dittoDimPlot: Shows data overlayed on a tsne, pca, or similar type of plot

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

Shows data overlayed on a tsne, pca, or similar type of plot

Description

Shows data overlayed on a tsne, pca, or similar type of plot

Usage

dittoDimPlot(
  object,
  var,
  reduction.use = .default_reduction(object),
  size = 1,
  opacity = 1,
  dim.1 = 1,
  dim.2 = 2,
  cells.use = NULL,
  shape.by = NULL,
  split.by = NULL,
  split.adjust = list(),
  extra.vars = NULL,
  multivar.split.dir = c("col", "row"),
  show.others = TRUE,
  split.show.all.others = TRUE,
  split.nrow = NULL,
  split.ncol = NULL,
  assay = .default_assay(object),
  slot = .default_slot(object),
  adjustment = NULL,
  swap.rownames = NULL,
  color.panel = dittoColors(),
  colors = seq_along(color.panel),
  shape.panel = c(16, 15, 17, 23, 25, 8),
  min.color = "#F0E442",
  max.color = "#0072B2",
  min = NA,
  max = NA,
  order = c("unordered", "increasing", "decreasing", "randomize"),
  main = "make",
  sub = NULL,
  xlab = "make",
  ylab = "make",
  rename.var.groups = NULL,
  rename.shape.groups = NULL,
  theme = theme_bw(),
  show.axes.numbers = TRUE,
  show.grid.lines = if (is.character(reduction.use)) {
     !grepl("umap|tsne",
    tolower(reduction.use))
 } else {
     TRUE
 },
  do.letter = FALSE,
  do.ellipse = FALSE,
  do.label = FALSE,
  labels.size = 5,
  labels.highlight = TRUE,
  labels.repel = TRUE,
  labels.split.by = split.by,
  labels.repel.adjust = list(),
  do.hover = FALSE,
  hover.data = var,
  hover.assay = .default_assay(object),
  hover.slot = .default_slot(object),
  hover.adjustment = NULL,
  add.trajectory.lineages = NULL,
  add.trajectory.curves = NULL,
  trajectory.cluster.meta,
  trajectory.arrow.size = 0.15,
  do.contour = FALSE,
  contour.color = "black",
  contour.linetype = 1,
  legend.show = TRUE,
  legend.size = 5,
  legend.title = "make",
  legend.breaks = waiver(),
  legend.breaks.labels = waiver(),
  shape.legend.size = 5,
  shape.legend.title = shape.by,
  do.raster = FALSE,
  raster.dpi = 300,
  data.out = 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 for each cell/sample. Discrete or continuous data both work. Alternatively, a string vector naming multiple genes or metadata, OR a vector of the same length as there are cells/samples in the object which provides per-cell data directly.
reduction.use	String, such as "pca", "tsne", "umap", or "PCA", etc, which is the name of a dimensionality reduction slot within the object, and which sets what dimensionality reduction space within the object to use. Default = the first dimensionality reduction slot inside the object with "umap", "tsne", or "pca" within its name, (priority: UMAP > t-SNE > PCA) or the first dimensionality reduction slot if none of those exist. Alternatively, a matrix (or data.frame) containing the dimensionality reduction embeddings themselves. The matrix should have as many rows as there are cells/samples in the object. Note that dim.1 and dim.2 will still be used to select which columns to pull from, and column names will serve as the default xlab & ylab.
size	Number which sets the size of data points. Default = 1.
opacity	Number between 0 and 1. Great for when you have MANY overlapping points, this sets how solid the points should be: 1 = not see-through at all. 0 = invisible. Default = 1. (In terms of typical ggplot variables, = alpha)
dim.1	The component number to use on the x-axis. Default = 1
dim.2	The component number to use on the y-axis. Default = 2
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.
shape.by	Variable for setting the shape of cells/samples in the plot. Note: must be discrete. Can be the name of a gene or meta-data. Alternatively, can be "ident" for clusters of a Seurat object. Alternatively, can be a numeric of length equal to the total number of cells/samples in object. Note: shapes can be harder to see, and to process mentally, than colors. Even as a color blind person myself writing this code, I recommend use of colors for variables with many discrete values.
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
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.
extra.vars	String vector providing names of any extra metadata to be stashed in the dataframe supplied to ggplot(data). Useful for making custom splitting/faceting or other additional alterations after dittoSeq plot generation.
multivar.split.dir	"row" or "col", sets the direction of faceting used for 'var' values when var is given multiple genes or metadata, and when split.by is used to provide additional data to facet by.
show.others	Logical. Whether other cells should be shown in the background in light gray. Default = TRUE.
split.show.all.others	Logical which sets whether gray "others" cells of facets should include all cells of other facets (TRUE) versus just cells left out by cell.use (FALSE).
split.nrow, split.ncol	Integers which set the dimensions of faceting/splitting when a single metadata is given to split.by.
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 / feature expression, should that data be used directly (default) or should it be adjusted to be "z-score": scaled with the scale() function to produce a relative-to-mean z-score representation "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.
color.panel	String vector which sets the colors to draw from. dittoColors() by default, see dittoColors for contents.
colors	Integer vector, the indexes / order, of colors from color.panel to actually use. Useful for quickly swapping the colors of nearby clusters.
shape.panel	Vector of integers corresponding to ggplot shapes which sets what shapes to use. When discrete groupings are supplied by shape.by, this sets the panel of shapes. When nothing is supplied to shape.by, only the first value is used. Default is a set of 6, c(16,15,17,23,25,8), the first being a simple, solid, circle. Note: Unfortunately, shapes can be hard to see when points are on top of each other & they are more slowly processed by the brain. For these reasons, even as a color blind person myself writing this code, I recommend use of colors for variables with many discrete values.
