plotDimRed {ggspavis} | R Documentation

## plotDimRed

### Description

Plotting functions for spatially resolved transcriptomics data.

### Usage

    plotDimRed(
      spe,
      type = c("UMAP", "PCA"),
      x_axis = NULL,
      y_axis = NULL,
      annotate = NULL,
      palette = "libd_layer_colors",
      size = 0.3
    )

### Arguments

`spe` | (SpatialExperiment) Input data, assumed to be a `SpatialExperiment` object.
`type` | (character) Type of reduced dimension plot. Options are "UMAP" or "PCA". Default = "UMAP".
`x_axis` | (character) Name of column in `reducedDim` containing x-coordinates. Default = "UMAP1" or "PC1", depending on plot type.
`y_axis` | (character) Name of column in `reducedDim` containing y-coordinates. Default = "UMAP2" or "PC2", depending on plot type.
`annotate` | (character) Name of column in `colData` containing values to annotate spots with colors, e.g. cluster labels (discrete values) or total UMI counts (continuous values). For discrete values such as cluster labels, the column in `colData` should be formatted as a factor.
`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. `c("gray90", "navy")`. Default = `"libd_layer_colors"`.
`size` | (numeric) Point size for `geom_point()`. Default = 0.3.

### Details

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.

### Value

Returns a ggplot object. Additional plot elements can be added as ggplot elements (e.g. title, labels, formatting, etc).

### Examples

    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")

[Package ggspavis version 1.0.0 Index]