Description Usage Arguments Details Value Functions Many characteristics of the plot can be adjusted using discrete inputs Author(s) See Also Examples
Plots continuous data for cutomizable cells'/samples' groupings on a y-axis
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | dittoPlot(
object,
var,
group.by,
color.by = group.by,
shape.by = NULL,
split.by = NULL,
extra.vars = NULL,
cells.use = NULL,
plots = c("jitter", "vlnplot"),
assay = .default_assay(object),
slot = .default_slot(object),
adjustment = NULL,
do.hover = FALSE,
hover.data = var,
color.panel = dittoColors(),
colors = seq_along(color.panel),
shape.panel = c(16, 15, 17, 23, 25, 8),
theme = theme_classic(),
main = "make",
sub = NULL,
ylab = "make",
y.breaks = NULL,
min = NULL,
max = NULL,
xlab = group.by,
x.labels = NULL,
x.labels.rotate = NA,
x.reorder = NULL,
split.nrow = NULL,
split.ncol = NULL,
do.raster = FALSE,
raster.dpi = 300,
jitter.size = 1,
jitter.width = 0.2,
jitter.color = "black",
jitter.shape.legend.size = NA,
jitter.shape.legend.show = TRUE,
boxplot.width = 0.2,
boxplot.color = "black",
boxplot.show.outliers = NA,
boxplot.fill = TRUE,
boxplot.position.dodge = vlnplot.width,
vlnplot.lineweight = 1,
vlnplot.width = 1,
vlnplot.scaling = "area",
ridgeplot.lineweight = 1,
ridgeplot.scale = 1.25,
ridgeplot.ymax.expansion = NA,
add.line = NULL,
line.linetype = "dashed",
line.color = "black",
legend.show = TRUE,
legend.title = "make",
data.out = FALSE
)
dittoRidgePlot(..., plots = c("ridgeplot"))
dittoRidgeJitter(..., plots = c("ridgeplot", "jitter"))
dittoBoxPlot(..., plots = c("boxplot", "jitter"))
|
object |
A Seurat, SingleCellExperiment, or SummarizedExperiment object. |
var |
Single string representing the name of a metadata or gene, OR a vector with length equal to the total number of cells/samples in the dataset. This is the data that will be displayed. |
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 |
shape.by |
Single string representing the name of a metadata to use for setting the shapes of the jitter points. When not provided, all cells/samples will be represented with dots. |
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 |
extra.vars |
String vector providing names of any extra metadata to be stashed in the dataframe supplied to Useful for making custom spliting/faceting or other additional alterations after dittoSeq plot generation. |
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 integer that set which data to use when plotting gene expression / feature data. See |
adjustment |
When plotting gene expression / feature counts, should that data be used directly (default) or should it be adjusted to be
|
do.hover |
Logical. Default = Note: Currently, hovering is incompatible with RidgePlots as plotly does not support the geom_density_ridges2 geom. |
hover.data |
String vector, a list of variable names, c("meta1","gene1","meta2",...) which determines what data to show upon hover when do.hover is set to |
color.panel |
String vector which sets the colors to draw from for plot fills.
Default = |
colors |
Integer vector, the indexes / order, of colors from color.panel to actually use.
(Provides an alternative to directly modifying |
shape.panel |
Vector of integers corresponding to ggplot shapes which sets what shapes to use.
When discrete groupings are supplied by |
theme |
A ggplot theme which will be applied before dittoSeq adjustments.
Default = |
main |
String, sets the plot title. Default = "make" and if left as make, a title will be automatically generated. To remove, set to |
sub |
String, sets the plot subtitle |
ylab |
String, sets the continuous-axis label (=y-axis for box and violin plots, x-axis for ridgeplots).
Defaults to " |
y.breaks |
Numeric vector, a set of breaks that should be used as major gridlines. 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 show. Default = set based on the limits of the data in var. |
xlab |
String which sets the grouping-axis label (=x-axis for box and violin plots, y-axis for ridgeplots).
Set to |
x.labels |
String vector, c("label1","label2","label3",...) which overrides the names of the samples/groups. |
x.labels.rotate |
Logical which sets whether the labels should be rotated.
Default: |
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: |
split.nrow, split.ncol |
Integers which set the dimensions of faceting/splitting when a single metadata is given to |
do.raster |
Logical. When set to |
raster.dpi |
Number indicating dots/pixels per inch (dpi) to use for rasterization. Default = 300. |
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. |
jitter.color |
String which sets the color of the jitter shapes |
jitter.shape.legend.size |
Scalar which changes the size of the shape key in the legend.
If set to |
jitter.shape.legend.show |
Logical which sets whether the shapes legend will be shown when its shape is determined by |
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 |
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 distance between boxplots when multiple are drawn per grouping (a.k.a. when |
vlnplot.lineweight |
Scalar which sets the thickness of the line that outlines the violin plots. |
vlnplot.width |
Scalar which sets the width/spread of the jitter in the x direction |
vlnplot.scaling |
String which sets how the widths of the of violin plots are set in relation to eachother.
