plotting-surface.Rmd
Surface plotting allows to visualize the barcode-spot’s information
with a spatial dimension. Behind the scenes they are scatterplots
whereby the x-aesthetic and y-aesthetic of the plot is mapped to the
respective coordinate-variable. SPATA2
offers a variety of
functions and options to visualize expression of genes or other
variables on the surface of the tissue sample.
# load required packages
library(SPATA2)
library(tidyverse)
# load object and process for this tutorial
object_t313 <-
loadExampleObject("UKF313T", process = TRUE, meta = TRUE) %>%
spatialAnnotationToGrouping(
object = .,
ids = c("necrotic_area", "necrotic_edge", "necrotic_edge2"),
grouping_name = "histology",
inside = "necrotic",
outside = "vivid"
)
object_t313 <- setDefault(object_t313, display_image = FALSE)
plotImage(object_t313)
The most important function to visualize gene expression on top of
the slide is plotSurface()
. It comes with a variety of
options for the plotting of both, continuous and categorical data.
Categorical data includes usually grouping of spots via clustering or spatial manual annotation.
plotSurface(
object = object_t313,
color_by = "bayes_space",
pt_clrp = "uc"
)
plotSurface(
object = object_t313,
color_by = "histology",
pt_clrp = "npg"
)
Using pt_clrp
you can adjust the color palette used to
display the groups. The following are predefined within SPATA2 and can
be used by referring to them by name. Inspect them via
showColorPalettes()
. To adjust single colors of groups use
the clrp_adjust
argument.
plotSurface(
object = object_t313,
color_by = "bayes_space",
pt_clrp = "jama",
clrp_adjust = c("B5" = "black") # for single adjustments
)
plotSurface(
object = object_t313,
color_by = "histology",
pt_clrp = "npg",
clrp_adjust = c("necrotic" = "black", "vivid" = "forestgreen") # ... or for multiple/all
)
You can use the ggpLayerGroupOutline()
function to
highlight the spatial extent of groups from the same or from other
grouping variables. Note, that ggpLayerGroupOutline()
outlines observations of one and the same group. Use
ggpLayerSpatAnnOutline()
to outline spatial annotations.
# creates ggproto objects that can be added via `+`
necrosis_outline <-
ggpLayerGroupOutline(
object = object_t313,
grouping = "histology",
group_subset = "necrotic",
line_size = 1.5
)
cluster_outline <-
ggpLayerGroupOutline(
object = object_t313,
grouping = "bayes_space",
group_subset = c("B1", "B6"),
line_size = 1.5
)
# plot with additional layers
plotSurface(
object = object_t313,
color_by = "bayes_space",
pt_clrp = "uc"
) +
cluster_outline +
labs(subtitle = "Cluster Outline")
plotSurface(
object = object_t313,
color_by = "bayes_space",
pt_clrp = "uc"
) +
necrosis_outline +
labs(subtitle = "Necrosis Outline")
Continuous data, such as gene expression, gene set expression,
RNA-count etc. Albeit viridis spectra are a commonly used you
might want to try different ones. The color spectra implemented in
SPATA2
can be obtained via validColorSpectra()
and inspected via showColorSpectra()
.
plotSurface(
object = object_t313,
color_by = "HM_HYPOXIA",
pt_clrsp = "inferno"
)
plotSurface(
object = object_t313,
color_by = "HM_HYPOXIA",
alpha_by = "HM_HYPOXIA", # use alpha, too
pt_clrsp = "inferno",
display_image = TRUE
)
The plot below does not look insightful due to the distribution of
the plotted variable. The argument transform_with
allows to
perform mathematical transformation before plotting if needed. As
n_counts_gene is not normally distributed you might want to
transform it to better visualize important information.
plotSurface(
object = object_t313,
color_by = "n_counts_gene",
pt_clrsp = "plasma"
)
plotDensityplot(object = object_t313, variables = "n_counts_gene")
plotSurface(
object = object_t313,
color_by = "n_counts_gene",
pt_clrsp = "plasma",
transform_with = log10 # provide the function
) +
labs(color = "Counts (log10)", subtitle = "No Outline")
plotSurface(
object = object_t313,
color_by = "n_counts_gene",
pt_clrsp = "plasma",
transform_with = log10 # or a named list with a function to apply
) +
labs(color = "Counts (log10)", subtitle = "Necrosis Outline") +
necrosis_outline
Color spectra that plot against white require to outline the tissue.
Use ggpLayerTissueOutline()
for that matter.
# obtain computed tissue outline layer to plot against white
tissue_outline <-
ggpLayerTissueOutline(object = object_t313, line_color = "grey")
plotSurface(
object = object_t313,
color_by = "VEGFA",
pt_clrsp = "Greens 3"
) + tissue_outline
plotSurface(
object = object_t313,
color_by = "VEGFA",
pt_clrsp = "Greens 3",
outline = TRUE # black with black/white points in the background
)
Smoothing of continuous variables is possible, too. Use
smooth
and smooth_span
for that matter.
plotSurface(
object = object_t313,
color_by = "HM_HYPOXIA",
smooth = FALSE, # the default
pt_clrsp = "Greens 3",
outline = TRUE
)
plotSurface(
object = object_t313,
color_by = "HM_HYPOXIA",
pt_clrsp = "Greens 3",
smooth = TRUE,
smooth_span = 0.2,
outline = T
)
In order to quickly compare the spatial distribution of a set of
variables of the same kind use plotSurfaceComparison()
.
# compare gene expression on the surface
plotSurfaceComparison(
object = object_t313,
color_by = c("CSTB", "HM_HYPOXIA", "MYL6", "CCL2"),
pt_clrsp = "Greens 3",
display_image = TRUE,
#smooth = TRUE,
alpha_by = TRUE
)