dittoHex: Show RNAseq data, grouped into hexagonal bins, on a scatter...
In dtm2451/DittoSeq: User Friendly Single-Cell and Bulk RNA Sequencing Visualization
dittoHex R Documentation
Show RNAseq data, grouped into hexagonal bins, on a scatter or dimensionality reduction plot
Description
Show RNAseq data, grouped into hexagonal bins, on a scatter or dimensionality reduction plot
Usage
dittoDimHex(
object,
color.var = NULL,
bins = 30,
color.method = NULL,
reduction.use = .default_reduction(object),
dim.1 = 1,
dim.2 = 2,
cells.use = NULL,
color.panel = dittoColors(),
colors = seq_along(color.panel),
split.by = NULL,
extra.vars = NULL,
multivar.split.dir = c("col", "row"),
split.nrow = NULL,
split.ncol = NULL,
split.adjust = list(),
assay = .default_assay(object),
slot = .default_slot(object),
adjustment = NULL,
swap.rownames = NULL,
assay.extra = assay,
slot.extra = slot,
adjustment.extra = adjustment,
show.axes.numbers = TRUE,
show.grid.lines = !grepl("umap|tsne", tolower(reduction.use)),
main = "make",
sub = NULL,
xlab = "make",
ylab = "make",
theme = theme_bw(),
do.contour = FALSE,
contour.color = "black",
contour.linetype = 1,
min.density = NA,
max.density = NA,
min.color = "#F0E442",
max.color = "#0072B2",
min.opacity = 0.2,
max.opacity = 1,
min = NA,
max = NA,
rename.color.groups = NULL,
do.ellipse = FALSE,
do.label = FALSE,
labels.size = 5,
labels.highlight = TRUE,
labels.repel = TRUE,
labels.split.by = split.by,
labels.repel.adjust = list(),
add.trajectory.lineages = NULL,
add.trajectory.curves = NULL,
trajectory.cluster.meta,
trajectory.arrow.size = 0.15,
data.out = FALSE,
legend.show = TRUE,
legend.color.title = "make",
legend.color.breaks = waiver(),
legend.color.breaks.labels = waiver(),
legend.density.title = if (isBulk(object)) "Samples" else "Cells",
legend.density.breaks = waiver(),
legend.density.breaks.labels = waiver()
)
dittoScatterHex(
object,
x.var,
y.var,
color.var = NULL,
bins = 30,
color.method = NULL,
split.by = NULL,
extra.vars = NULL,
cells.use = NULL,
color.panel = dittoColors(),
colors = seq_along(color.panel),
multivar.split.dir = c("col", "row"),
split.nrow = NULL,
split.ncol = NULL,
split.adjust = list(),
assay.x = .default_assay(object),
slot.x = .default_slot(object),
adjustment.x = NULL,
assay.y = .default_assay(object),
slot.y = .default_slot(object),
adjustment.y = NULL,
assay.color = .default_assay(object),
slot.color = .default_slot(object),
adjustment.color = NULL,
assay.extra = .default_assay(object),
slot.extra = .default_slot(object),
adjustment.extra = NULL,
swap.rownames = NULL,
min.density = NA,
max.density = NA,
min.color = "#F0E442",
max.color = "#0072B2",
min.opacity = 0.2,
max.opacity = 1,
min = NA,
max = NA,
rename.color.groups = NULL,
xlab = x.var,
ylab = y.var,
main = "make",
sub = NULL,
theme = theme_bw(),
do.contour = FALSE,
contour.color = "black",
contour.linetype = 1,
do.ellipse = FALSE,
do.label = FALSE,
labels.size = 5,
labels.highlight = TRUE,
labels.repel = TRUE,
labels.split.by = split.by,
labels.repel.adjust = list(),
add.trajectory.lineages = NULL,
add.trajectory.curves = NULL,
trajectory.cluster.meta,
trajectory.arrow.size = 0.15,
legend.show = TRUE,
legend.color.title = "make",
legend.color.breaks = waiver(),
legend.color.breaks.labels = waiver(),
legend.density.title = if (isBulk(object)) "Samples" else "Cells",
legend.density.breaks = waiver(),
legend.density.breaks.labels = waiver(),
data.out = FALSE
)
Arguments
object
A Seurat, SingleCellExperiment, or SummarizedExperiment object.
color.var
Single string giving a gene or metadata that will set the color of cells/samples in the plot.
