vis_gene_p: Sample spatial gene visualization workhorse function

In LieberInstitute/spatialLIBD: spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

Sample spatial gene visualization workhorse function

Description

This function visualizes the gene expression stored in assays(spe) or any continuous variable stored in colData(spe) for one given sample at the spot-level using (by default) the histology information on the background. This is the function that does all the plotting behind vis_gene() To visualize clusters (or any discrete variable) use vis_clus_p().

Usage

vis_gene_p(
  spe,
  d,
  sampleid = unique(spe$sample_id)[1],
  spatial,
  title,
  viridis = TRUE,
  image_id = "lowres",
  alpha = NA,
  cont_colors = if (viridis) viridisLite::viridis(21) else c("aquamarine4",
    "springgreen", "goldenrod", "red"),
  point_size = 2,
  auto_crop = TRUE,
  na_color = "#CCCCCC40",
  legend_title = ""
)

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.
d	A data.frame() with the sample-level information. This is typically obtained using cbind(colData(spe), spatialCoords(spe)). The data.frame has to contain a column with the continuous variable data to plot stored under d$COUNT.
sampleid	A character(1) specifying which sample to plot from colData(spe)$sample_id (formerly colData(spe)$sample_name).
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.
title	The title for the plot.
viridis	A logical(1) whether to use the color-blind friendly palette from viridis or the color palette used in the paper that was chosen for contrast when visualizing the data on top of the histology image. One issue is being able to differentiate low values from NA ones due to the purple-ish histology information that is dependent on cell density.
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.
cont_colors	A character() vector of colors that supersedes the viridis argument.
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.
legend_title	A character(1) specifying the legend title.

Value

A ggplot2 object.

See Also

Other Spatial gene visualization functions: vis_gene(), vis_grid_gene()

Examples

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

    ## Prepare the data for the plotting function
    spe_sub <- spe[, spe$sample_id == "151673"]
    df <- as.data.frame(cbind(colData(spe_sub), SpatialExperiment::spatialCoords(spe_sub)), optional = TRUE)
    df$COUNT <- df$expr_chrM_ratio

    ## Don't plot the histology information
    p <- vis_gene_p(
        spe = spe_sub,
        d = df,
        sampleid = "151673",
        title = "151673 chrM expr ratio",
        spatial = FALSE
    )
    print(p)

    ## Clean up
    rm(spe_sub)
}

LieberInstitute/spatialLIBD documentation built on Dec. 19, 2024, 7:12 p.m.