Sample spatial cluster visualization

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,
  na_color = "#CCCCCC40",
  is_stitched = FALSE,
  guide_point_size = point_size,
  datatype = c("Visium", "Xenium"),
  ...
)

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.

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.

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.

is_stitched

A logical(1) vector: If TRUE, expects a SpatialExperiment-class built with visiumStitched::build_spe(). http://research.libd.org/visiumStitched/reference/build_spe.html; in particular, expects a logical colData column exclude_overlapping specifying which spots to exclude from the plot. Sets auto_crop = FALSE.

guide_point_size

A numeric(1) specifying the size of the points in guide. Defaults to point_size. Increase to improve visibility.

datatype

A character(1) specifying the type of spatial transcriptomics data stored in spe. Supported options are:

"Visium": (Default) Expects pxl_col_in_fullres and pxl_row_in_fullres as columns of spatialCoords(spe). Enables image handling via the spatialData slot.
"Xenium": Expects x_centroid and y_centroid as columns of spatialCoords(spe).

...

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() or vis_clus_c().

Examples


if (enough_ram()) {
    ## Obtain the necessary data: Visium example
    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)

    ## With some NA values
    spe$tmp <- spe$layer_guess_reordered
    spe$tmp[spe$sample_id == "151673"][seq_len(500)] <- NA
    p4 <- vis_clus(
        spe = spe,
        clustervar = "tmp",
        sampleid = "151673",
        colors = libd_layer_colors,
        na_color = "white",
        ... = " LIBD Layers"
    )
    print(p4)

    ## edit plot point size but keep guide size larger
    p5 <- vis_clus(
        spe = spe,
        clustervar = "layer_guess_reordered",
        sampleid = "151673",
        colors = libd_layer_colors,
        na_color = "white",
        point_size = 1,
        guide_point_size = 3,
        ... = " LIBD Layers"
    )
    print(p5)

    ## Obtain the necessary data: Xenium example
    if (!exists("spe_xenium")) spe_xenium <- fetch_data("spe_xenium_example")

    spe_xenium$x_half <- ifelse(spatialCoords(spe_xenium)[,"x_centroid"] < 3088, "left", "right")

    p6 <- vis_clus(
        spe = spe_xenium,
        clustervar = "x_half",
        sampleid = "Br1556",
        colors = c(left = "red", right = "blue"),
        na_color = "white",
        point_size = 1,
        alpha = 0.5,
        guide_point_size = 3,
        datatype = "Xenium"
    )
    print(p6)

}
#> 2026-09-28 14:55:15.361957 loading file /github/home/.cache/R/BiocFileCache/e2d3506994e_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1





#> 2026-09-28 14:55:26.967028 loading file /github/home/.cache/R/BiocFileCache/e2d6c76d1f9_spe_Xenium_test.rds%3Frlkey%3D0ql1pu5d9qe448sjmkh3ja3o8%26st%3Dnpwqxtdk%26dl%3D1

Arguments

Value

Details

See also

Examples