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,
  ...
)

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().

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
#> 2023-03-17 21:38:17 loading file /github/home/.cache/R/BiocFileCache/73556cab927_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1

Arguments

Value

Details

See also

Examples