Sample spatial cluster visualization grid

This function visualizes the clusters for a set of samples at the spot-level using (by default) the histology information on the background. To visualize gene-level (or any continuous variable) use vis_grid_gene().

vis_grid_clus(
  spe,
  clustervar,
  pdf_file,
  sort_clust = TRUE,
  colors = NULL,
  return_plots = FALSE,
  spatial = TRUE,
  height = 24,
  width = 36,
  image_id = "lowres",
  alpha = NA,
  sample_order = unique(spe$sample_id),
  point_size = 2,
  auto_crop = TRUE,
  na_color = "#CCCCCC40",
  is_stitched = FALSE,
  ...
)

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.
clustervar: A character(1) with the name of the colData(spe) column that has the cluster values.
pdf_file: A character(1) specifying the path for the resulting PDF.
sort_clust: A logical(1) indicating whether you want to sort the clusters by frequency using sort_clusters().
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.
return_plots: A logical(1) indicating whether to print the plots to a PDF or to return the list of plots that you can then print using plot_grid.
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.
height: A numeric(1) passed to pdf.
width: A numeric(1) passed to pdf.
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.
sample_order: A character() with the names of the samples to use and their order.
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.
...: Passed to paste0() for making the title of the plot following the sampleid.

Value

A list of ggplot2 objects.

Details

This function prepares the data and then loops through vis_clus() for computing the list of ggplot2 objects.

Examples


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

    ## Subset to two samples of interest and obtain the plot list
    p_list <-
        vis_grid_clus(
            spe[, spe$sample_id %in% c("151673", "151674")],
            "layer_guess_reordered",
            spatial = FALSE,
            return_plots = TRUE,
            sort_clust = FALSE,
            colors = libd_layer_colors
        )

    ## Visualize the spatial adjacent replicates for position = 0 micro meters
    ## for subject 3
    cowplot::plot_grid(plotlist = p_list, ncol = 2)
}
#> 2024-12-16 21:53:24.873465 loading file /github/home/.cache/R/BiocFileCache/10535bcd9c3d_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1

Arguments

Value

Details

See also

Examples