Alternatively, can be a directly supplied numeric or string vector or a factor of length equal to the total number of cells/samples in object.
bins
Numeric or numeric vector giving the number of haxagonal bins in the x and y directions. Set to 30 by default.
color.method
Works differently depending on whether the color.var-data is continuous versus discrete:
Continuous: String naming a function for how target data should be summarized for each bin.
Can be any function that summarizes a numeric vector input with a single numeric output value.
Default is median. Other useful options are sum, mean, sd, or mad.
You can also use a previously assigned function; e.g. first run pNonZero <- function(x) { sum(x!=0)/length(x)},
then you give color.method = "pNonZero"
Discrete: A string signifying whether the color should (default) be simply based on the "max" grouping of the bin,
based on "prop.<value>" the proportion of a specific value (e.g. "prop.A" or "prop.TRUE"),
or based on the "max.prop"ortion of cells/samples belonging to any grouping.
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.
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.
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
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
extra.vars
String vector providing names of any extra metadata to be stashed in the dataframe supplied to ggplot(data).
Useful for making custom 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.
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.
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.
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.
main
String, sets the plot title. The default title is either "Density", color.var, or NULL, depending on the identity of color.var.
To remove, set to NULL.
sub
String, sets the plot subtitle.
xlab, ylab
Strings which set the labels for the axes. To remove, set to NULL.
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.
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.
min.density, max.density
Number which sets the min/max values used for the density scale.
Used no matter whether density is represented through opacity or color.
min.color, max.color
color for the min/max values of the color scale.
min.opacity, max.opacity
Scalar between [0,1] which sets the minimum or maximum opacity used for the density legend (when color is used for color.var data and density is shown via opacity).
min, max
Number which sets the values associated with the minimum or maximum color for color.var data.
rename.color.groups
String vector containing new names for the identities of discrete color groups.
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.
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, (for dittoDimHex only, but not dittoScatterHex) 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.
data.out
Logical. When set to TRUE, changes the output from the plot alone to a list containing the plot ("plot"),
and data.frame of the underlying data for target cells ("data").
legend.show
Logical. Whether any legend should be displayed. Default = TRUE.
legend.density.title, legend.color.title
Strings which set the title for the legends.
legend.density.breaks, legend.color.breaks
Numeric vector which sets the discrete values to label in the density and color.var legends.
legend.density.breaks.labels, legend.color.breaks.labels
String vector, with same length as legend.*.breaks, which sets the labels for the tick marks or hex icons of the associated legend.
x.var, y.var
Single string giving a gene or metadata that will be used for the x- and y-axis of the scatterplot.
Note: must be continuous.
Alternatively, can be a directly supplied numeric vector of length equal to the total number of cells/samples in object.
assay.x, assay.y, assay.color, assay.extra, slot.x, slot.y, slot.color, slot.extra
single strings or integers (SCEs and SEs) or an optionally named vector of such values that set which expression data to use for each given data target.
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.x, adjustment.y, adjustment.color, adjustment.extra
For the given data target, when targeting 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]
Details
The functions create a dataframe with x and y coordinates for each cell/sample, determined by either x.var and y.var for dittoScatterHex,
or reduction.use, dim.1 (x), and dim.2 (y) for dittoDimHex.
Extra data requested by color.var for coloring, split.by for faceting, or extra.var for manual external manipulations, are added to the dataframe as well.
For expression/counts data, assay, slot, and adjustment inputs can be used to select which values to use, and if they should be adjusted in some way.
The dataframe is then subset to only target cells/samples based on the cells.use input.
Finally, a hex plot is created using this dataframe:
If color.var is not rovided, coloring is based on the density of cells/samples within each hex bin.
When color.var is provided, density is represented through opacity while coloring is based on a summarization, chosen with the color.method input, of the target color.var data.
If split.by was used, the plot will be split into a matrix of panels based on the associated groupings.
Value
A ggplot object where colored hexagonal bins are used to summarize RNAseq data in a scatterplot or tSNE, PCA, UMAP.
Alternatively, if data.out=TRUE, a list containing two slots is output: the plot (named 'plot'), and a data.table containing the underlying data for target cells (named 'data').
