Surface Plotting

1. Prerequisites

None.

2. Introduction

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.

library(SPATA2)
library(SPATAData)
library(tidyverse)

object_t313 <- downloadFromPublication(pub = "kueckelhaus_et_al_2024", id = "UKF313T")

object_t313 <- setDefault(object_t313, display_image = FALSE)

plotImage(object_t313)

3. Basic surface plots one by one

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.

3.1 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("B4" = "black") # for single adjustments
)

plotSurface(
  object = object_t313, 
  color_by = "histology", 
  pt_clrp = "npg", 
  clrp_adjust = c("necrosis" = "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",
    groups_subset = "necrosis", 
    line_size = 1.5
  ) 

cluster_outline <- 
  ggpLayerGroupOutline(
    object = object_t313, 
    grouping = "bayes_space",
    groups_subset = c("B4", "B5"), 
    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")

3.2 Continuous data on the surface

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
) 

3.2.1 Variable transformation

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 nCount_Spatial is not normally distributed you might want to transform it to better visualize important information.

plotSurface(
  object = object_t313, 
  color_by = "nCount_Spatial", 
  pt_clrsp = "plasma" 
) 

plotDensityplot(object = object_t313, variables = "nCount_Spatial")

plotSurface(
  object = object_t313, 
  color_by = "nCount_Spatial", 
  pt_clrsp = "plasma", 
  transform_with = log10 # provide the function
) + 
  labs(color = "Counts (log10)", subtitle = "No Outline")

plotSurface(
  object = object_t313, 
  color_by = "nCount_Spatial", 
  pt_clrsp = "plasma", 
  transform_with = list(nCount_Spatial = log10) # or a named list with a function to apply
) + 
  labs(color = "Counts (log10)", subtitle = "Necrosis Outline") +
  necrosis_outline

3.2.2 Tissue 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
) 

3.2.3 Spatial smoothing

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
)  

4. Surface plots in comparison

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
)