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

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/multi_plotters.R

Description

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

Usage

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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.

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

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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.