Visualize gene expression on a dimensional reduction plot

This function plots gene expression on a dimensional reduction plot using methods like t-SNE, UMAP, or PCA. Each single cell is color-coded based on the expression of a specific gene or feature.

plotGeneExpressionDimred(
  se_object,
  method = c("TSNE", "UMAP", "PCA"),
  pc_subset = 1:5,
  feature,
  cell_type_col = NULL,
  cell_types = NULL,
  assay_name = "logcounts",
  max_cells = 2500
)

Arguments

se_object

An object of class SingleCellExperiment containing log-transformed expression matrix and other metadata. It can be either a reference or query dataset.

method

The reduction method to use for visualization. It should be one of the supported methods: "TSNE", "UMAP", or "PCA".

pc_subset

An optional vector specifying the principal components (PCs) to include in the plot if method = "PCA". Default is 1:5.

feature

A character string representing the name of the gene or feature to be visualized.

cell_type_col

The column name in the colData of se_object that identifies the cell types.

cell_types

A character vector specifying the cell types to include in the plot. If NULL, all cell types are included.

assay_name

Name of the assay on which to perform computations. Default is "logcounts".

max_cells

Maximum number of cells to retain. If the object has fewer cells, it is returned unchanged. Default is 2500.

Value

A ggplot object representing the dimensional reduction plot with gene expression.

Examples

# Load data
data("query_data")

# Plot gene expression on PCA plot
plotGeneExpressionDimred(se_object = query_data,
                         cell_type_col = "SingleR_annotation",
                         method = "PCA",
                         pc_subset = 1:5,
                         feature = "CD8A",
                         cell_types = "CD4")