dittoPlotVarsAcrossGroups: Generates a dittoPlot where data points are genes/metadata...

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

Generates a dittoPlot where data points are genes/metadata summaries, per groups, instead of individual values per cells/samples.

Description

Generates a dittoPlot where data points are genes/metadata summaries, per groups, instead of individual values per cells/samples.

Usage

dittoPlotVarsAcrossGroups(
  object,
  vars,
  group.by,
  color.by = group.by,
  split.by = NULL,
  summary.fxn = mean,
  cells.use = NULL,
  plots = c("vlnplot", "jitter"),
  assay = .default_assay(object),
  slot = .default_slot(object),
  adjustment = "z-score",
  swap.rownames = NULL,
  do.hover = FALSE,
  main = NULL,
  sub = NULL,
  ylab = "make",
  y.breaks = NULL,
  min = NA,
  max = NA,
  xlab = group.by,
  x.labels = NULL,
  x.labels.rotate = NA,
  x.reorder = NULL,
  groupings.drop.unused = TRUE,
  color.panel = dittoColors(),
  colors = c(seq_along(color.panel)),
  theme = theme_classic(),
  jitter.size = 1,
  jitter.width = 0.2,
  jitter.color = "black",
  jitter.position.dodge = boxplot.position.dodge,
  do.raster = FALSE,
  raster.dpi = 300,
  boxplot.width = 0.2,
  boxplot.color = "black",
  boxplot.show.outliers = NA,
  boxplot.outlier.size = 1.5,
  boxplot.fill = TRUE,
  boxplot.position.dodge = vlnplot.width,
  boxplot.lineweight = 1,
  vlnplot.lineweight = 1,
  vlnplot.width = 1,
  vlnplot.scaling = "area",
  vlnplot.quantiles = NULL,
  ridgeplot.lineweight = 1,
  ridgeplot.scale = 1.25,
  ridgeplot.ymax.expansion = NA,
  ridgeplot.shape = c("smooth", "hist"),
  ridgeplot.bins = 30,
  ridgeplot.binwidth = NULL,
  add.line = NULL,
  line.linetype = "dashed",
  line.color = "black",
  split.nrow = NULL,
  split.ncol = NULL,
  split.adjust = list(),
  legend.show = TRUE,
  legend.title = NULL,
  data.out = FALSE
)