Functions
-
dittoDimHex(): Show RNAseq data overlayed on a tsne, pca, or similar, grouped into hexagonal bins
-
dittoScatterHex(): Make a scatter plot of RNAseq data, grouped into hexagonal bins
Many characteristics of the plot can be adjusted using discrete inputs
Colors: min.color and max.color adjust the colors for continuous data.
For discrete color.var plotting with color.method = "max", colors are instead adjusted with color.panel and/or colors & the labels of the groupings can be changed using rename.color.groups.
Titles and axes labels can be adjusted with main, sub, xlab, ylab, and legend.color.title and legend.density.title arguments.
Legends can also be adjusted in other ways, using variables that all start with "legend." for easy tab completion lookup.
Additional Features
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.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 with some code adapted from Giuseppe D'Agostino
See Also
dittoDimPlot and dittoScatterPlot for making very similar data representations, but where each cell is represented individually.
It is often best to investigate your data with both the individual and hex-bin methods, then pick whichever is the best representation for your particular goal.
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.
Examples
example(importDittoBulk, echo = FALSE)
myRNA
# Mock up some nCount_RNA and nFeature_RNA metadata
# == the default way to extract
myRNA$nCount_RNA <- runif(60,200,1000)
myRNA$nFeature_RNA <- myRNA$nCount_RNA*runif(60,0.95,1.05)
# and also percent.mito metadata
myRNA$percent.mito <- sample(c(runif(50,0,0.05),runif(10,0.05,0.2)))
dittoScatterHex(
myRNA, x.var = "nCount_RNA", y.var = "nFeature_RNA")
dittoDimHex(myRNA)
# We don't have too many samples here, so let's increase the bin size.
dittoDimHex(myRNA, bins = 10)
# x and y bins can be set separately, useful for non-square plots
dittoDimHex(myRNA, bins = c(20, 10))
### Coloring
# Default coloring, as above, is by cell/sample density in the region, but
# 'color.var' can be used to color the data by another metric.
# Density with then be represented via bin opacity.
dittoDimHex(myRNA, color.var = "clustering", bins = 10)
dittoDimHex(myRNA, color.var = "gene1", bins = 10)
# 'color.method' is then used to adjust how the target data is summarized
dittoDimHex(myRNA, color.var = "groups", bins = 10,
color.method = "max.prop")
dittoDimHex(myRNA, color.var = "gene1", bins = 10,
color.method = "mean")
# One particularly useful 'color.method' for discrete 'color.var'-data is
# to use 'prop.<value>' to color by the proportion of a particular value
# within each bin:
dittoDimHex(myRNA, color.var = "groups", bins = 10,
color.method = "prop.A")
### Additional Features:
# Faceting with 'split.by'
dittoDimHex(myRNA, bins = 10, split.by = "groups")
dittoDimHex(myRNA, bins = 10, split.by = c("groups", "clustering"))
# Faceting can also be used to show multiple continuous variables side-by-side
# by giving a vector of continuous metadata or gene names to 'color.var'.
# This can also be combined with 1 'split.by' variable, with direction then
# controlled via 'multivar.split.dir':
dittoDimHex(myRNA, bins = 10,
color.var = c("gene1", "gene2"))
dittoDimHex(myRNA, bins = 10,
color.var = c("gene1", "gene2"),
split.by = "groups")
dittoDimHex(myRNA, bins = 10,
color.var = c("gene1", "gene2"),
split.by = "groups",
multivar.split.dir = "row")
# Underlying data output with 'data.out = TRUE'
dittoDimHex(myRNA, data.out = TRUE)
# Contour lines can be added with 'do.contours = TRUE'
dittoDimHex(myRNA, bins = 10,
do.contour = TRUE,
contour.color = "lightblue", # Optional, black by default
contour.linetype = "dashed") # Optional, solid by default
# Trajectories can be added to dittoDimHex plots
dittoDimHex(myRNA, bins = 10,
add.trajectory.lineages = list(c(1,2,4), c(1,4), c(1,3)),
trajectory.cluster.meta = "clustering")
dtm2451/DittoSeq documentation built on May 4, 2024, 7:31 a.m.
