.cells.meta metadata for which to generate a plot.
show.titles	Logical which sets whether grouping-levels should be used as titles for the individual VaryCell plots. Default = TRUE.
show.allcells.plot	Logical which sets whether an additional plot showing all of the cells should be added.
allcells.main	String which adjusts the title of the allcells plot. Default = "All Cells". Set to NULL to remove.
show.legend.single	Logical which sets whether to add a single legend as an additional plot. Default = TRUE.
show.legend.plots	Logical which sets whether or not legends should be plotted in inidividual VaryCell plots. Default = FALSE.
show.legend.allcells.plot	Logical which sets whether or a legend should be plotted in the allcells plot. Default = FALSE.
ncol, nrow	Integers which set dimensions of the plot grid when list.out = FALSE.
list.out	Logical which controls whether the list of plots should be returned as a list instead of as a single grid arrangement of the plots.
OUT.List	Deprecated. Use list.out
..., color.panel, colors, min, max, assay, slot, adjustment, data.out, do.hover	additional parameters passed to dittoDimPlot. All parameters of dittoDimPlot can be utilized and adjusted except for cells.use, main, and legend.show which are handled with alternative methods here. A few suggestions: reduction.use for setting which dimensionality reduction space to use. xlab and ylab can be set to NULL to remove the axes labels and provide extra room for the data. size can be used to adjust the size of the dots.

Details

This function generates separate dittoDimPlots that show the same target data, but each for distinct cells.

How cells are separated into distinct plots is controlled with the vary.cells.meta parameter. Individual dittoDimPlots are created for all levels of var.cells.meta groupings given to the vary.cells.levels input (default = all).

The function then appends a plot containing all cell/samples when show.allcells.plot = TRUE, with title of this plot controlled by allcells.main, as well as as single legend when show.legend.single = TRUE.

By default, these dittoDimPlots are output in a grid (default) with ncol columns and nrow rows, Alternatively, if list.out is set to TRUE, they are returned as a list. In the list, the VaryCell plots will be named by the levels of vary.cells.meta that they contain, and the optional allcells plot and single legend will be named "allcells" and "legend", respectively.

Either continuous or discrete var data can be displayed.

• For continuous data, the range of potential values is calculated at the start, and set, so that colors represent the same value across all plots.

• For discrete data, colors used in each plot are adjusted so that colors represent the same groupings across all plots.

Value

A set of dittoDimPlots either arranged into a grid (default), or output as a list.

Author(s)

Daniel Bunis

See Also

multi_dittoDimPlot for an alternate dittoDimPlot multi-plotter where vars are varied across plots rather than cells/samples

dittoDimPlot for the base dittoDimPlot plotting function and details on all accepted inputs.

Examples

example(importDittoBulk, echo = FALSE)

# This function can be used to quickly scan for differences in expression
#   within or across clusters/cell types.
multi_dittoDimPlotVaryCells(myRNA, "gene1", vary.cells.meta = "clustering")

# Output as list instead
multi_dittoDimPlotVaryCells(myRNA, "gene1", vary.cells.meta = "clustering",
    list.out = TRUE)

# This function is also great for generating separate plots of each individual
#   grouping of a tsne/PCA/umap. This can be useful to check for dispersion
#   of groups that might otherwise be hidden behind other cells/samples.
#   The effect is similar to faceting, but: all distinct plots are treated
#   separately rather than being just a part of the whole, and with portrayal
#   of all cells/samples in an additional plot by default.
#
#   To do so, set 'var' and 'vary.cells.meta' the same.
multi_dittoDimPlotVaryCells(myRNA, "clustering", vary.cells.meta = "clustering")

# The function can also be used to quickly visualize how separate clustering
#   resolutions match up to each other, or perhaps how certain conditions of
#   cells disperse across clusters.
# (For an alternative method of viewing, and easily quantifying, how discrete
#   conditions of cells disperse across clusters, see '?dittoBarPlot')
multi_dittoDimPlotVaryCells(myRNA, "groups", vary.cells.meta = "clustering")

dittoSeq documentation built on April 17, 2021, 6:01 p.m.