Arguments

object	A Seurat, SingleCellExperiment, or SummarizedExperiment object.
vars	String vector (example: c("gene1","gene2","gene3")) which selects which variables, typically genes, to extract from the object, summarize across groups, and add to the plot
group.by	String representing the name of a metadata to use for separating the cells/samples into discrete groups.
color.by	String representing the name of a metadata to use for setting fills. Great for highlighting subgroups when wanted, but it defaults to group.by so this input can be skipped otherwise. Affects boxplot, vlnplot, and ridgeplot fills.
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
summary.fxn	A function which sets how variables' data will be summarized across the groups. Default is mean, which will take the average value, but any function can be used as long as it takes in a numeric vector and returns a single numeric value. Alternative examples: median, max, or function(x) mean(x!=0).
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.
plots	String vector which sets the types of plots to include: possibilities = "jitter", "boxplot", "vlnplot", "ridgeplot". Order matters: c("vlnplot", "boxplot", "jitter") will put a violin plot in the back, boxplot in the middle, and then individual dots in the front. See details section for more info.
assay, slot	single strings or integers (SCEs and SEs) or an optionally named vector of such values that set which expression data to use. See GeneTargeting for specifics and examples – Seurat and SingleCellExperiment objects deal with these differently, and functionality additions in dittoSeq have led to some minimal divergence from the native methodologies.
adjustment	When plotting gene expression (or antibody, or other forms of counts data), should that data be used directly or should it be adjusted to be "z-score": DEFAULT, centered and scaled to produce a relative-to-mean z-score representation NULL: no adjustment, the normal method for all other ditto expression plotting functions "relative.to.max": divided by the maximum expression value to give percent of max values between [0,1]
swap.rownames	optionally named string or string vector. For SummarizedExperiment or SingleCellExperiment objects, its value(s) specifies the column name of rowData(object) to be used to identify features instead of rownames(object). When targeting multiple modalities (alternative experiments), names can be used to specify which level / alternative experiment (use 'main' for the top-level) individual values should be used for. See GeneTargeting for more specifics and examples.
do.hover	Logical. Default = FALSE. If set to TRUE (and if there is a "jitter" in plots): the object will be converted to a plotly object in which underlying data about individual points will be displayed when you hover your cursor over them.
main	String which sets the plot title.
sub	String which sets the plot subtitle.
ylab	String which sets the y axis label. Default = a combination of the name of the summary function + adjustment + "expression". Set to NULL to remove.
y.breaks	Numeric vector, a set of breaks that should be used as major grid lines. c(break1,break2,break3,etc.).
min, max	Scalars which control the zoom of the plot. These inputs set the minimum / maximum values of the data to display. Default = NA, which allows ggplot to set these limits based on the range of all data being shown.
xlab	String which sets the grouping-axis label (=x-axis for box and violin plots, y-axis for ridgeplots). Set to NULL to remove.
x.labels	String vector, c("label1","label2","label3",...) which overrides the names of groupings.
x.labels.rotate	Logical which sets whether the labels should be rotated. Default: TRUE for violin and box plots, but FALSE for ridgeplots.
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.
groupings.drop.unused	Logical. TRUE by default. If group.by-data is a factor, factor levels are retained for ordering purposes, but some level(s) can end up with zero cells left after cells.use subsetting. By default, we remove them, but you can set this input to FALSE to keep them.
color.panel	String vector which sets the colors to draw from for plot fills.
colors	Integer vector, the indexes / order, of colors from color.panel to actually use. (Provides an alternative to directly modifying color.panel.)
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.
jitter.size	Scalar which sets the size of the jitter shapes.
jitter.width	Scalar that sets the width/spread of the jitter in the x direction. Ignored in ridgeplots. Note for when color.by is used to split x-axis groupings into additional bins: ggplot does not shrink jitter widths accordingly, so be sure to do so yourself! Ideally, needs to be 0.5/num_subgroups.
jitter.color	String which sets the color of the jitter shapes
jitter.position.dodge	Scalar which adjusts the relative distance between jitter widths when multiple subgroups exist per group.by grouping (a.k.a. when group.by and color.by are not equal). Similar to boxplot.position.dodge input & defaults to the value of that input so that BOTH will actually be adjusted when only, say, boxplot.position.dodge = 0.3 is given.
do.raster	Logical. When set to TRUE, rasterizes the jitter plot layer, changing it from individually encoded points to a flattened set of pixels. This can be useful for editing in external programs (e.g. Illustrator) when there are many thousands of data points.
raster.dpi	Number indicating dots/pixels per inch (dpi) to use for rasterization. Default = 300.
boxplot.width	Scalar which sets the width/spread of the boxplot in the x direction
boxplot.color	String which sets the color of the lines of the boxplot
boxplot.show.outliers	Logical, whether outliers should by including in the boxplot. Default is FALSE when there is a jitter plotted, TRUE if there is no jitter.
boxplot.outlier.size	Scalar which adjusts the size of points used to mark outliers
boxplot.fill	Logical, whether the boxplot should be filled in or not. Known bug: when boxplot fill is turned off, outliers do not render.
boxplot.position.dodge	Scalar which adjusts the relative distance between boxplots when multiple are drawn per grouping (a.k.a. when group.by and color.by are not equal). By default, this input actually controls the value of jitter.position.dodge unless the jitter version is provided separately.
boxplot.lineweight	Scalar which adjusts the thickness of boxplot lines.
vlnplot.lineweight	Scalar which sets the thickness of the line that outlines the violin plots.
vlnplot.width	Scalar which sets the width/spread of violin plots in the x direction
vlnplot.scaling	String which sets how the widths of the of violin plots are set in relation to each other. Options are "area", "count", and "width". If the default is not right for your data, I recommend trying "width". For an explanation of each, see geom_violin.
vlnplot.quantiles	Single number or numeric vector of values in [0,1] naming quantiles at which to draw a horizontal line within each violin plot. Example: c(0.1, 0.5, 0.9)
ridgeplot.lineweight	Scalar which sets the thickness of the ridgeplot outline.
ridgeplot.scale	Scalar which sets the distance/overlap between ridgeplots. A value of 1 means the tallest density curve just touches the baseline of the next higher one. Higher numbers lead to greater overlap. Default = 1.25
ridgeplot.ymax.expansion	Scalar which adjusts the minimal space between the top-most grouping and the top of the plot in order to ensure that the curve is not cut off by the plotting grid. The larger the value, the greater the space requested. When left as NA, dittoSeq will attempt to determine an ideal value itself based on the number of groups & linear interpolation between these goal posts: 0.6 when g<=3, 0.1 when g==12, and 0.05 when g>=34, where g is the number of groups.
ridgeplot.shape	Either "smooth" or "hist", sets whether ridges will be smoothed (the typical, and default) versus rectangular like a histogram. (Note: as of the time shape "hist" was added, combination of jittered points is not supported by the stat_binline that dittoSeq relies on.)
ridgeplot.bins	Integer which sets how many chunks to break the x-axis into when ridgeplot.shape = "hist". Overridden by ridgeplot.binwidth when that input is provided.
ridgeplot.binwidth	Integer which sets the width of chunks to break the x-axis into when ridgeplot.shape = "hist". Takes precedence over ridgeplot.bins when provided.
add.line	numeric value(s) where one or multiple line(s) should be added
line.linetype	String which sets the type of line for add.line. Defaults to "dashed", but any ggplot linetype will work.
line.color	String that sets the color(s) of the add.line line(s)
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.
legend.show	Logical. Whether the legend should be displayed. Default = TRUE.
legend.title	String or NULL, sets the title for the main legend which includes colors and data representations.
data.out	Logical. When set to TRUE, changes the output, from the plot alone, to a list containing the plot (p) and data (data).