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| dittoHex | R Documentation |
Show RNAseq data, grouped into hexagonal bins, on a scatter or dimensionality reduction plot
Description
Show RNAseq data, grouped into hexagonal bins, on a scatter or dimensionality reduction plot
Usage
dittoDimHex(
object,
color.var = NULL,
bins = 30,
color.method = NULL,
reduction.use = .default_reduction(object),
dim.1 = 1,
dim.2 = 2,
cells.use = NULL,
color.panel = dittoColors(),
colors = seq_along(color.panel),
split.by = NULL,
extra.vars = NULL,
multivar.split.dir = c("col", "row"),
split.nrow = NULL,
split.ncol = NULL,
split.adjust = list(),
assay = .default_assay(object),
slot = .default_slot(object),
adjustment = NULL,
swap.rownames = NULL,
assay.extra = assay,
slot.extra = slot,
adjustment.extra = adjustment,
show.axes.numbers = TRUE,
show.grid.lines = !grepl("umap|tsne", tolower(reduction.use)),
main = "make",
sub = NULL,
xlab = "make",
ylab = "make",
theme = theme_bw(),
do.contour = FALSE,
contour.color = "black",
contour.linetype = 1,
min.density = NA,
max.density = NA,
min.color = "#F0E442",
max.color = "#0072B2",
min.opacity = 0.2,
max.opacity = 1,
min = NA,
max = NA,
rename.color.groups = NULL,
do.ellipse = FALSE,
do.label = FALSE,
labels.size = 5,
labels.highlight = TRUE,
labels.repel = TRUE,
labels.split.by = split.by,
labels.repel.adjust = list(),
add.trajectory.lineages = NULL,
add.trajectory.curves = NULL,
trajectory.cluster.meta,
trajectory.arrow.size = 0.15,
data.out = FALSE,
legend.show = TRUE,
legend.color.title = "make",
legend.color.breaks = waiver(),
legend.color.breaks.labels = waiver(),
legend.density.title = if (isBulk(object)) "Samples" else "Cells",
legend.density.breaks = waiver(),
legend.density.breaks.labels = waiver()
)
dittoScatterHex(
object,
x.var,
y.var,
color.var = NULL,
bins = 30,
color.method = NULL,
split.by = NULL,
extra.vars = NULL,
cells.use = NULL,
color.panel = dittoColors(),
colors = seq_along(color.panel),
multivar.split.dir = c("col", "row"),
split.nrow = NULL,
split.ncol = NULL,
split.adjust = list(),
assay.x = .default_assay(object),
slot.x = .default_slot(object),
adjustment.x = NULL,
assay.y = .default_assay(object),
slot.y = .default_slot(object),
adjustment.y = NULL,
assay.color = .default_assay(object),
slot.color = .default_slot(object),
adjustment.color = NULL,
assay.extra = .default_assay(object),
slot.extra = .default_slot(object),
adjustment.extra = NULL,
swap.rownames = NULL,
min.density = NA,
max.density = NA,
min.color = "#F0E442",
max.color = "#0072B2",
min.opacity = 0.2,
max.opacity = 1,
min = NA,
max = NA,
rename.color.groups = NULL,
xlab = x.var,
ylab = y.var,
main = "make",
sub = NULL,
theme = theme_bw(),
do.contour = FALSE,
contour.color = "black",
contour.linetype = 1,
do.ellipse = FALSE,
do.label = FALSE,
labels.size = 5,
labels.highlight = TRUE,
labels.repel = TRUE,
labels.split.by = split.by,
labels.repel.adjust = list(),
add.trajectory.lineages = NULL,
add.trajectory.curves = NULL,
trajectory.cluster.meta,
trajectory.arrow.size = 0.15,
legend.show = TRUE,
legend.color.title = "make",
legend.color.breaks = waiver(),
legend.color.breaks.labels = waiver(),
legend.density.title = if (isBulk(object)) "Samples" else "Cells",
legend.density.breaks = waiver(),
legend.density.breaks.labels = waiver(),
data.out = FALSE
)
Arguments
object |
A Seurat, SingleCellExperiment, or SummarizedExperiment object. |
color.var |
Single string giving a gene or metadata that will set the color of cells/samples in the plot. Alternatively, can be a directly supplied numeric or string vector or a factor of length equal to the total number of cells/samples in |
bins |
Numeric or numeric vector giving the number of haxagonal bins in the x and y directions. Set to 30 by default. |
color.method |
Works differently depending on whether the Continuous: String naming a function for how target data should be summarized for each bin.
Can be any function that summarizes a numeric vector input with a single numeric output value.