Options are "area", "count", and "width". If the deafult is not right for your data, I recommend trying "width".
For a detailed explanation of each, see |
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 topmost grouping and the top of the plot in order to ensure 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: #groups of 3 or fewer: 0.6; #groups=12: 0.1; #groups or 34 or greater: 0.05. |
add.line |
numeric value(s) where one or multiple line should be added |
line.linetype |
String which sets the type of line for |
line.color |
String that sets the color(s) of the |
legend.show |
Logical. Whether the legend should be displayed. Default = |
legend.title |
String or |
data.out |
Logical. When set to Note: plotly conversion is turned off in the |
... |
arguments passed to dittoPlot by dittoRidgePlot, dittoRidgeJitter, and dittoBoxPlot wrappers. Options are all the ones above. |
The function creates a dataframe containing the metadata or expression data associated with the given var
(or if a vector of data is provided, that data).
On the discrete axis, data will be grouped by the metadata given to group.by
and colored by the metadata given to color.by
.
The assay
and slot
inputs can be used to change what expression data is used when displaying gene expression.
If a set of cells to use is indicated with the cells.use
input, the data is subset to include only those cells before plotting.
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"
.
Inclusion of "ridgeplot"
overrides boxplot and violin plot and changes the plot to be horizontal.
When split.by
is provided the name of a metadata containing discrete data, separate plots will be produced representing each of the distinct groupings of the split.by data.
dittoRidgePlot
, dittoRidgeJitter
, and dittoBoxPlot
are included as wrappers of the basic dittoPlot
function
that simply change the default for the plots
input to be "ridgeplot"
, c("ridgeplot","jitter")
, or c("boxplot","jitter")
,
to make such plots even easier to produce.
a ggplot or plotly where continuous data, grouped by sample, age, cluster, etc., shown on either the y-axis by a violin plot, boxplot, and/or jittered points, or on the x-axis by a ridgeplot with or without jittered points.
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.
dittoRidgePlot
: Plots continuous data for cutomizable cells'/samples' groupings horizontally in a density representation
dittoRidgeJitter
: dittoRidgePlot, but with jitter overlaid
dittoBoxPlot
: Plots continuous data for cutomizable cells'/samples' groupings in boxplot form
Each data representation has options which are controlled by variables that start with their associated string.
For example, all jitter adjustments, like jitter.size
, start with "jitter.
".
Colors can be adjusted with color.panel
.
Shapes used in conjunction with shape.by
can be adjusted with shape.panel
.
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 = TRUE
.
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 off with x.labels.rotate = FALSE
.
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.
Single or multiple additional per-cell features can be retrieved and stashed within the underlying data using extra.vars
.
This can be very useful for making manual additional alterations after dittoSeq plot generation.
Daniel Bunis
multi_dittoPlot
for easy creation of multiple dittoPlots each focusing on a different var
.
dittoPlotVarsAcrossGroups
to create dittoPlots that show summarized expression (or values for metadata), accross groups, of multiple vars
in a single plot.
dittoRidgePlot
, dittoRidgeJitter
, and dittoBoxPlot
for shortcuts to a few 'plots' input shortcuts
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | example(importDittoBulk, echo = FALSE)
myRNA
# Basic dittoplot, with jitter behind a vlnplot (looks better with more cells)
dittoPlot(object = myRNA, var = "gene1", group.by = "timepoint")
# Color distinctly from the grouping variable using 'color.by'
dittoPlot(object = myRNA, var = "gene1", group.by = "timepoint",
color.by = "conditions")
# Update the 'plots' input to change / reorder the data representations
dittoPlot(myRNA, "gene1", "timepoint",
plots = c("vlnplot", "boxplot", "jitter"))
# Modify the look with intuitive inputs
dittoPlot(myRNA, "gene1", "timepoint",
plots = c("vlnplot", "boxplot", "jitter"),
boxplot.color = "white",
main = "CD3E",
legend.show = FALSE)
# Data can also be split in other ways with 'shape.by' or 'split.by'
dittoPlot(object = myRNA, var = "gene1", group.by = "timepoint",
plots = c("vlnplot", "boxplot", "jitter"),
shape.by = "clustering",
split.by = "SNP") # single split.by element
dittoPlot(object = myRNA, var = "gene1", group.by = "timepoint",
plots = c("vlnplot", "boxplot", "jitter"),
split.by = c("groups","SNP")) # row and col split.by elements
# For faceting, instead of using 'split.by', the target data can alternatively
# be given to 'extra.var' to have it added in the underlying dataframe, then
# faceting can be added manually for extra flexibility
dittoPlot(myRNA, "gene1", "clustering",
plots = c("vlnplot", "boxplot", "jitter"),
extra.var = "SNP") + facet_wrap("SNP", ncol = 1, strip.position = "left")
# Quickly make a Ridgeplot
dittoRidgePlot(myRNA, "gene1", group.by = "timepoint")
# Quickly make a Boxplot
dittoBoxPlot(myRNA, "gene1", group.by = "timepoint")
|
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