plotDimRed {ggspavis} | R Documentation |
Plotting functions for spatially resolved transcriptomics data.
plotDimRed( spe, type = c("UMAP", "PCA"), x_axis = NULL, y_axis = NULL, annotate = NULL, palette = "libd_layer_colors", size = 0.3 )
spe |
(SpatialExperiment) Input data, assumed to be a
|
type |
(character) Type of reduced dimension plot. Options are "UMAP" or "PCA". Default = "UMAP". |
x_axis |
(character) Name of column in |
y_axis |
(character) Name of column in |
annotate |
(character) Name of column in |
palette |
(character) Color palette for annotation. Options for discrete
labels are "libd_layer_colors", "Okabe-Ito", or a vector of color names or
hex values. For continuous values, provide a vector of length 2 for the low
and high range, e.g. |
size |
(numeric) Point size for |
Function to plot spot-based spatially resolved transcriptomics data stored in
a SpatialExperiment
object.
This function generates a plot in reduced dimension coordinates (PCA or UMAP), along with annotation such as cluster labels or total UMI counts.
Returns a ggplot object. Additional plot elements can be added as ggplot elements (e.g. title, labels, formatting, etc).
library(STexampleData) spe <- Visium_humanDLPFC() # use small subset of data for this example # for longer examples see our online book OSTA spe <- spe[, spatialData(spe)$in_tissue == 1] set.seed(100) n <- 200 spe <- spe[, sample(seq_len(ncol(spe)), n)] # calculate log-transformed normalized counts library(scran) set.seed(100) qclus <- quickCluster(spe) spe <- computeSumFactors(spe, cluster = qclus) spe <- logNormCounts(spe) # identify top highly variable genes (HVGs) is_mito <- grepl("(^MT-)|(^mt-)", rowData(spe)$gene_name) spe <- spe[!is_mito, ] dec <- modelGeneVar(spe) top_hvgs <- getTopHVGs(dec, prop = 0.1) # run dimensionality reduction library(scater) set.seed(100) spe <- runPCA(spe, subset_row = top_hvgs) set.seed(100) spe <- runUMAP(spe, dimred = "PCA") colnames(reducedDim(spe, "UMAP")) <- paste0("UMAP", 1:2) # generate plot plotDimRed(spe, type = "UMAP", annotate = "ground_truth")