min.color	color for lowest values of var/min. Default = yellow
max.color	color for highest values of var/max. Default = blue
min	Number which sets the value associated with the minimum color.
max	Number which sets the value associated with the maximum color.
order	String. If the data should be plotted based on the order of the color data, sets whether to plot (from back to front) in "increasing", "decreasing", "randomize" order. If left as "unordered", plot order is simply based on the order of cells within the object.
main	String, sets the plot title. Default title is automatically generated if not given a specific value. To remove, set to NULL.
sub	String, sets the plot subtitle
xlab, ylab	Strings which set the labels for the axes. Default labels are generated if you do not give this a specific value. To remove, set to NULL.
rename.var.groups	String vector which sets new names for the identities of var groups.
rename.shape.groups	String vector which sets new names for the identities of shape.by groups.
theme	A ggplot theme which will be applied before dittoSeq adjustments. Default = theme_bw(). See https://ggplot2.tidyverse.org/reference/ggtheme.html for other options and ideas.
show.axes.numbers	Logical which controls whether the axes values should be displayed.
show.grid.lines	Logical which sets whether gridlines of the plot should be shown. They are removed when set to FALSE. Default = FALSE for umap and tsne reduction.use, TRUE otherwise.
do.letter	Logical which sets whether letters should be added on top of the colored dots. For extended colorblindness compatibility. NOTE: do.letter is ignored if do.hover = TRUE or shape.by is provided a metadata because lettering is incompatible with plotly and with changing the dots' to be different shapes.
do.ellipse	Logical. Whether the groups should be surrounded by median-centered ellipses.
do.label	Logical. Whether to add text labels near the center (median) of clusters for grouping vars.
labels.size	Size of the the labels text
labels.highlight	Logical. Whether the labels should have a box behind them
labels.repel	Logical, that sets whether the labels' placements will be adjusted with ggrepel to avoid intersections between labels and plot bounds. TRUE by default.
labels.split.by	String of one or two metadata names which controls the facet-split calculations for label placements. Defaults to split.by, so generally there is no need to adjust this except when you are utilizing the extra.vars input to achieve manual faceting control.
labels.repel.adjust	A named list which allows extra parameters to be pushed through to ggrepel function calls. List elements should be valid inputs to the geom_label_repel by default, or geom_text_repel when labels.highlight = FALSE.
do.hover	Logical which controls whether the output will be converted to a plotly object so that data about individual points will be displayed when you hover your cursor over them. hover.data argument is used to determine what data to use.
hover.data	String vector of gene and metadata names, example: c("meta1","gene1","meta2") which determines what data to show on hover when do.hover is set to TRUE.
hover.assay, hover.slot, hover.adjustment	Similar to the non-hover versions of these inputs, when showing expression data upon hover, these set what data will be shown.
add.trajectory.lineages	List of vectors representing trajectory paths, each from start-cluster to end-cluster, where vector contents are the names of clusters provided in the trajectory.cluster.meta input. If the slingshot package was used for trajectory analysis, you can provide add.trajectory.lineages = slingLineages('object').
add.trajectory.curves	List of matrices, each representing coordinates for a trajectory path, from start to end, where matrix columns represent x (dim.1) and y (dim.2) coordinates of the paths. Alternatively, a list of lists(/princurve objects) can be provided. Thus, if the slingshot package was used for trajectory analysis, you can provide add.trajectory.curves = slingCurves('object')
trajectory.cluster.meta	String name of metadata containing the clusters that were used for generating trajectories. Required when plotting trajectories using the add.trajectory.lineages method. Names of clusters inside the metadata should be the same as the contents of add.trajectory.lineages vectors.
trajectory.arrow.size	Number representing the size of trajectory arrows, in inches. Default = 0.15.
do.contour	Logical. Whether density-based contours should be displayed.
contour.color	String that sets the color(s) of the do.contour contours.
contour.linetype	String or numeric which sets the type of line used for do.contour contours. Defaults to "solid", but see linetype for other options.
legend.show	Logical. Whether the legend should be displayed. Default = TRUE.
legend.size	Number representing the size at which color legend shapes should be plotted (for discrete variable plotting) in the color legend. Default = 5. *Enlarging the colors legend is incredibly helpful for making colors more distinguishable by color blind individuals.
legend.title	String which sets the title for the color legend. Default = NULL normally, but var when a shape legend will also be shown.
legend.breaks	Numeric vector which sets the discrete values to show in the color-scale legend for continuous data.
legend.breaks.labels	String vector, with same length as legend.breaks, which renames what's displayed next to the tick marks of the color-scale.
shape.legend.size	Number representing the size at which shapes should be plotted in the shape legend.
shape.legend.title	String which sets the title of the shapes legend. Default is shape.by
do.raster	Logical. When set to TRUE, rasterizes the internal plot area. Useful for editing in external programs (e.g. Illustrator).
raster.dpi	Number indicating dpi to use for rasterization. Default = 300.
data.out	Logical. When set to TRUE, changes the output, from the plot alone, to a list containing the plot ("p"), a data.frame containing the underlying data for target cells ("Target_data"), and a data.frame containing the underlying data for non-target cells ("Others_data").

