Sample spatial cluster visualization workhorse function

This function visualizes the clusters for one given sample at the spot-level using (by default) the histology information on the background. This is the function that does all the plotting behind vis_clus(). To visualize gene-level (or any continuous variable) use vis_gene_p().

vis_clus_p(
  spe,
  d,
  clustervar,
  sampleid = unique(spe$sample_id)[1],
  colors,
  spatial,
  title,
  image_id = "lowres",
  alpha = NA,
  point_size = 2,
  auto_crop = TRUE,
  na_color = "#CCCCCC40"
)

Arguments

spe: A SpatialExperiment-class object. See fetch_data() for how to download some example objects or read10xVisiumWrapper() to read in spaceranger --count output files and build your own spe object.
d: A data.frame() with the sample-level information. This is typically obtained using cbind(colData(spe), spatialCoords(spe)).
clustervar: A character(1) with the name of the colData(spe) column that has the cluster values.
sampleid: A character(1) specifying which sample to plot from colData(spe)$sample_id (formerly colData(spe)$sample_name).
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.
title: The title for the plot.
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.
na_color: A character(1) specifying a color for the NA values. If you set alpha = NA then it's best to set na_color to a color that has alpha blending already, which will make non-NA values pop up more and the NA values will show with a lighter color. This behavior is lost when alpha is set to a non-NA value.

Value

A ggplot2 object.

Examples


if (enough_ram()) {
    ## Obtain the necessary data
    if (!exists("spe")) spe <- fetch_data("spe")
    spe_sub <- spe[, spe$sample_id == "151673"]

    ## Use the manual color palette by Lukas M Weber
    ## Don't plot the histology information
    p <- vis_clus_p(
        spe = spe_sub,
        d = as.data.frame(cbind(colData(spe_sub), SpatialExperiment::spatialCoords(spe_sub)), optional = TRUE),
        clustervar = "layer_guess_reordered",
        sampleid = "151673",
        colors = libd_layer_colors,
        title = "151673 LIBD Layers",
        spatial = FALSE
    )
    print(p)

    ## Clean up
    rm(spe_sub)
}
#> 2025-09-17 01:19:43.534859 loading file /github/home/.cache/R/BiocFileCache/22ac1bc3fd2d_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1

Sample spatial cluster visualization workhorse function

Arguments

Value

See also

Examples