vis_clus: Sample spatial cluster visualization

In LieberInstitute/spatialLIBD: spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

Sample spatial cluster visualization

Description

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().

Usage

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,
  ...
)

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

Details

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

Value

A ggplot2 object.

See Also

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)

    ## 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)
         
}

LieberInstitute/spatialLIBD documentation built on April 14, 2025, 5:19 a.m.