Details

The function creates a dataframe containing the metadata or expression data associated with the given var (or if a vector of data is provided directly, it just uses that), plus X and Y coordinates data determined by the reduction.use and dim.1 (x-axis) and dim.2 (y-axis) inputs. Any extra data requested with shape.by, split.by or extra.var is added as well. For expression/counts data, assay, slot, and adjustment inputs can be used to change which data is used, and if it should be adjusted in some way.

Next, if a set of cells or samples to use is indicated with the cells.use input, then the dataframe is split into Target_data and Others_data based on subsetting by the target cells/samples.

Finally, a scatter plot is then created using these dataframes where non-target cells will be displayed in gray if show.others=TRUE, and target cell data is displayed on top, colored based on the var-associated data, and with shapes determined by the shape.by-associated data. If split.by was used, the plot will be split into a matrix of panels based on the associated groupings.

Value

A ggplot or plotly object where colored dots (or other shapes) are overlayed onto a tSNE, PCA, UMAP, ..., plot of choice.

Alternatively, if data.out=TRUE, a list containing three slots is output: the plot (named 'p'), a data.table containing the underlying data for target cells (named 'Target_data'), and a data.table containing the underlying data for non-target cells (named 'Others_data').

Alternatively, if do.hover is set to TRUE, the plot is coverted from ggplot to plotly & cell/sample information, determined by the hover.data input, is retrieved, added to the dataframe, and displayed upon hovering the cursor over the plot.

Many characteristics of the plot can be adjusted using discrete inputs

• size and opacity can be used to adjust the size and transparency of the data points.

• Color can be adjusted with color.panel and/or colors for discrete data, or min, max, min.color, and max.color for continuous data.

• Shapes can be adjusted with shape.panel.

• Color and shape labels can be changed using rename.var.groups and rename.shape.groups.

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

• Legends can also be adjusted in other ways, using variables that all start with "legend." for easy tab-completion lookup.

Additional Features

Many other tweaks and features can be added as well. Each is accessible through 'tab' autocompletion starting with "do."--- or "add."---, and if additional inputs are involved in implementing or tweaking these, the associated inputs will start with the "---.":

• If do.label is set to TRUE, labels will be added based on median centers of the discrete var-data groupings. The size of the text in the labels can be adjusted using the labels.size input. By default labels will repel eachother and the bounds of the plot, and labels will be highlighted with a white background. Either of these can be turned off by setting labels.repel = FALSE or labels.highlight = FALSE,

• If do.ellipse is set to TRUE, ellipses will be added to highlight distinct var-data groups' positions based on median positions of their cell/sample components.