Default is Discrete: A string signifying whether the color should (default) be simply based on the "max" grouping of the bin, based on "prop.<value>" the proportion of a specific value (e.g. "prop.A" or "prop.TRUE"), or based on the "max.prop"ortion of cells/samples belonging to any grouping. |
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 |
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. |
color.panel |
String vector which sets the colors to draw from. |
colors |
Integer vector, the indexes / order, of colors from color.panel to actually use |
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 alterations after dittoSeq plot generation. |
multivar.split.dir |
"row" or "col", sets the direction of faceting used for 'var' values when |
split.nrow, split.ncol |
Integers which set the dimensions of faceting/splitting when a single metadata is given to |
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 |
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 |
adjustment |
When plotting gene / feature expression, should that data be used directly (default) or should it be adjusted to be
|
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 |
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 |
main |
String, sets the plot title. The default title is either "Density", |
sub |
String, sets the plot subtitle. |
xlab, ylab |
Strings which set the labels for the axes. To remove, set to |
theme |
A ggplot theme which will be applied before dittoSeq adjustments.
Default = |
do.contour |
Logical. Whether density-based contours should be displayed. |
contour.color |
String that sets the color(s) of the |
contour.linetype |
String or numeric which sets the type of line used for |
min.density, max.density |
Number which sets the min/max values used for the density scale. Used no matter whether density is represented through opacity or color. |
min.color, max.color |
color for the min/max values of the color scale. |
min.opacity, max.opacity |
Scalar between [0,1] which sets the minimum or maximum opacity used for the density legend (when color is used for |
min, max |
Number which sets the values associated with the minimum or maximum color for |
rename.color.groups |
String vector containing new names for the identities of discrete color groups. |
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 |
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 |
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 If the |
add.trajectory.curves |
List of matrices, each representing coordinates for a trajectory path, from start to end, where matrix columns represent x ( Alternatively, (for dittoDimHex only, but not dittoScatterHex) a list of lists(/princurve objects) can be provided.
Thus, if the |
trajectory.cluster.meta |
String name of metadata containing the clusters that were used for generating trajectories. Required when plotting trajectories using the |
trajectory.arrow.size |
Number representing the size of trajectory arrows, in inches. Default = 0.15. |
data.out |
Logical. When set to |
legend.show |
Logical. Whether any legend should be displayed. Default = |
legend.density.title, legend.color.title |
Strings which set the title for the legends. |
legend.density.breaks, legend.color.breaks |
Numeric vector which sets the discrete values to label in the density and color.var legends. |
legend.density.breaks.labels, legend.color.breaks.labels |
String vector, with same length as |
x.var, y.var |
Single string giving a gene or metadata that will be used for the x- and y-axis of the scatterplot. Note: must be continuous. Alternatively, can be a directly supplied numeric vector of length equal to the total number of cells/samples in |
assay.x, assay.y, assay.color, assay.extra, slot.x, slot.y, slot.color, slot.extra |
single strings or integers (SCEs and SEs) or an optionally named vector of such values that set which expression data to use for each given data target.
See |
adjustment.x, adjustment.y, adjustment.color, adjustment.extra |
For the given data target, when targeting gene / feature expression, should that data be used directly (default) or should it be adjusted to be
|
Details
The functions create a dataframe with x and y coordinates for each cell/sample, determined by either x.var and y.var for dittoScatterHex,
or reduction.use, dim.1 (x), and dim.2 (y) for dittoDimHex.
Extra data requested by color.var for coloring, split.by for faceting, or extra.var for manual external manipulations, are added to the dataframe as well.
For expression/counts data, assay, slot, and adjustment inputs can be used to select which values to use, and if they should be adjusted in some way.
The dataframe is then subset to only target cells/samples based on the cells.use input.
Finally, a hex plot is created using this dataframe:
If color.var is not rovided, coloring is based on the density of cells/samples within each hex bin.
When color.var is provided, density is represented through opacity while coloring is based on a summarization, chosen with the color.method input, of the target color.var data.
If split.by was used, the plot will be split into a matrix of panels based on the associated groupings.
Value
A ggplot object where colored hexagonal bins are used to summarize RNAseq data in a scatterplot or tSNE, PCA, UMAP.
Alternatively, if data.out=TRUE, a list containing two slots is output: the plot (named 'plot'), and a data.table containing the underlying data for target cells (named 'data').
