plotExpression: Plot expression values for all cells

In LTLA/scater: Single-Cell Analysis Toolkit for Gene Expression Data in R

Plot expression values for all cells

Description

Plot expression values for a set of features (e.g. genes or transcripts) in a SingleExperiment object, against a continuous or categorical covariate for all cells.

Usage

plotExpression(
  object,
  features,
  x = NULL,
  exprs_values = "logcounts",
  log2_values = FALSE,
  colour_by = color_by,
  shape_by = NULL,
  size_by = NULL,
  order_by = NULL,
  by_exprs_values = exprs_values,
  xlab = NULL,
  feature_colours = feature_colors,
  one_facet = TRUE,
  ncol = 2,
  scales = "fixed",
  other_fields = list(),
  swap_rownames = NULL,
  color_by = NULL,
  feature_colors = TRUE,
  point_fun = NULL,
  assay.type = exprs_values,
  scattermore = FALSE,
  bins = NULL,
  summary_fun = "sum",
  hex = FALSE,
  by.assay.type = by_exprs_values,
  ...
)

Arguments

object	A SingleCellExperiment object containing expression values and other metadata.
features	A character vector or a list specifying the features to plot. If a list is supplied, each entry of the list can be a string, an AsIs-wrapped vector or a data.frame - see ?retrieveCellInfo.
x	Specification of a column metadata field or a feature to show on the x-axis, see the by argument in ?retrieveCellInfo for possible values.
exprs_values	Alias to assay.type.
log2_values	Logical scalar, specifying whether the expression values be transformed to the log2-scale for plotting (with an offset of 1 to avoid logging zeroes).
colour_by	Specification of a column metadata field or a feature to colour by, see the by argument in ?retrieveCellInfo for possible values.
shape_by	Specification of a column metadata field or a feature to shape by, see the by argument in ?retrieveCellInfo for possible values.
size_by	Specification of a column metadata field or a feature to size by, see the by argument in ?retrieveCellInfo for possible values.
order_by	Specification of a column metadata field or a feature to order points by, see the by argument in ?retrieveCellInfo for possible values.
by_exprs_values	Alias to by.assay.type.
xlab	String specifying the label for x-axis. If NULL (default), x will be used as the x-axis label.
feature_colours	Logical scalar indicating whether violins should be coloured by feature when x and colour_by are not specified and one_facet=TRUE.
one_facet	Logical scalar indicating whether grouped violin plots for multiple features should be put onto one facet. Only relevant when x=NULL.
ncol	Integer scalar, specifying the number of columns to be used for the panels of a multi-facet plot.
scales	String indicating whether should multi-facet scales be fixed ("fixed"), free ("free"), or free in one dimension ("free_x", "free_y"). Passed to the scales argument in the facet_wrap when multiple facets are generated.
other_fields	Additional cell-based fields to include in the data.frame, see ?"scater-plot-args" for details.
swap_rownames	Column name of rowData(object) to be used to identify features instead of rownames(object) when labelling plot elements.
color_by	Alias to colour_by.
feature_colors	Alias to feature_colours.
point_fun	Function used to create a geom that shows individual cells. Should take ... args and return a ggplot2 geom. For example, point_fun=function(...) geom_quasirandom(...).
assay.type	A string or integer scalar specifying which assay in assays(object) to obtain expression values from. Also the alias assay.type is accepted.
scattermore	Logical, whether to use the scattermore package to greatly speed up plotting a large number of cells. Use point_size = 0 for the most performance gain.
bins	Number of bins, can be different in x and y, to bin and summarize the points and their values, to avoid overplotting. If NULL (default), then the points are plotted without binning. Only used when both x and y are numeric.
summary_fun	Function to summarize the feature value of each point (e.g. gene expression of each cell) when the points binned, defaults to sum. Can be either the name of the function or the function itself.
hex	Logical, whether to use geom_hex.
by.assay.type	A string or integer scalar specifying which assay to obtain expression values from, for use in point aesthetics - see the assay.type argument in ?retrieveCellInfo. Also the alias by.assay.type is accepted.
...	Additional arguments for visualization, see ?"scater-plot-args" for details.

Details

This function plots expression values for one or more features. If x is not specified, a violin plot will be generated of expression values. If x is categorical, a grouped violin plot will be generated, with one violin for each level of x. If x is continuous, a scatter plot will be generated.

If multiple features are requested and x is not specified and one_facet=TRUE, a grouped violin plot will be generated with one violin per feature. This will be coloured by feature if colour_by=NULL and feature_colours=TRUE, to yield a more aesthetically pleasing plot. Otherwise, if x is specified or one_facet=FALSE, a multi-panel plot will be generated where each panel corresponds to a feature. Each panel will be a scatter plot or (grouped) violin plot, depending on the nature of x.

Note that this assumes that the expression values are numeric. If not, and x is continuous, horizontal violin plots will be generated. If x is missing or categorical, rectangule plots will be generated where the area of a rectangle is proportional to the number of points for a combination of factors.

Value

A ggplot object.

Note

Arguments shape_by and size_by are ignored when scattermore = TRUE. Using scattermore is only recommended for very large datasets to speed up plotting. Small point size is also recommended. For larger point size, the point shape may be distorted. Also, when scattermore = TRUE, the point_size argument works differently.

Author(s)

Davis McCarthy, with modifications by Aaron Lun

Examples

example_sce <- mockSCE()
example_sce <- logNormCounts(example_sce)

## default plot
plotExpression(example_sce, rownames(example_sce)[1:15])

## plot expression against an x-axis value
plotExpression(example_sce, c("Gene_0001", "Gene_0004"),
    x="Mutation_Status")
plotExpression(example_sce, c("Gene_0001", "Gene_0004"),
    x="Gene_0002")

## add visual options
plotExpression(example_sce, rownames(example_sce)[1:6],
    colour_by = "Mutation_Status")
plotExpression(example_sce, rownames(example_sce)[1:6],
    colour_by = "Mutation_Status", shape_by = "Treatment",
    size_by = "Gene_0010")

## use boxplot as well as violin plot
plotExpression(example_sce, rownames(example_sce)[1:6],
    show_boxplot = TRUE, show_violin = FALSE)

## plot expression against expression values for Gene_0004
plotExpression(example_sce, rownames(example_sce)[1:4],
    "Gene_0004", show_smooth = TRUE)

# Use scattermore
plotExpression(example_sce, "Gene_0001", x = "Gene_0100", scattermore = TRUE,
    point_size = 2)
# Bin to show point density
plotExpression(example_sce, "Gene_0001", x = "Gene_0100", bins = 10)
# Bin to summarize values (default is sum but can be changed with summary_fun)
plotExpression(example_sce, "Gene_0001", x = "Gene_0100", bins = 10,
    colour_by = "Gene_0002", summary_fun = "mean")

LTLA/scater documentation built on July 21, 2024, 5:43 p.m.