spatialLIBD 1.19.6
One of the goals of spatialLIBD
is to provide options for visualizing Visium data by 10x Genomics. In
particular, vis_gene()
and vis_clus()
allow plotting of individual continuous or
discrete quantities belonging to each Visium spot, in a spatially accurate manner and
optionally atop histology images.
This vignette explores a more complex capability of vis_gene()
: to visualize a summary
metric of several continuous variables simultaneously. We’ll start with a basic one-gene
use case for vis_gene()
before moving to more advanced cases.
First, let’s load some example data for us to work on. This data is a subset from a recent publication with Visium data from the dorsolateral prefrontal cortex (DLPFC) (Huuki-Myers, Spangler, Eagles, Montgomergy, Kwon, Guo, Grant-Peters, Divecha, Tippani, Sriworarat, Nguyen, Ravichandran, Tran, Seyedian, Consortium, Hyde, Kleinman, Battle, Page, Ryten, Hicks, Martinowich, Collado-Torres, and Maynard, 2024).
library("spatialLIBD")
spe <- fetch_data(type = "spatialDLPFC_Visium_example_subset")
spe
#> class: SpatialExperiment
#> dim: 28916 12107
#> metadata(1): BayesSpace.data
#> assays(2): counts logcounts
#> rownames(28916): ENSG00000243485 ENSG00000238009 ... ENSG00000278817 ENSG00000277196
#> rowData names(7): source type ... gene_type gene_search
#> colnames(12107): AAACAAGTATCTCCCA-1 AAACACCAATAACTGC-1 ... TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
#> colData names(155): age array_col ... VistoSeg_proportion wrinkle_type
#> reducedDimNames(8): 10x_pca 10x_tsne ... HARMONY UMAP.HARMONY
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
Next, let’s define several genes known to be markers for white matter (Tran, Maynard, Spangler et al., 2021).
white_matter_genes <- c("GFAP", "AQP4", "MBP", "PLP1")
white_matter_genes <- rowData(spe)$gene_search[
rowData(spe)$gene_name %in% white_matter_genes
]
## Our list of white matter genes
white_matter_genes
#> [1] "GFAP; ENSG00000131095" "AQP4; ENSG00000171885" "MBP; ENSG00000197971" "PLP1; ENSG00000123560"
A typical use of vis_gene()
involves
plotting the spatial distribution of a single gene or continuous variable of interest.
For example, let’s plot just the expression of GFAP.
vis_gene(
spe,
geneid = white_matter_genes[1],
point_size = 1.5
)