Functions
-
dittoDimHex(): Show RNAseq data overlayed on a tsne, pca, or similar, grouped into hexagonal bins -
dittoScatterHex(): Make a scatter plot of RNAseq data, grouped into hexagonal bins
Many characteristics of the plot can be adjusted using discrete inputs
Colors:
min.colorandmax.coloradjust the colors for continuous data.For discrete
color.varplotting withcolor.method = "max", colors are instead adjusted withcolor.paneland/orcolors& the labels of the groupings can be changed usingrename.color.groups.Titles and axes labels can be adjusted with
main,sub,xlab,ylab, andlegend.color.titleandlegend.density.titlearguments.Legends can also be adjusted in other ways, using variables that all start with "
legend." for easy tab completion lookup.
Additional Features
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.contouris provided, density gradiant contour lines will be overlaid with color and linetype adjustable viacontour.colorandcontour.linetype.If
add.trajectory.lineagesis 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 totrajectory.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.curvesis 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 thetrajectory.arrow.sizeinput.
Author(s)
Daniel Bunis with some code adapted from Giuseppe D'Agostino
See Also
dittoDimPlot and dittoScatterPlot for making very similar data representations, but where each cell is represented individually.
It is often best to investigate your data with both the individual and hex-bin methods, then pick whichever is the best representation for your particular goal.
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.
Examples
example(importDittoBulk, echo = FALSE)
myRNA
# Mock up some nCount_RNA and nFeature_RNA metadata
# == the default way to extract
myRNA$nCount_RNA <- runif(60,200,1000)
myRNA$nFeature_RNA <- myRNA$nCount_RNA*runif(60,0.95,1.05)
# and also percent.mito metadata
myRNA$percent.mito <- sample(c(runif(50,0,0.05),runif(10,0.05,0.2)))
dittoScatterHex(
myRNA, x.var = "nCount_RNA", y.var = "nFeature_RNA")
dittoDimHex(myRNA)
# We don't have too many samples here, so let's increase the bin size.
dittoDimHex(myRNA, bins = 10)
# x and y bins can be set separately, useful for non-square plots
dittoDimHex(myRNA, bins = c(20, 10))
### Coloring
# Default coloring, as above, is by cell/sample density in the region, but
# 'color.var' can be used to color the data by another metric.
# Density with then be represented via bin opacity.
dittoDimHex(myRNA, color.var = "clustering", bins = 10)
dittoDimHex(myRNA, color.var = "gene1", bins = 10)
# 'color.method' is then used to adjust how the target data is summarized
dittoDimHex(myRNA, color.var = "groups", bins = 10,
color.method = "max.prop")
dittoDimHex(myRNA, color.var = "gene1", bins = 10,
color.method = "mean")
# One particularly useful 'color.method' for discrete 'color.var'-data is
# to use 'prop.<value>' to color by the proportion of a particular value
# within each bin:
dittoDimHex(myRNA, color.var = "groups", bins = 10,
color.method = "prop.A")
### Additional Features:
# Faceting with 'split.by'
dittoDimHex(myRNA, bins = 10, split.by = "groups")
dittoDimHex(myRNA, bins = 10, split.by = c("groups", "clustering"))
# Faceting can also be used to show multiple continuous variables side-by-side
# by giving a vector of continuous metadata or gene names to 'color.var'.
# This can also be combined with 1 'split.by' variable, with direction then
# controlled via 'multivar.split.dir':
dittoDimHex(myRNA, bins = 10,
color.var = c("gene1", "gene2"))
dittoDimHex(myRNA, bins = 10,
color.var = c("gene1", "gene2"),
split.by = "groups")
dittoDimHex(myRNA, bins = 10,
color.var = c("gene1", "gene2"),
split.by = "groups",
multivar.split.dir = "row")
# Underlying data output with 'data.out = TRUE'
dittoDimHex(myRNA, data.out = TRUE)
# Contour lines can be added with 'do.contours = TRUE'
dittoDimHex(myRNA, bins = 10,
do.contour = TRUE,
contour.color = "lightblue", # Optional, black by default
contour.linetype = "dashed") # Optional, solid by default
# Trajectories can be added to dittoDimHex plots
dittoDimHex(myRNA, bins = 10,
add.trajectory.lineages = list(c(1,2,4), c(1,4), c(1,3)),
trajectory.cluster.meta = "clustering")
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