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,
...
)
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.
A character(1)
with the name of the colData(spe)
column that has the cluster values.
A character(1)
specifying the path for the resulting PDF.
A logical(1)
indicating whether you want to sort
the clusters by frequency using sort_clusters()
.
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
.
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.
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
.
A numeric(1)
passed to pdf.
A numeric(1)
passed to pdf.
A character(1)
with the name of the image ID you want to
use in the background.
A numeric(1)
in the [0, 1]
range that specifies the
transparency level of the data on the spots.
A character()
with the names of the samples to use
and their order.
A numeric(1)
specifying the size of the points. Defaults
to 1.25
. Some colors look better if you use 2
for instance.
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.
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.
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
.
A list of ggplot2 objects.
This function prepares the data and then loops through
vis_clus()
for computing the list of ggplot2
objects.
Other Spatial cluster visualization functions:
frame_limits()
,
vis_clus()
,
vis_clus_p()
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)
}
#> 2024-07-26 23:50:54.796467 loading file /github/home/.cache/R/BiocFileCache/e60656c8edd_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1