This function visualizes the clusters for one given sample at the spot-level using (by default) the histology information on the background. To visualize gene-level (or any continuous variable) use vis_gene().

vis_clus(
spe,
sampleid = unique(spe$sample_id)[1], clustervar, colors = c("#b2df8a", "#e41a1c", "#377eb8", "#4daf4a", "#ff7f00", "gold", "#a65628", "#999999", "black", "grey", "white", "purple"), spatial = TRUE, image_id = "lowres", alpha = NA, point_size = 2, auto_crop = TRUE, ... ) ## Arguments spe Defaults to the output of fetch_data(type = 'spe'). This is a SpatialExperiment-class object with the spot-level Visium data and information required for visualizing the histology. See fetch_data() for more details. sampleid A character(1) specifying which sample to plot from colData(spe)$sample_id (formerly colData(spe)\$sample_name).

clustervar

A character(1) with the name of the colData(spe) column that has the cluster values.

colors

A vector of colors to use for visualizing the clusters from clustervar. If the vector has names, then those should match the values of clustervar.

spatial

A logical(1) indicating whether to include the histology layer from geom_spatial(). If you plan to use ggplotly() then it's best to set this to FALSE.

image_id

A character(1) with the name of the image ID you want to use in the background.

alpha

A numeric(1) in the [0, 1] range that specifies the transparency level of the data on the spots.

point_size

A numeric(1) specifying the size of the points. Defaults to 1.25. Some colors look better if you use 2 for instance.

auto_crop

A logical(1) indicating whether to automatically crop the image / plotting area, which is useful if the Visium capture area is not centered on the image and if the image is not a square.

...

Passed to paste0() for making the title of the plot following the sampleid.

## Value

A ggplot2 object.

## Details

This function subsets spe to the given sample and prepares the data and title for vis_clus_p().

Other Spatial cluster visualization functions: frame_limits(), vis_clus_p(), vis_grid_clus()

## Examples


if (enough_ram()) {
## Obtain the necessary data
if (!exists("spe")) spe <- fetch_data("spe")

## Check the colors defined by Lukas M Weber
libd_layer_colors

## Use the manual color palette by Lukas M Weber
p1 <- vis_clus(
spe = spe,
clustervar = "layer_guess_reordered",
sampleid = "151673",
colors = libd_layer_colors,
... = " LIBD Layers"
)
print(p1)

## Without auto-cropping the image
p2 <- vis_clus(
spe = spe,
clustervar = "layer_guess_reordered",
sampleid = "151673",
colors = libd_layer_colors,
auto_crop = FALSE,
... = " LIBD Layers"
)
print(p2)

## Without histology
p3 <- vis_clus(
spe = spe,
clustervar = "layer_guess_reordered",
sampleid = "151673",
colors = libd_layer_colors,
... = " LIBD Layers",
spatial = FALSE
)
print(p3)
}
#> snapshotDate(): 2022-10-31