Maps, pixel classes and scatter plot of genes, optionally saved as PDF

This function creates a multi-panel plot for each gene showing spatial expression patterns, pixel classifications, and the expression scatter plot. Each gene gives a four-panel figure, which can also be saved as a PDF file. It needs the patchwork package.

Usage

savePlots(
  geneNames,
  spatialSimilarity,
  rastGexp,
  assayName = NULL,
  filePath = FALSE
)

Arguments

geneNames

Character vector. Names of genes to visualize and save.

spatialSimilarity

A list. Results from spatialSimilarity() containing similarity tables and analysis parameters.

rastGexp

A list of two SpatialExperiment objects: the rasterized gene expression data that spatialSimilarity() compared, in the same order. The names of the list are the titles of the expression panels.

assayName

A character string or numeric specifying the assay in the Spatial Experiment to use. Default is NULL: the assay that spatialSimilarity() used. It is used for all four panels.

filePath

Character. Directory where a PDF file is saved for each gene. Default is FALSE: no files are written.

Value

A list containing the arranged plot objects for each gene, with gene names as list element names. Each plot object is a four-panel arrangement showing:

Panel 1:

Spatial expression plot for the first experiment.

Panel 2:

Spatial expression plot for the second experiment.

Panel 3:

Pixel classification plot showing similarity categories (pixelClass()).

Panel 4:

Expression scatter plot comparing the experiments (linearRegression()).

Details

If filePath is a directory, each gene's figure is saved there as a PDF file named after the gene (<gene>.pdf), 17 inches wide by 5 inches tall.

See also

plotGene(), the standardized figure of a gene in a compareSpatial() result (the same panels, plus the native resolution and the statistics).

Examples

data(speKidney)
##### Rasterize to get pixels at matched spatial locations #####
rastKidney <- SEraster::rasterizeGeneExpression(speKidney,
                assay_name = 'counts', resolution = 0.2, fun = "mean",
                square = FALSE)
rastAB <- list(A = rastKidney$A, B = rastKidney$B)
s <- spatialSimilarity(rastAB)
plts <- savePlots("Gene", s, rastAB)
plts
#> $Gene
#> Warning: Removed 15 rows containing missing values or values outside the scale range
#> (`geom_point()`).

#>