Spatial plots of discrete or continuous features for stitched-together capture areas.

This function is essentially a wrapper around spatialLIBD::vis_clus and spatialLIBD::vis_gene, suitable for merged samples (each sample in the SpatialExperiment 'spe' is a donor consisting of multiple capture areas, with colData column 'exclude_overlapping' indicating overlapping spots to drop (to prevent overplotting).

spot_plot(
  spe,
  sample_id,
  image_id = "lowres",
  title = sprintf("%s_%s", sample_id, var_name),
  var_name,
  multi_gene_method = c("z_score", "pca", "sparsity"),
  include_legend = TRUE,
  is_discrete,
  colors = NULL,
  assayname = "logcounts",
  minCount = 0.5,
  spatial = FALSE
)

Arguments

spe: A SpatialExperiment with colData column exclude_overlapping, passed to spatialLIBD::vis_gene or spatialLIBD::vis_clus
sample_id: character(1) passed to sampleid in spatialLIBD::vis_gene or spatialLIBD::vis_clus. Assumed to be a donor, possibly consisting of several capture areas to plot at once
image_id: character(1) giving the name of the image (e.g. "lowres") to plot, used both to determine an appropriate spot size and passed to spatialLIBD::vis_gene or spatialLIBD::vis_clus
title: character(1) giving the title of the plot
var_name: character() passed to geneid for spatialLIBD::vis_gene or character(1) passed to clustervar for spatialLIBD::vis_clus
multi_gene_method: A character(1): either "pca", "sparsity", or "z_score". This parameter controls how multiple continuous variables are combined for visualization, and only applies when var_name has length > 1 and is_discrete is FALSE
include_legend: logical(1): if FALSE, remove the plot legend
is_discrete: logical(1): if TRUE, use spatialLIBD::vis_clus; otherwise, use spatialLIBD::vis_gene
colors: character() of colors passed to colors for spatialLIBD::vis_clus if is_discrete or otherwise to cont_colors for spatialLIBD::vis_gene
assayname: character(1) passed to spatialLIBD::vis_gene if not is_discrete
minCount: numeric(1) passed to passed to spatialLIBD::vis_gene if not is_discrete
spatial: logical(1) passed to sampleid in spatialLIBD::vis_gene or spatialLIBD::vis_clus

Value

A ggplot object containing a "spot plot" of the specified sample

Details

Spot sizes are almost consistent among donors, regardless of full- resolution image dimensions, when title is NULL, include_legend is FALSE, and the plot is saved to a square output (e.g. PDF with 7in width and height). However, ggplot does not seem to scale plots of different aspect ratios exactly consistently when writing to PDF (untested for other formats)

Author

Nicholas J. Eagles

Examples


#   Grab an example SpatialExperiment and suppose all of its spots should be
#   plotted (for spatialNAc, 'exclude_overlapping' will only have genuinely
#   overlapping spots be TRUE)
spe <- if (!exists("spe")) {
    spatialLIBD::fetch_data(type = "spatialDLPFC_Visium_example_subset")
}
#> 2024-06-06 17:41:16.613515 loading file /github/home/.cache/R/BiocFileCache/6fb23c6a6be_spatialDLPFC_spe_subset_example.rds%3Fdl%3D1
spe$exclude_overlapping <- FALSE

#   Plot age spatially for the first sample
sample_id <- unique(spe$sample_id)[1]
p <- spot_plot(
    spe,
    sample_id = sample_id,
    title = sample_id, var_name = "age",
    include_legend = TRUE, is_discrete = FALSE, minCount = 0,
    assayname = "logcounts"
)
print(p)


#    Define several markers for white matter
white_matter_genes <- c(
    "ENSG00000197971", "ENSG00000131095", "ENSG00000123560",
    "ENSG00000171885"
)

#   Plot multiple white matter genes simultaneously for the first sample.
#   Use the "pca" method for combining them
p <- spot_plot(
    spe,
    sample_id = sample_id,
    title = sample_id, var_name = white_matter_genes,
    multi_gene_method = "pca", include_legend = TRUE, is_discrete = FALSE,
    minCount = 0, assayname = "logcounts"
)
print(p)