Details

Generally, this function will output a dittoPlot where each data point represents a gene (or metadata) rather than a cell/sample. Values are the summary (mean by default) of the values for each gene or metadata requested with vars, within each group set by group.by.

To start with, the data for each element of vars is obtained. When elements are genes/features, assay and slot are utilized to determine which expression data to use, and adjustment determines if and how the expression data might be adjusted. By default, a z-score adjustment is applied to all gene/feature vars. Note that this adjustment is applied before cells/samples subsetting.

x-axis groupings are then determined using group.by, and data for each variable is summarized using the summary.fxn.

Finally, data is plotted with the data representation types in plots.

Value

a ggplot object

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

Alternatively when do.hover = TRUE, a plotly converted version of the plot where additional data will be displayed when the cursor is hovered over jitter points.

Plot Customization

The plots argument determines the types of data representation that will be generated, as well as their order from back to front. Options are "jitter", "boxplot", "vlnplot", and "ridgeplot".

Each plot type has specific associated options which are controlled by variables that start with their associated string. For example, all jitter adjustments start with "jitter.", such as jitter.size and jitter.width.

Inclusion of "ridgeplot" overrides "boxplot" and "vlnplot" presence and changes the plot to be horizontal.

Additionally:

• Colors can be adjusted with color.panel.

• Subgroupings: color.by can be utilized to split major group.by groupings into subgroups. When this is done in y-axis plotting, dittoSeq automatically ensures the centers of all geoms will align, but users will need to manually adjust jitter.width to less than 0.5/num_subgroups to avoid overlaps. There are also three inputs through which one can use to control geom-center placement, but the easiest way to do all at once so is to just adjust vlnplot.width! The other two: boxplot.position.dodge, and jitter.position.dodge.

• Line(s) can be added at single or multiple value(s) by providing these values to add.line. Linetype and color are set with line.linetype, which is "dashed" by default, and line.color, which is "black" by default.

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

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

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

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

• Shapes used in conjunction with shape.by can be adjusted with shape.panel.

Author(s)

Daniel Bunis

See Also

dittoPlot and multi_dittoPlot for plotting of single or mutliple expression and metadata vars, each as separate plots, on a per cell/sample basis.

dittoDotPlot for an alternative representation of per-group summaries of multiple vars where all vars are displayed separately, but still in a single plot.

Examples

example(importDittoBulk, echo = FALSE)

# Pick a set of genes
genes <- getGenes(myRNA)[1:30]

dittoPlotVarsAcrossGroups(
    myRNA, genes, group.by = "timepoint")

# Color can be controlled separately from grouping with 'color.by'
#   Just note: all groupings must map to a single color.
dittoPlotVarsAcrossGroups(myRNA, genes, "timepoint",
    color.by = "conditions")

# To change it to have the violin plot in the back, a jitter on
#  top of that, and a white boxplot with no fill in front:
dittoPlotVarsAcrossGroups(myRNA, genes, "timepoint",
    plots = c("vlnplot","jitter","boxplot"),
    boxplot.color = "white",
    boxplot.fill = FALSE)

## Data can be summarized in other ways by changing the summary.fxn input.
#  median
dittoPlotVarsAcrossGroups(myRNA, genes, "timepoint",
    summary.fxn = median,
    adjustment = NULL)
#  Percent non-zero expression ( = boring for this fake data)
percent <- function(x) {sum(x!=0)/length(x)}
dittoPlotVarsAcrossGroups(myRNA, genes, "timepoint",
    summary.fxn = percent,
    adjustment = NULL)

# To investigate the identities of outlier genes, we can turn on hovering
# (if the plotly package is available)
if (requireNamespace("plotly", quietly = TRUE)) {
    dittoPlotVarsAcrossGroups(
        myRNA, genes, "timepoint",
        do.hover = TRUE)
}

dtm2451/dittoSeq documentation built on April 3, 2024, 9:11 p.m.