• If do.contour is provided, density gradiant contour lines will be overlaid with color and linetype adjustable via contour.color and contour.linetype.

• If add.trajectory.lineages is provided a list of vectors (each vector being cluster names from start-cluster-name to end-cluster-name), and a metadata name pointing to the relevant clustering information is provided to trajectory.cluster.meta, then median centers of the clusters will be calculated and arrows will be overlayed to show trajectory inference paths in the current dimmenionality reduction space.

• If add.trajectory.curves is provided a list of matrices (each matrix containing x, y coordinates from start to end), paths and arrows will be overlayed to show trajectory inference curves in the current dimmenionality reduction space. Arrow size is controlled with the trajectory.arrow.size input.

Author(s)

Daniel Bunis and Jared Andrews

See Also

getGenes and getMetas to see what the var, split.by, etc. options are of an object.

getReductions to see what the reduction.use options are of an object.

importDittoBulk for how to create a SingleCellExperiment object from bulk seq data that dittoSeq functions can use & addDimReduction for how to specifically add calculated dimensionality reductions that dittoDimPlot can utilize.

dittoScatterPlot for showing very similar data representations, but where genes or metadata are wanted as the axes.

dittoDimHex and dittoScatterHex for showing very similar data representations, but where nearby cells are summarized together in hexagonal bins.

dittoPlot for an alternative continuous data display method where data broken into discrete groupings is shown on a y- (or x-) axis.

dittoBarPlot for an alternative discrete data display and quantification method.

Examples

example(importDittoBulk, echo = FALSE)
myRNA

# Display discrete data:
dittoDimPlot(myRNA, "clustering")
# Display continuous data:
dittoDimPlot(myRNA, "gene1")

# You can also plot multiple sets of continuous data:
dittoDimPlot(myRNA, c("gene1", "gene2"))
# (See ?multi_dittoDimPlot if you would like to have wholy separate
# plots/scales/legends for each set.)

# To show currently set clustering for seurat objects, you can use "ident".
# To change the dimensional reduction type, use 'reduction.use'.
dittoDimPlot(myRNA, "clustering",
    reduction.use = "pca",
    dim.1 = 3,
    dim.2 = 4)

# Subset to certain cells with cells.use
dittoDimPlot(myRNA, "clustering",
    cells.us = !myRNA$SNP)

# Data can also be split in other ways with 'shape.by' or 'split.by'
dittoDimPlot(myRNA, "gene1",
    shape.by = "clustering",
    split.by = "SNP") # single split.by element
dittoDimPlot(myRNA, "gene1",
    split.by = c("groups","SNP")) # row and col split.by elements

# Modify the look with intuitive inputs
dittoDimPlot(myRNA, "clustering",
    size = 2, opacity = 0.7, show.axes.numbers = FALSE,
    ylab = NULL, xlab = "tSNE",
    main = "Plot Title",
    sub = "subtitle",
    legend.title = "clustering")

# MANY addtional tweaks are possible.
# Also, many extra features are easy to add as well:
dittoDimPlot(myRNA, "clustering",
    do.label = TRUE, do.ellipse = TRUE)
dittoDimPlot(myRNA, "clustering",
    do.label = TRUE, labels.highlight = FALSE, labels.size = 8)
if (requireNamespace("plotly", quietly = TRUE)) {
    dittoDimPlot(myRNA, "gene1", do.hover = TRUE,
        hover.data = c("gene2", "clustering", "timepoint"))
}
dittoDimPlot(myRNA, "gene1", add.trajectory.lineages = list(c(1,2,4), c(1,3)),
    trajectory.cluster.meta = "clustering",
    sub = "Pseudotime Trajectories")

dittoDimPlot(myRNA, "gene1",
    do.contour = TRUE,
    contour.color = "lightblue", # Optional, black by default
    contour.linetype = "dashed") # Optional, solid by default

# Plotting ordering can also be adjusted with 'order':
dittoDimPlot(myRNA, "timepoint", size = 20,
    order = "increasing")
dittoDimPlot(myRNA, "timepoint", size = 20,
    order = "decreasing")
dittoDimPlot(myRNA, "timepoint", size = 20,
    order = "randomize")

dtm2451/DittoSeq documentation built on May 4, 2024, 7:31 a.m.