pseudobulk_dim_plot: Plot PCA of pseudobulk samples — pseudobulk_dim

Generates a PCA plot after computation of "pseudobulk" counts

pseudobulk_dim_plot(
  x = NULL,
  color_pal = "muted",
  plot_meta = NULL,
  dim = "pca",
  pcx = 1,
  pcy = 2,
  ptsize = 5
)

Arguments

x: an STlist with pseudobulk PCA results in the @misc slot (generated by pseudobulk_samples)
color_pal: a string of a color palette from khroma or RColorBrewer, or a vector of color names or HEX values. Each color represents a category in the variable specified in plot_meta
plot_meta: a string indicating the name of the variable in the sample metadata to color points in the PCA plot
dim: one of umap or pca. The dimension reduction to plot
pcx: integer indicating the principal component to plot in the x axis
pcy: integer indicating the principal component to plot in the y axis
ptsize: the size of the points in the PCA plot. Passed to the size aesthetic from ggplot2

Value

a ggplot object

Details

Generates a Principal Components Analysis plot to help in initial data exploration of differences among samples. The points in the plot represent "pseudobulk" samples. This function follows after usage of pseudobulk_samples.

Examples


# Using included melanoma example (Thrane et al.)
# Download example data set from spatialGE_Data
thrane_tmp = tempdir()
unlink(thrane_tmp, recursive=TRUE)
dir.create(thrane_tmp)
lk='https://github.com/FridleyLab/spatialGE_Data/raw/refs/heads/main/melanoma_thrane.zip?download='
download.file(lk, destfile=paste0(thrane_tmp, '/', 'melanoma_thrane.zip'), mode='wb')
zip_tmp = list.files(thrane_tmp, pattern='melanoma_thrane.zip$', full.names=TRUE)
unzip(zipfile=zip_tmp, exdir=thrane_tmp)
# Generate the file paths to be passed to the STlist function
count_files <- list.files(paste0(thrane_tmp, '/melanoma_thrane'),
                          full.names=TRUE, pattern='counts')
coord_files <- list.files(paste0(thrane_tmp, '/melanoma_thrane'),
                          full.names=TRUE, pattern='mapping')
clin_file <- list.files(paste0(thrane_tmp, '/melanoma_thrane'),
                        full.names=TRUE, pattern='clinical')
# Create STlist
library('spatialGE')
melanoma <- STlist(rnacounts=count_files,
                   spotcoords=coord_files,
                   samples=clin_file, cores=2)
#> Found matrix data
#> Matching gene expression and coordinate data...
#> Converting counts to sparse matrices
#> Completed STlist!
melanoma <- pseudobulk_samples(melanoma)
pseudobulk_dim_plot(melanoma, plot_meta='patient')